“Revolutionary AI Model on Mac Studio Outperforms Competition, Challenging OpenAI’s Cloud Dominance with Impressive Speed and Efficiency”

DeepSeek-V3 Hits 20 Tokens/Sec on Mac Studio: A Game-Changer for OpenAI?

Introduction
A new open-source AI milestone has been reached: DeepSeek-V3, a massive 671-billion-parameter language model, can now run at over 20 tokens per second on a Mac Studio (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This breakthrough blurs the line between data-center AI and consumer hardware, signaling a shift in how and where advanced AI models can be deployed. The feat, achieved on Apple’s latest Mac Studio with an M3 Ultra chip, highlights the rapid progress of open-source AI models – and has even been called a “nightmare for OpenAI” by some commentators. In this article, we’ll dive deep into the history of DeepSeek, the technical magic behind DeepSeek-V3, its Mac Studio performance, and what it means for OpenAI and other AI providers. We’ll also compare DeepSeek-V3 to GPT-4, Mistral, LLaMA and more in terms of speed, accuracy, and hardware needs. Finally, we’ll provide a practical guide to running this model locally (yes, you can run a local LLM faster than GPT in some cases) and examine community reactions, broader impacts on AI accessibility, and what the future may hold for open-source LLMs.

DeepSeek: History and Background

DeepSeek is a Chinese AI startup that has quickly gained a reputation for pushing the limits of open-source large language models (LLMs). The company’s strategy is unusual by Western standards – it often releases major new models with minimal fanfare or marketing (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). DeepSeek-V1 and V2 established the foundation, with DeepSeek-V2 already introducing a Mixture-of-Experts (MoE) architecture to scale up parameters efficiently. In late 2024, DeepSeek-V2’s technical report detailed innovations like load balancing across experts and speculative decoding techniques (GitHub – deepseek-ai/DeepSeek-V3) (GitHub – deepseek-ai/DeepSeek-V3). These laid the groundwork for the next leap.

In early 2025, DeepSeek-V3 arrived, quietly uploaded to Hugging Face with an empty README and no formal announcement (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). The version is sometimes referred to as DeepSeek-V3-0324, denoting its March 24, 2025 release date (deepseek-ai/DeepSeek-V3-0324). Despite the stealth launch, this model immediately drew attention for two reasons: its unprecedented scale – 671 billion parameters (with 37 billion “active” per token) – and its permissive MIT license allowing free commercial use (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This marked a shift from previous releases (earlier DeepSeek versions had more restrictive licenses). By open-sourcing DeepSeek-V3 under MIT, the company signaled a strong commitment to the open AI ecosystem, inviting researchers and developers worldwide to experiment freely.

Development of DeepSeek-V3

DeepSeek-V3 represents the culmination of the lab’s research into training huge models efficiently. According to the technical report (DeepSeek-V3 Technical Report), V3 was pre-trained on a staggering 14.8 trillion tokens, yet the team managed this with only about 2.8 million GPU hours on H800 GPUs – an impressive training cost efficiency for such a massive model (DeepSeek-V3 Technical Report). Key to this efficiency was the continued use of the DeepSeekMoE architecture (a sophisticated MoE design) and new techniques like FP8 mixed-precision training (GitHub – deepseek-ai/DeepSeek-V3) to reduce memory and compute overhead. The developers also employed improved parallelism and communication optimizations to keep the multi-GPU training stable and scalable (GitHub – deepseek-ai/DeepSeek-V3).

DeepSeek-V3 was released as a “base” model with 671B parameters (37B active) and also a fine-tuned chat model variant. Notably, it offers an extraordinary 128K context length (GitHub – deepseek-ai/DeepSeek-V3) – far beyond most LLMs – thanks to its Multi-Head Latent Attention (MLA) mechanism which helps maintain long-term context (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). The model went through the typical fine-tuning and reinforcement learning steps after pre-training to align it for useful output (GitHub – deepseek-ai/DeepSeek-V3). Yet, unlike many Western releases, there was no months-long hype cycle or glossy press event. DeepSeek simply dropped the weights online, trusting the community to pick them up. This lean launch approach has become part of DeepSeek’s identity and a point of contrast with competitors.

DeepSeek-V3’s Architecture and Capabilities

At the heart of DeepSeek-V3 is a Mixture-of-Experts architecture that allows it to achieve extreme scale without needing to activate all weights for every input. Instead of using all 671 billion parameters at once (which would be prohibitively slow and memory-intensive), the model dynamically routes each query to a subset of expert networks, utilizing about 37 billion parameters per token generated (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This selective activation is a paradigm shift in efficiency – it means DeepSeek-V3 can match the performance of a dense model hundreds of billions of parameters larger, while keeping computation per token relatively moderate (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). In essence, the model has many experts specializing in different aspects of language tasks, and a gating mechanism decides which experts are most relevant for a given prompt (What Is Mixture of Experts (MoE)? How It Works, Use Cases & More). By “divide and conquer,” DeepSeek achieves both scale and speed.

Beyond MoE, DeepSeek-V3 introduced two other major innovations: Multi-Head Latent Attention (MLA) and Multi-Token Prediction (MTP). MLA allows the model to maintain coherence over very long texts by efficiently attending over latent representations, which is one reason it supports up to 128,000 tokens of context (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). MTP, on the other hand, enables the model to predict multiple tokens in a single inference step instead of the usual one-at-a-time autoregressive generation (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). By effectively performing a form of speculative decoding, MTP boosts output throughput by nearly 80% in DeepSeek-V3 (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). These features make DeepSeek-V3 remarkably fast in generating text, despite its massive size. In fact, the model’s design prioritizes throughput and efficiency, an emphasis that clearly paid off in the recent Mac Studio performance tests.

Capability-wise, early evaluations of DeepSeek-V3 showed it outperforms other open-source models on a wide range of benchmarks and even rivals leading closed-source models. DeepSeek’s team reported superior results on standard academic tests like MMLU (multi-subject knowledge exam), coding challenges, and math problems, beating open competitors such as Qwen-2.5 (72B, Alibaba’s model) and even larger dense models like the hypothetical LLaMA 3.1 (405B) in many cases (GitHub – deepseek-ai/DeepSeek-V3) (GitHub – deepseek-ai/DeepSeek-V3). One tester, AI researcher Xeophon, noted a “huge jump in all metrics” compared to the previous version, calling DeepSeek-V3 “the best non-reasoning model, dethroning Sonnet 3.5” (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). (Sonnet 3.5 refers to Claude-Sonnet v3.5, an Anthropic model.) This suggests that for general tasks like coding, knowledge Q&A, and creative writing (as opposed to complex reasoning puzzles), DeepSeek-V3 may now hold the crown among open models. It even competes with giants like GPT-4 in several areas of performance (DeepSeek-V3 Technical Report). And unlike GPT-4 or Claude, which require paid APIs or subscriptions, DeepSeek-V3’s weights are freely downloadable to all (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat).

It’s important to note that DeepSeek also has a parallel “R” series of models specialized for reasoning (DeepSeek-R1 was released after V3, and an R2 is rumored soon). DeepSeek-V3 is considered the strong general foundation on which those reasoning models build (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). Even so, V3 itself is already demonstrating impressive reasoning and coding abilities out-of-the-box. For example, the model’s context length allows it to handle long documents or multi-turn dialogues without losing track, and testers have had success with tasks like code generation for interactive web pages and even generating SVG images via code. One early user prompted DeepSeek-V3 to “Generate an SVG of a pelican riding a bicycle” and received an attempt at SVG code that, when rendered, did produce a pelican and a bicycle (albeit somewhat disassembled) (deepseek-ai/DeepSeek-V3-0324) (deepseek-ai/DeepSeek-V3-0324) – a quirky but illustrative example of the model’s creative capability to output working code and visuals.

DeepSeek-V3 Mac Studio Performance: 20 Tokens/Sec Breakthrough

Perhaps the most astonishing demonstration of DeepSeek-V3’s efficiency came when an AI developer managed to run it on a single high-end Apple Mac Studio at over 20 tokens per second. The Mac Studio in question was a top-of-the-line configuration with Apple’s M3 Ultra chip and 512GB of unified memory (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This machine, costing around $9,499, stretches the definition of “consumer hardware,” but it is essentially a desktop computer – not a server rack or supercomputer. Seeing a 671B-parameter model generate text smoothly on a Mac is a milestone few would have imagined a year ago. It demonstrates that local LLM deployments are not limited to small models anymore; even a model rivaling GPT-4 in size and skill can be brought to life on a workstation under one’s desk.

(Mac Studio – Apple) Apple’s Mac Studio with the M3 Ultra chip can be configured with up to 80 GPU cores and 512GB of unified memory – enough to load and run DeepSeek-V3 locally at high speed (Mac Studio – Apple).

The achievement was first announced by Awni Hannun, an AI researcher and developer, who got DeepSeek-V3 running on the Mac Studio using a specialized Apple silicon optimization library. He reported “The new DeepSeek V3 0324 in 4-bit runs at > 20 toks/sec on a 512GB M3 Ultra with mlx-lm!” (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This was accompanied by a screenshot showing the model generating text quickly. How was this possible? The secret lies in a combination of Apple’s powerful hardware and some clever software optimization:

• 4-bit Quantization: The model weights were compressed to 4-bit precision, reducing the memory footprint dramatically. DeepSeek-V3’s full FP16 weight set is about 641 GB (deepseek-ai/DeepSeek-V3-0324). In 4-bit integer format, it shrinks to roughly 352 GB on disk (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This smaller size can fit into the Mac’s 512 GB RAM with room for overhead. AI developer Simon Willison noted that at 4-bit, running V3 on “high-end consumer hardware like the Mac Studio…is feasible” (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). Without such quantization, it would be impossible to load the model on a single machine.
• MLX Library for Apple Silicon: Hannun used an open-source package called mlx-lm (often referred to simply as “MLX”) to run the model (deepseek-ai/DeepSeek-V3-0324). MLX is a toolset for running and fine-tuning LLMs on Apple GPUs with minimal hassle (mlx-lm · PyPI) (mlx-lm · PyPI). It integrates with Hugging Face Hub, allowing users to download community-quantized models and run them via Apple’s Metal performance shaders. In this case, the DeepSeek-V3 4-bit quantized weights were made available as mlx-community/DeepSeek-V3-0324-4bit on Hugging Face (deepseek-ai/DeepSeek-V3-0324). With a few commands, Awni was able to load these onto the Mac Studio’s 80-core GPU and utilize the enormous unified memory effectively. The MLX runtime ensures the model uses the Apple Neural Engine and GPU efficiently for matrix operations, enabling high throughput.
• Unified Memory & Bandwidth: Apple’s M3 Ultra chip not only provides a large memory pool, but also extremely high memory bandwidth (up to ~819 GB/s) (Mac Studio – Apple). This is crucial because large models are often bottlenecked by memory transfer speeds. The unified architecture (CPU, GPU, and Neural Engine sharing the same memory) means data doesn’t need to shuffle between different VRAM pools, avoiding a common slowdown in PC GPUs when models exceed a single GPU’s memory. In effect, the Mac Studio can treat the 352GB model as one contiguous block and the M3 Ultra’s GPUs can rapidly fetch weights from RAM while generating tokens.
• Parallelism and Multi-Token Generation: Although it’s a single machine, the M3 Ultra is internally a dual-die system (two M3 Max chips fused, hence “Ultra”). This provides 32 CPU cores and 80 GPU cores that MLX can leverage in parallel. DeepSeek-V3’s MTP capability (multi-token prediction) means it doesn’t necessarily generate tokens strictly sequentially – it can predict a few at a time under the hood (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This further boosts the effective token output rate, making it possible to hit >20 tokens/s. By comparison, many cloud-hosted models (including GPT-4’s API) often generate at only a few tokens per second for users. In other words, under these conditions DeepSeek-V3 can run a local LLM faster than GPT-4 can stream its output over the internet, a remarkable reversal of the typical expectation.

For context, 20 tokens per second on DeepSeek-V3 is roughly in line with what multi-GPU server setups achieve as well. Developers deploying V3 on clusters of NVIDIA H100 GPUs have reported similar generation speeds (~20 tok/s) when using 16–32 GPUs in parallel ([Bug] DeepSeekV3 instructions don’t work for multi-node H100 setup · Issue #2673 · sgl-project/sglang · GitHub). The Mac Studio accomplishing this on its own highlights the efficiency of Apple’s silicon and the model’s optimizations. It’s also an eye-opener: normally, one might assume you’d need a data center with hundreds of gigabytes of VRAM and huge power draw to run a 37B-parameter-per-token model. Instead, a desktop machine under 200 watts was shown to handle it (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This huge gap in power usage (kilowatts for GPU farms vs <0.2 kW for the Mac) suggests that AI infrastructure paradigms could be shifting (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). If more models adopt efficient architectures like MoE and if hardware continues to improve memory and bandwidth, we might see more “AI at home” scenarios for cutting-edge models.

It’s worth noting that running DeepSeek-V3 on Mac Studio is still at the fringe of “consumer” – few individuals have a need (or budget) for a 512GB RAM monster machine. However, Apple’s push with M-series chips indicates that such capabilities will only grow. Today it’s a $9k Mac Studio; in a couple of years, perhaps a $3k laptop could run a model of this caliber, given the pace of advancement. In any case, the DeepSeek-V3 Mac Studio performance demo is a powerful proof-of-concept that open-source LLMs can challenge the cloud giants not just in quality, but even in where they can run.

Comparing DeepSeek-V3 to GPT-4, Mistral, LLaMA, and Other LLMs

How does DeepSeek-V3 stack up against other prominent large language models? Let’s compare it from several angles – size, speed, accuracy, and hardware requirements – with OpenAI’s GPT-4, the Mistral model, Meta’s LLaMA family, and more.

Model Size and Architecture

DeepSeek-V3 is unique in that it has an enormous total parameter count (685B including a small MTP module (GitHub – deepseek-ai/DeepSeek-V3)) but uses only 37B at a time (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This MoE design gives it an effective model capacity larger than any single dense model publicly known, yet with runtime costs closer to a 30-40B model. By contrast, GPT-4’s exact size is not public, but estimates range from 180B to as high as 1.5T parameters (with some experts suspecting a mixture-of-experts or ensemble under the hood). What we do know is GPT-4 is a dense model (or appears as such externally) that engages all its parameters for each output token. This likely means GPT-4 is doing more computation per token than DeepSeek-V3, which could be one reason GPT-4 is relatively slow and expensive to run. On the flip side, GPT-4 is currently the gold standard for quality on most reasoning and knowledge benchmarks – it has a depth of understanding that open models are still catching up to. DeepSeek-V3, while very advanced, is generally considered on par with GPT-3.5/Claude level in many tasks, and just below GPT-4 for complex reasoning. However, with an upcoming DeepSeek-R2 reasoning model, even that gap might narrow (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat).

Mistral 7B is an example of the opposite end of the spectrum. It’s a relatively small open-source LLM (7 billion parameters) released by Mistral AI, focused on efficiency and licensed for free use. Mistral made waves in late 2023 for its strong performance per parameter – a well-tuned 7B that in some tasks rivaled older 13B+ models. Obviously, DeepSeek-V3 dwarfs Mistral in scale: 37B vs 7B active parameters is more than a 5x difference, and DeepSeek was trained on far more data. DeepSeek-V3 simply has more knowledge and a larger “brain” to draw on, which shows in benchmark results (for instance, code generation or factual QA accuracy should be much higher with V3). But Mistral’s advantage is that it’s tiny and fast: it can run on a mobile phone or a Raspberry Pi (with quantization) and hardly requires any special hardware. On a high-end PC GPU, Mistral 7B can generate hundreds of tokens per second, and fine-tuning it on custom data is feasible with a single GPU. In short, Mistral vs DeepSeek-V3 is a trade-off of speed vs sophistication. If you need a lightweight local AI for simple tasks, Mistral (or similar small models) win on practicality. But if you need near-GPT4-level competence and have the hardware, DeepSeek-V3 is in a different league. It’s also worth noting Mistral is a dense model; to reach higher quality, one would need to scale its size or adopt techniques like MoE – a path that DeepSeek has already taken.

LLaMA (particularly LLaMA 2) was the previous king of open models in terms of widely available capability. LLaMA 2 comes in 7B, 13B, and 70B variants (Meta did not release larger versions publicly). The 70B LLaMA 2 model is often used as a baseline for “GPT-3 class” performance in open-source – it’s quite good on many tasks, roughly comparable to the original GPT-3 (175B) and outperforms most other open models of similar or smaller size as of 2023. DeepSeek-V3, however, appears to outperform LLaMA 70B by a significant margin in multiple benchmarks (GitHub – deepseek-ai/DeepSeek-V3), likely due to its greater activated size and advanced training methods. Also, LLaMA 2 is a dense model with a standard Transformer architecture; it does not have the multi-token or MoE optimizations. That means running LLaMA2-70B requires loading all 70B parameters and updating them each token. Many enthusiasts have run LLaMA 70B on consumer hardware (with 4-bit quantization you need ~40 GB of RAM, which fits on a 48GB GPU or across two 24GB GPUs). But the performance is modest – typically on a single GPU one might get 1-2 tokens per second from LLaMA 70B in 4-bit mode. DeepSeek-V3, even though effectively half that size per token (37B), is much faster because of MTP and efficient implementation. The best local LLM on Apple Silicon before DeepSeek’s arrival might have been LLaMA2 70B or a 33B model like Code LLaMA, which could perhaps reach 4-5 tokens/sec on an M2 Ultra Mac with 128GB (using 4-bit). DeepSeek-V3 blowing past 20 tokens/sec on M3 Ultra sets a new bar for Apple Silicon. It’s arguably now the best local LLM on Apple Silicon in terms of raw capability – although with the huge caveat of needing that massive memory upgrade to fully exploit it.

GPT-3.5 (OpenAI’s text-davinci-003 or GPT-3.5 Turbo) is a good point of comparison for speed. GPT-3.5 is around 175B parameters (dense) and known to be significantly faster than GPT-4 in practice. Many users observe GPT-3.5 generating about 30-50 tokens per second in chat applications, whereas GPT-4 might do 5-10 tokens/sec at best. Interestingly, DeepSeek-V3 in the Mac Studio scenario (~20 tokens/sec) comes close to GPT-4’s speed or even exceeds it, while delivering quality above GPT-3.5 in many tasks. In one analysis, DeepSeek V3 was noted to produce about 20 tok/s and was roughly twice as fast as GPT-4 in throughput (GPT-4 being closer to 10 tok/s) (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat) (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). However, GPT-3.5 still holds an edge in sheer speed due to its smaller size – in an optimized environment GPT-3.5 can output text extremely quickly (OpenAI’s servers likely deploy optimizations that yield the ~50 tok/s figure). For open models, though, DeepSeek-V3’s 20 tok/s is a major breakthrough; most other open LLMs of comparable strength (like a 30B dense model or previous DeepSeek-V2) would be much slower on equivalent hardware.

Accuracy and Capabilities

When it comes to accuracy and task performance, GPT-4 remains generally top-notch, especially for complex reasoning, nuanced understanding, and creative tasks. DeepSeek-V3 is reported to be close to GPT-4 on many benchmarks and even surpass it on some coding and math tests (DeepSeek Open-Sources DeepSeek-V3, a 671B Parameter Mixture of Experts LLM – InfoQ), but likely still falls short on the hardest reasoning challenges (which is why DeepSeek is separately developing reasoning-focused models like R1/R2). GPT-4 has been fine-tuned with extensive human feedback and has access to proprietary training data, which can give it an edge in areas like following subtle instructions or common-sense reasoning.

DeepSeek-V3, with its open training, might occasionally lag in those areas or produce less polished responses. That said, testers calling it the best “non-reasoning” model suggest that for everything aside from those chain-of-thought puzzles, V3 is currently the top open model (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). For example, on coding tasks, DeepSeek-V3 reportedly outscored Claude 3.5 (Anthropic) in five different coding benchmark suites (DeepSeek Open-Sources DeepSeek-V3, a 671B Parameter Mixture of Experts LLM – InfoQ), which indicates excellent programming capabilities. Its huge context window also means it can take in very large codebases or documents – something GPT-4 can only do with special 32k context versions, and others like Mistral or LLaMA cannot do at all (unless fine-tuned with longer context). So for tasks like analyzing or generating long documents, DeepSeek-V3 has a unique advantage.

The Mistral 7B and similar small models (like LLaMA2-7B, 13B) are much less accurate than DeepSeek-V3 on virtually any complex task. They shine in simple conversation or as lightweight assistants, but they do not have the knowledge breadth or depth. One can expect Mistral 7B to falter on tricky questions, logic puzzles, or multi-step problems that DeepSeek-V3 would handle easily. There is simply no substitute for parameters and training data when it comes to capturing the intricacies of human language, and DeepSeek-V3 has orders of magnitude more of both.

LLaMA 2 (70B) is closer in spirit – it’s a powerful open model that, until now, was a common choice for those wanting an offline GPT-3 alternative. DeepSeek-V3 likely outperforms LLaMA2 70B significantly on benchmarks like MMLU or BIG-bench, given the reports. For instance, if LLaMA2 70B gets around ~70% on MMLU, DeepSeek-V3 might be in the high 70s or low 80s (hypothetically) since it surpasses even some 100B+ dense models. In areas like coding, LLaMA2 70B was decent but not state-of-the-art – DeepSeek-V3, trained with newer techniques and possibly more code data, is better. It’s telling that DeepSeek’s team compared V3 against Claude-Sonnet 3.5 and GPT-4 (labeled GPT-4o) and found that V3 “outperformed the other models on a majority of tests” (DeepSeek Open-Sources DeepSeek-V3, a 671B Parameter Mixture of Experts LLM – InfoQ). This suggests that except for certain reasoning tasks, V3 can hold its ground or win against even these closed models.

Hardware Requirements and Accessibility

The biggest difference among these models is how and where you can run them. OpenAI’s GPT-4 (and 3.5) are closed-source, cloud-only. As an end user or developer, you cannot run GPT-4 on your own machine at all; you must use OpenAI’s API or services built on it. That means hardware requirements are abstracted away – OpenAI runs it on massive clusters (likely using tens of A100/H100 GPUs per instance) and charges you per token. The downside is you pay recurring costs and have usage limits, plus you’re subject to whatever policies OpenAI enforces. There’s no offline use, which can be a problem for privacy or for users in restricted environments.

DeepSeek-V3, being open, gives you the option to run it yourself if you have the hardware, or use community-run services for free/cheap. The catch: the hardware needed is extreme by personal computing standards. As we saw, a minimum to run the full 4-bit model is around 352GB of memory, plus a very fast CPU/GPU to handle it. That’s not something a typical PC or laptop has. To put it in perspective, even the best consumer PC GPU (NVIDIA RTX 4090) has 24GB VRAM – not even 1/10th of what’s needed. To run DeepSeek-V3 on standard hardware, you’d likely need to distribute it across many GPUs or use cloud VMs with very high RAM. For most people, the practical way to use V3 today is via the cloud – but through open platforms like Hugging Face, OpenRouter, or other API providers that host the model (often at lower cost or free). For example, OpenRouter offers free chat access to DeepSeek-V3-0324 (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat), and independent providers like Hyperbolic Labs made the model available via their endpoints immediately after release (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). So, while GPT-4 is locked behind OpenAI’s API, DeepSeek-V3 is popping up on multiple open-source LLM vs OpenAI alternative channels, increasing its accessibility without proprietary gatekeepers.

Mistral 7B and smaller models excel in accessibility. You can run Mistral 7B on a normal laptop CPU (albeit slowly) or any modern GPU with 8GB or more memory. Many developers load these smaller models on their local machines for quick tests or integrate them into apps for offline use. In terms of hardware, Mistral is democratized – even a smartphone can handle it (with Qualcomm’s AI acceleration or Apple’s ANE for 4-bit quant). LLaMA2 13B can run on phones (some demos exist), and 70B can run on a single high-end consumer GPU with quantization. DeepSeek-V3, in contrast, is not something you’ll run on a phone or even a typical desktop – it remains at the bleeding edge where only workstations or servers tread. So there’s a spectrum: Mistral – extremely accessible but limited; LLaMA2 – moderately accessible with decent power; DeepSeek-V3 – barely accessible but top power; GPT-4 – not accessible (except via paid API) but top power. The trend, however, is that what’s cutting-edge today (DeepSeek-V3) could become mainstream accessible in a few years as hardware and optimization catch up.

Speed and Throughput

We already discussed speed in terms of tokens per second. To recap succinctly: On their respective ideal hardware, GPT-4 is relatively slow (perhaps a few tokens per second to end-users, by design, to ensure quality). GPT-3.5 is faster (~30-50 tok/s observed). Mistral 7B can be extremely fast, potentially hundreds of tok/s on a good desktop, because it’s small. LLaMA2 70B might do ~2-5 tok/s on a single GPU with heavy quantization and optimizations. DeepSeek-V3 can reach 20+ tok/s on a Mac Studio (or multi-GPU rig) as demonstrated (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat) ([Bug] DeepSeekV3 instructions don’t work for multi-node H100 setup · Issue #2673 · sgl-project/sglang · GitHub). So in terms of raw generation speed: Mistral (small model) > GPT-3.5 > DeepSeek-V3 ≈ GPT-4 > LLaMA2-70B (this ordering can vary with hardware, but generally small models have the edge in speed, while largest models are slowest). What’s remarkable is that DeepSeek-V3 breaks the expectation that “very large model = very slow output.” Thanks to MTP and optimizations, it’s punching above its weight in speed.

For a user deciding between these, it often comes down to use-case: if you need a quick conversational agent with less concern for perfect accuracy, a fast 7B or 13B model might suffice and feel instant. If you need the absolute best answers and have patience or can afford API calls, GPT-4 is chosen. DeepSeek-V3 is aiming to offer a sweet spot – as open models improve, one could get high quality and reasonable speed without relying on closed APIs. The 20 tok/s on Mac is a proof that local models can be responsive for real applications (20 tok/s is more than comfortable for interactive chat or writing assistance). As the open model ecosystem grows, this speed advantage will likely increase with better software (compilers, quantization schemes, etc.).

Implications for OpenAI and Closed-Source Model Providers

The sudden emergence of DeepSeek-V3 – and its ability to run outside traditional cloud infrastructure – carries major implications for companies like OpenAI, Anthropic, and other closed-source AI providers. In many ways, it intensifies the competitive pressure that open-source AI is placing on proprietary models.

First and foremost, if developers and organizations can obtain GPT-4-like performance for free and run it on their own hardware (or access it through community-driven services), why should they pay for an API? OpenAI’s business model relies on offering superior quality models as a service. But the quality gap is shrinking. When open-source LLM vs OpenAI comparisons show parity on numerous tasks, the value proposition of closed models diminishes. OpenAI has already been lowering its prices and increasing token limits over the past year, likely in response to competition. (Indeed, OpenAI’s models became about 7x cheaper from 2022 to 2023 in a bid to stay attractive (OpenAI’s models became 7-20x cheaper over the last year. In the …).) The existence of DeepSeek-V3 could further force OpenAI to rethink pricing and policy. They might need to cut costs or offer new features (like guaranteed data privacy, fine-tuning support, or multimodal capabilities) to justify using their API over a free alternative.

Another implication is the pace of innovation. Open-source communities iterate quickly. DeepSeek-V3 was open-sourced, and within hours it was integrated into multiple platforms (SGLang, LMDeploy, vLLM, etc. all added support (GitHub – deepseek-ai/DeepSeek-V3) (GitHub – deepseek-ai/DeepSeek-V3)). Bugs are identified and fixed by a global pool of contributors (for example, issues running it on multi-node clusters were being debugged in public forums ([Bug] DeepSeekV3 instructions don’t work for multi-node H100 setup · Issue #2673 · sgl-project/sglang · GitHub)). This rapid, decentralized improvement cycle means open models can potentially improve faster than closed ones, which depend on internal teams and slower release cadences. OpenAI now faces not just competition at release, but an ongoing competition with an entire ecosystem’s worth of talent refining the open models. We’ve seen a similar dynamic in software with open-source projects vs proprietary software – often the open side, with enough momentum, becomes an unstoppable force.

The “nightmare” scenario for OpenAI that some have alluded to is that open models like DeepSeek-V3 could make closed models obsolete in many domains (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). If a free model is good enough for, say, 90% of use cases, then OpenAI’s only moat is the remaining 10% (the most complex tasks, enterprise support, or highly specialized needs). Already, we see companies opting to deploy models like LLaMA 2 or OpenAI’s smaller open alternatives on-premises for cost and privacy reasons. DeepSeek-V3 ups the ante by bringing top-tier performance to that equation. It’s telling that DeepSeek comes from China – OpenAI and Anthropic must now consider not just domestic (Western) open-source efforts but a thriving AI open-source movement in China (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). Chinese companies are releasing powerful models openly as a strategy to bootstrap an ecosystem, since they face constraints on accessing Western tech like Nvidia GPUs (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This has led to a philosophical split: Western AI leaders guard their crown jewels behind APIs, while Chinese labs are increasingly releasing theirs openly (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat) (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). If the open approach proves successful (attracting users, talent, and driving innovation faster), Western firms might be pressured to adapt or risk being leapfrogged.

OpenAI has so far maintained that closed models allow them to ensure safety and manage usage better. But open models are improving their safety too (via community alignment efforts), and many users prefer having control despite potential risks. We may see OpenAI respond by accelerating their next-gen models (like GPT-5) to maintain a clear quality gap. The VB report even speculated that DeepSeek-R2 (a reasoning model likely based on V3) could pose a direct challenge to a future GPT-5 if released soon (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This kind of head-to-head open vs closed race is something that wasn’t as prominent a year ago.

For other closed-source providers like Anthropic (Claude) or Cohere, the pressure is similarly mounting. They have been promoting “constitutional AI” and other alignment as differentiators, but if open models match them in ability, those differentiators might not justify proprietary status. Some companies might pivot to offering fine-tuned solutions or focusing on specific domains (like medical or legal models) to escape the commodity trap. OpenAI itself might double down on things like multimodal AI (e.g., GPT-4 can see and generate images) or highly optimized inference (making GPT models faster and cheaper using custom hardware). In essence, DeepSeek-V3 and its kin are commoditizing the base model. The value may shift to the layers on top – fine-tuning, support, integration, and novel features.

There are also pricing and licensing implications. With DeepSeek-V3 being MIT licensed, even commercial players can integrate it without legal worries (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This means startups can build products on top of V3 without paying royalties or risking license violations, undercutting those who rely on selling model access. OpenAI might find that some customers who only needed a large language model (but not necessarily GPT-4’s full prowess) will opt for an open model to reduce API costs. We might also see OpenAI lobby for stronger usage of their models by highlighting reliability or safety – e.g., “our model is tested and less likely to produce certain errors compared to unvetted open models.” But if the open ones prove reliable enough, that argument weakens.

One interesting effect is that OpenAI and others may increasingly incorporate open research. For instance, the multi-token prediction idea in DeepSeek-V3 is similar to speculative decoding research that has been floated in papers – OpenAI could adopt similar techniques in their inference stack to speed up GPT-4. Likewise, the FP8 training and MoE ideas might influence how closed models are made (Google’s sparse models like Switch Transformer were early MoEs; now DeepSeek shows it works well in practice). In a competitive environment, the flow of innovation is two-way: open source borrows from closed papers, and closed developers pay close attention to what open models achieve. OpenAI might have to contend not just with model quality, but also narratives: if open models are “good enough,” they may face community pressure to open up more of their own work or risk losing goodwill. Sam Altman has previously expressed caution about open-sourcing very powerful models due to misuse concerns, but if others do it anyway, OpenAI’s stance could isolate them or make them appear purely profit-driven.

In summary, DeepSeek-V3 is a wake-up call to closed-source model providers. It shows that open-source disruption in AI is not a distant possibility – it’s happening now, at the very high end of model sizes. The competitive landscape is shifting: it’s no longer just OpenAI vs Google vs Anthropic, but an entire open-source community (global, multilingual, and fast-moving) versus any single company. For users and enterprises, this is largely good news: more options, lower costs, and more transparency. For OpenAI, it means the pressure is on to innovate faster, differentiate their offerings (quality, safety, ease of use), and perhaps adjust their strategy to coexist with an open-source era of AI.

How to Install and Run DeepSeek-V3 on Mac Studio (Performance Tips)

For those inspired to try DeepSeek-V3 on a Mac Studio or similar machine, here is a practical guide. Be warned: running this model locally is hardware-intensive, but if you have access to the right setup (or are just curious how it’s done), these steps will help:

1. Hardware Requirements: At minimum, you’ll need an Apple Silicon machine with Apple M-series chip and a huge amount of memory. The demo used an M3 Ultra with 512GB unified memory (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). Lower memory (256GB or 192GB) will not be enough to load the full model in 4-bit precision. In theory, you could attempt a CPU-only run with disk swapping, but performance would be extremely slow and not practical. So ideally, use a Mac Studio (or Mac Pro) with the highest RAM option available (M2 Ultra 192GB or M3 Ultra 512GB). The M3 Ultra is preferred for its 80-core GPU and ~800GB/s memory bandwidth (Mac Studio – Apple).
2. Install the MLX LLM Package: DeepSeek-V3 is not officially supported on Mac (the GitHub explicitly says Linux only) (GitHub – deepseek-ai/DeepSeek-V3). Instead, use the MLX toolkit developed by the community for Apple devices. Install it via pip:

   pip install mlx-lm
   

   This package, maintained by Apple ML researchers (including Awni Hannun), will let you download and run models optimized for Apple’s Metal framework (mlx-lm · PyPI). It also provides a convenient CLI.

3. Download the Quantized Model: Rather than trying to convert the official 641GB model yourself, use the ready-made 4-bit quantized version prepared by the MLX community. It’s hosted on Hugging Face Hub under the name mlx-community/DeepSeek-V3-0324-4bit (deepseek-ai/DeepSeek-V3-0324). You can download it directly with MLX:

   mlx_lm download-model mlx-community/DeepSeek-V3-0324-4bit
   

   This will fetch all the shards of the quantized model (approximately 352GB total). Ensure you have enough disk space (SSD recommended for speed). The model will likely download as safetensors files.

4. Run the Model: Once downloaded, you can launch a chat or generation session. Using the MLX CLI:

   mlx_lm.chat -m mlx-community/DeepSeek-V3-0324-4bit
   

   This will open an interactive REPL where you can prompt the model. The first load will take some time as the model is loaded into memory. After that, you should get a prompt. Try something simple first, like asking a question, to verify it’s working. Alternatively, if you have Simon Willison’s llm tool, he notes you could use his llm-mlx plugin to run it similarly (deepseek-ai/DeepSeek-V3-0324).

5. Performance Tuning: By default, MLX will attempt to use the Apple GPU and possibly the Neural Engine for computation. To maximize throughput:
   • Ensure no other heavy processes are running (so the model gets full use of CPU/GPU).
   • If the MLX library has options for threading or batch size, adjust them. For example, you might be able to increase the --threads for CPU offloading or tweak how many tokens MTP generates per step if such an option exists. (Consult mlx_lm.chat -h for options (mlx-lm · PyPI).)
   • Temperature and decoding settings won’t affect speed much, but max_new_tokens length will – however, DeepSeek supports very long outputs, so you can set a high limit if needed.
   • Monitor memory pressure: use macOS’s Activity Monitor to ensure you’re not swapping to disk. If memory usage is near 100%, consider closing other apps or, if possible, not loading all experts (though currently MLX likely loads the entire model).
6. Using Multiple Machines: If you don’t have a single machine with enough RAM, an alternative is distributed inference. DeepSeek-V3 can split across nodes (the official instructions use PyTorch FSDP across 2 nodes with 8 GPUs each) (GitHub – deepseek-ai/DeepSeek-V3). While MLX is aimed at single Mac usage, you could theoretically use two Macs each with 256GB and connect them. However, MLX’s distributed features (mx.distributed) might need manual setup. For most, this isn’t feasible, but it’s good to know that multi-node is supported in principle by frameworks like vLLM and SGLang (GitHub – deepseek-ai/DeepSeek-V3) (GitHub – deepseek-ai/DeepSeek-V3).
7. Alternative: Use Cloud or Smaller Model: If you don’t have the required hardware, you can still experience DeepSeek-V3. The easiest way is through OpenRouter’s web chat (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat) or an API wrapper, which doesn’t require any install. Just select the model and chat in your browser. Another option is using a cloud VM with multiple GPUs – some cloud providers may even offer instances with 8×A100 GPUs that could run V3 (though at significant cost). And if it’s just the open-source experience you want, consider running a smaller variant: perhaps the DeepSeek-V2 (236B total, 21B active) model which might run on a 48GB GPU with 4-bit, or other 30B models like InternLM or Xverse which are more tractable locally.

Running DeepSeek-V3 on Mac Studio is at the cutting edge of DIY AI. It’s not everyday computing – it’s a peek into the future where such power might become commonplace. By following the above steps, you join a very select group pushing what’s possible in local AI. And given how fast things move, today’s “extreme” setup might be tomorrow’s normal. Keep an eye on MLX updates and community forums (like Reddit’s r/LocalLLaMA or the Hugging Face threads) for new tips, because performance with these tools is improving constantly.

Community and Expert Reactions

The release of DeepSeek-V3 and its local run feats sparked immediate reactions across social media, developer forums, and the AI research community. Here’s a roundup of what experts and enthusiasts are saying:

• “A Nightmare for OpenAI?” – The dramatic phrasing in some discussions originates from observers noting how disruptive V3 could be. On X (Twitter), users highlighted the fact that “China just dropped DeepSeek V3… 20 tokens/s on Mac Studio” as a potential nightmare scenario for OpenAI’s dominance (el.cine on X: “thisis nightmare for OpenAI China just dropped …). The idea is that OpenAI, which has led the field, must now contend with an open model out of left field that undercuts their advantages. While perhaps hyperbolic, it reflects a sentiment of surprise and concern in some quarters that open-source is catching up faster than expected.
• AI Researchers’ Praise: Several AI researchers who got early access were impressed. We mentioned Xeophon (an independent AI evaluator) who declared it the new best non-reasoning model, dethroning a top Anthropic model (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This kind of endorsement is significant; it tells the community that DeepSeek-V3 isn’t just hype – it’s delivering results. Another researcher, Awni Hannun (of MLX and formerly DeepSpeech project lead at Baidu), showcased the Mac Studio speed and implicitly praised Apple’s hardware synergy with such models (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). His successful demo was essentially a challenge to others: “If I can do this, so can you,” galvanizing more testing.
• Open-Source Community Excitement: On Reddit’s r/LocalLLaMA and Hugging Face forums, threads popped up discussing how to run DeepSeek-V3, comparing notes on performance. One Reddit user speculated even before running that a “decent CPU server” could maybe pull 14-20 tokens/s with DeepSeek-V3 (Wow deepseek v3 ? : r/LocalLLaMA – Reddit) – which turned out to be accurate on high-end setups. Others joked that it’s time to “mortgage your house for RAM” to join the fun. There was also healthy skepticism: some pointed out that 20 tokens/sec isn’t actually that fast compared to, say, smaller models or even Anthropic’s Claude which can stream very quickly (DeepSeek-V3 的输出有这么快吗？ – 前沿快讯 – LINUX DO). However, skeptics acknowledged that given V3’s size, 20 tok/s is remarkable.
• Hugging Face and GitHub Discussions: As soon as the model hit Hugging Face, it climbed the trending list. Developers opened issues on GitHub repositories (like LMDeploy, vLLM, and SGLang) to share results and solve problems with multi-GPU setups ([Bug] DeepSeekV3 instructions don’t work for multi-node H100 setup · Issue #2673 · sgl-project/sglang · GitHub). Within a day, we saw multi-node deployments, containerized versions, and conversion scripts all being shared. The expert commentary often focused on technical aspects: e.g., engineers at NVIDIA noting how this validates MoE approaches, or framework authors discussing how to better support 128K context and FP8 for this model. Simon Willison’s blog provided a concise summary that was widely shared, highlighting the MIT license and the shock of seeing 20 tok/s on a “consumer” machine (deepseek-ai/DeepSeek-V3-0324). His write-up served as both news and a how-to, influencing many to try it themselves.
• Industry Reactions: While OpenAI or Anthropic did not make public comments (as of this writing) about DeepSeek-V3, some industry figures chimed in indirectly. Nvidia’s CEO Jensen Huang, during an interview, referenced DeepSeek’s previous model R1 and noted it “consumes 100 times more compute than a non-reasoning AI” (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat) – essentially acknowledging the challenge and complexity DeepSeek tackled. This was cited in context to emphasize how impressive it is that DeepSeek’s team managed to deliver such performance under resource constraints. Huang’s comment underscores the respect even competitors have for what DeepSeek achieved technologically. Meanwhile, LinkedIn posts by AI practitioners celebrated the open-source win, with one calling DeepSeek V3 “a new leader in open source AI” and analyzing its cost-to-train vs performance metrics (OpenAI’s models became 7-20x cheaper over the last year. In the …). The general vibe among AI engineers is excitement – many see this as validating years of research into MoE, and a win for the open approach.
• Chinese Tech Circles: On platforms like Weibo or Zhihu (Chinese equivalents of Twitter/Quora), there was national pride in DeepSeek’s success. Chinese commentators noted that this open strategy is accelerating their AI progress domestically (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). Some mentioned Baidu’s open-sourcing of ERNIE 4.5 and others as part of a broader trend, essentially viewing DeepSeek-V3 as part of China’s open-source AI revolution that could challenge Western AI dominance (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat) (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This context is important – it’s not just one startup but a reflection of an ecosystem shift that has geopolitical and business ramifications.
• Balanced Views: Not everyone is declaring OpenAI doomed or DeepSeek victorious, of course. Several experts caution that closed models still have a lead in certain areas. There’s also the issue of trust and safety: open models like DeepSeek-V3 could be misused (e.g., generating disinformation or harmful content without the safety filters that OpenAI employs). Some discussions revolve around how to implement guardrails for open models now that they are so powerful and readily available. The community is actively looking at fine-tuning V3 or adding moderation layers if it’s to be deployed in consumer-facing applications.

In summary, the community and expert reaction has been a mix of astonishment, admiration, and analysis. A common refrain is that 2024-2025 is doing to AI what open-source did to software – taking what was once exclusive and expensive, and making it accessible to all with surprising speed. DeepSeek-V3’s release is a rallying point for open-source AI enthusiasts, and a real case study for researchers to chew on (expect academic papers analyzing its performance, training efficiency, and comparing it to the likes of GPT-4). It has undeniably moved the Overton window of what people consider possible in the open AI world.

Broader Impact: AI Accessibility, Decentralization, and the Future of LLMs

The ripple effects of DeepSeek-V3’s breakthrough go beyond just one model or one company. They touch on the very direction of AI development and deployment:

• AI Accessibility: We are witnessing a dramatic increase in AI accessibility. Not long ago, a model with nearly GPT-4 level performance was out of reach for anyone outside a few big tech companies. Now, with DeepSeek-V3, anyone (with sufficient hardware or even just an internet connection) can access a state-of-the-art LLM. This democratization means that researchers in academia, startups in developing countries, or hobbyists at home can experiment at the cutting edge without needing millions of dollars or special partnerships. It’s hard to overstate how important this is: accessibility fuels innovation. We’ll likely see new applications, plugins, and research papers built on DeepSeek-V3 or its successors, simply because more people have the means to try. It also means AI benefits can reach more people – for example, local running means AI assistants that respect user privacy and work offline (useful for journalists, healthcare applications, or users with sensitive data).
• Decentralization of AI Power: Linked to accessibility is the decentralization of AI. Until recently, a handful of companies (OpenAI, Google, etc.) held the most advanced models. That concentrated power raised concerns about monopoly, influence over information, and single points of failure (if one API goes down or changes terms). With powerful open models, the AI power structure becomes more distributed. We might see peer-to-peer networks serving models, or an ecosystem of smaller providers competing to serve or fine-tune models. This can lead to greater resilience and diversity in the AI applications available. It can also mitigate censorship or control – for better or worse, as no single entity can unilaterally decide what the AI can or cannot say when people can run their own. Society will need to adapt to a world where AI is everywhere and not controllable from a central kill-switch. That makes AI literacy and responsible use even more important.
• Open-Source vs Closed Development Paradigms: The success of DeepSeek-V3 vindicates the open-source development paradigm in AI. Much like Linux in the operating system world, we might be seeing the rise of an “Linux of AI” – a core set of open models that everyone builds on. Companies might start focusing on providing value-added services on top of open models (similar to how Red Hat built a business around open-source Linux). The future of LLMs could involve a few major open models (maybe from organizations like DeepSeek, Meta’s AI lab, etc.) that serve as bases, and countless fine-tuned versions for specific domains. This would leave closed models in a tough spot unless they are significantly ahead. It’s possible we’ll see more collaboration too – perhaps OpenAI even deciding to open-source certain components or join forces with open initiatives to set standards (e.g., for safety or evaluation benchmarks).
• Innovation Acceleration: With more minds and companies having access to top models, we may see an acceleration in innovative uses of AI. Expect to see LLMs integrated into edge devices, creative industries, scientific research, and more, in ways that weren’t possible when only an API or a small model was available. For instance, decentralized personal AI assistants could become normal – running on local machines but as capable as cloud AI. In education, students anywhere could have a powerful tutor AI without needing to pay for it, potentially reducing educational inequality. In scientific research, labs can use these models to sift through literature, generate hypotheses, or design experiments at a level that only top institutions with AI teams could before.
• Challenges and Future Directions: The open-source LLM revolution will not be without challenges. Ensuring safety and ethical use is a big one. When models are open, anyone can fine-tune them in unknown ways. We could see an increase in spam or deepfakes generated by these models. The community and perhaps regulators will have to work together on guidelines and detection tools. Another challenge is sustainability: training these huge models is costly (DeepSeek-V3 still required nearly 3 million GPU hours (DeepSeek-V3 Technical Report), likely funded by government or industry in China). Will open models continue to scale? Possibly, if collaborations or funding pools emerge, but it’s something to watch. It could be that open models slightly lag behind the absolute frontier (if GPT-5 or 6 goes into multi-trillions) due to resource needs, but they might find ways to do more with less (the way DeepSeek did with efficiency). There is also the question of evaluation: as we get many models, how do we benchmark them? Expect open leaderboards and competitions to become more prominent, which in turn spur further progress.
• Future of LLMs: Looking forward, it seems clear that mixture-of-experts and efficiency techniques are here to stay. DeepSeek-V3 has proven their worth. The future LLM might not be one giant black box, but a collection of experts, retrieval systems, and multi-step reasoning modules all working in concert – and much of that might be open-source. We might see hybrid approaches: perhaps OpenAI or others release smaller distilled versions of their big models for local use, or open-source groups incorporate features like multi-modality (image understanding) into their models. The lines will blur between what is considered a “research lab model” and a “community model.”

In essence, DeepSeek-V3’s success is a harbinger of an AI future that is more inclusive and decentralized. It challenges assumptions about who gets to wield advanced AI. If GPT-3’s reveal in 2020 was the start of the large model era, and ChatGPT’s debut in 2022 was the start of mainstream AI awareness, then DeepSeek-V3 in 2025 might be remembered as a key moment in the open-source AI era.

Conclusion

The advent of DeepSeek-V3 running at 20 tokens per second on a Mac Studio is a landmark moment in AI – one that encapsulates how far the field has come and hints at where it’s headed. We’ve seen how DeepSeek evolved from a little-known project into a trailblazer, delivering a 671B-parameter model that anyone can use. We unpacked the model’s architecture, from its clever Mixture-of-Experts design to its multi-token generation prowess, that allows it to combine massive scale with high speed (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). We marveled at the technical feat of getting such a model to run on Apple’s M3 Ultra chip, a reminder that today’s cutting-edge AI might not need a data center at all (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). In comparing DeepSeek-V3 with GPT-4, Mistral, LLaMA and others, we found that open models are now neck-and-neck with the best in many respects, each with their own strengths. And we discussed how this open-source powerhouse is sending shockwaves through OpenAI and its peers, forcing a re-examination of strategies in a quickly evolving landscape (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat) (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat).

For developers and AI enthusiasts, DeepSeek-V3 offers both inspiration and a toolkit: we provided guidance on installing and running it on a Mac Studio, showing that it’s not magic but method – the right hardware and software can put unprecedented AI capability in your hands (deepseek-ai/DeepSeek-V3-0324) (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). The community’s reaction – from excited tweets to rapid integration efforts – shows the collective momentum behind open models. Experts are already weighing in with analyses, and there’s a sense that this is just the beginning. As open models become more capable and widespread, we can expect AI to become even more embedded in daily life, largely on the terms set by users and communities rather than a few companies.

The key takeaways from DeepSeek-V3’s breakthrough are clear: open-source LLMs are now a force to be reckoned with, and they’re leveling the playing field of AI innovation. A year ago, running a model of this caliber outside a corporate lab might have seemed like science fiction – today it’s a demonstrated reality (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat). This democratization is likely to accelerate progress, spawn new creative applications, and empower a broader range of contributors in the AI field. It also raises important discussions about how to handle such powerful technology responsibly when it’s freely available.

In the coming years, the tug-of-war (or perhaps synergy) between open and closed AI development will shape the trajectory of the entire industry. DeepSeek-V3 has shown one possible future – one where the most advanced AI models are open, fast, and accessible to all who seek them. For OpenAI and others, the challenge will be to adapt and innovate in step with this changing reality. For the rest of us, it’s an exciting time: the future of LLMs looks more open, decentralized, and dynamic than ever. As we conclude, one can’t help but feel that we’re at the dawn of a new era – one where anyone can “deep seek” the capabilities of a GPT-scale model on their own terms, and in doing so, collectively push the boundaries of what AI can achieve.

Sources: DeepSeek-AI Technical Report (DeepSeek-V3 Technical Report) (DeepSeek-V3 Technical Report); VentureBeat News on DeepSeek-V3 (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat) (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat) (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat); Simon Willison’s Weblog (deepseek-ai/DeepSeek-V3-0324) (DeepSeek-V3 now runs at 20 tokens per second on Mac Studio, and that’s a nightmare for OpenAI | VentureBeat); InfoQ Report (DeepSeek Open-Sources DeepSeek-V3, a 671B Parameter Mixture of Experts LLM – InfoQ) (DeepSeek Open-Sources DeepSeek-V3, a 671B Parameter Mixture of Experts LLM – InfoQ); GitHub Discussions ([Bug] DeepSeekV3 instructions don’t work for multi-node H100 setup · Issue #2673 · sgl-project/sglang · GitHub); Apple M3 Ultra Specifications (Mac Studio – Apple).