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2.5: offers explicit thinking budgets, allowing enterprises to cap reasoning depth; Alibaba Qwen3: supports instruction-driven switching between reasoning and non-reasoning modes; DeepSeek v3.1: merges conversational and reasoning flows within a dual-mode single model The trend is clear: future inference systems will be defined by selectivity and intelligence, not just model size. Recent Research: vLLM Semantic Router Responding to this shift, the vLLM Semantic Router offers an open-source, inte

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h is approximately 1Tbit/s of unidirectional bandwidth vs 14.4Tbit/s of NVLink for R200. This is enough I/O for performing prefill even for modern MoE frontier LLMs. For instance, DeepSeek V3 when running on the NVFP4 number format will require 335GB of memory capacity to load all the model weights – this exceeds the 128GB of memory capacity of a single CPX chip. This can be overcome by using pipeline parallelism (‘PP’), where multiple layers of a model are split across different GPUs. In PP, ea

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But constraints have also led to adaptation, and Chinese companies have adapted. High batch sizes and disaggregated serving are but two examples. Despite advances, in the case of DeepSeek, most of their tokens are still inferenced on western hardware. We wrote about this dynamic in our recent DeepSeek debrief. Training of DeepSeek's next generation model was also delayed by the use of Huawei chips, as we also said in the debrief. This is not a stable equilibrium. There are always moving pieces

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ting, the open-weight 235B-Instruct variant is ranked 8 on the LMArena leaderboard, tied with the proprietary Claude Opus 4. The only 2 other open-weight LLMs that rank higher are DeepSeek 3.1 (3x larger) and Kimi K2 (4x larger). On September 5th, Qwen3 released a 1T parameter “max” variant on their platform that beats Kimi K2, DeepSeek 3.1, and Claude Opus 4 on all major benchmarks; however, this model is closed-source for now; There are many different model sizes available for different comput

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"Attention Is All You Need" https://arxiv.org/abs/1706.03762; "Efficient Memory Management for Large Language Model Serving with PagedAttention" https://arxiv.org/abs/2309.06180; "DeepSeek-V2: A Strong, Economical, and Efficient Mixture-of-Experts Language Model" https://arxiv.org/abs/2405.04434; "Jenga: Effective Memory Management for Serving LLM with Heterogeneity" https://arxiv.org/abs/2503.18292; "Orca: A Distributed Serving System for Transformer-Based Generative Models" https://www.usenix.

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from 128 H100s to 2048 H100s and across different versions of Nvidia software. Later in this report, we will also analyze GB200 NVL72 benchmark results across Llama4 400B MoE and DeepSeek 670B MoE and compare this data to our earlier results from the H100. We will discuss whether the GB200 NVL72 performance per $ advantages survives once reliability issues are factored in. Downtime from poor reliability and lost engineering time is one of the main factors that we will capture in our perf per TC

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M, it’s a core part of the inference pipeline. Combined with caching, dynamic shape support, CUDA Graphs, and custom passes, it enables efficient, scalable LLM serving across any environment. Related Posts DeepSeek-V4.1-Flash on vLLM: 5x Agentic Throughput Since Day 0 Within three weeks of release, vLLM made DeepSeek-V4.1-Flash 1.9x faster at low concurrency and lifted its throughput 5x on SemiAnalysis AgentX, with SWA bounded replay, CUDA graphs, DeepSeek's new kernels, and vLLM kernel fusions.

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those python files are scanned, because as we already mentioned, static code analysis is NP-hard. We can see one example for usage of this feature with the popular Chinese models DeepSeek-R1 and Kimi-K2: Although using such a feature requires the user to explicitly state they “trust remote code”, unfortunately users are not sufficiently equipped with tools to decide whether source code is worthy of their trust or not. Static scanners, sadly, currently provide no insights into source code files.

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trends and developments point towards more, smaller models as being beneficial. This change, at a constant total parameter size, is nicely illustrated in Figure 15 below from the DeepSeekMoE paper. Notably, unlike DeepSeek's models, neither gpt-oss nor Qwen3 uses shared experts, though. To be fair, the small number of experts in gpt-oss could be a side effect of the 20B size. Looking at the 120B mode below, they indeed increased the number of experts (and transformer blocks) while keeping every

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spite seemingly unlimited resources, Meta has been falling behind foundation labs in model performance. The real wake-up call came when Meta lost its lead in open-weight models to DeepSeek. That stirred the sleeping giant. Now in full Founder Mode, Mark Zuckerberg is personally leading Meta’s charge, identifying Meta’s two core shortcomings: Talent and Compute. As one of the last founders still running a tech behemoth, Mark doesn’t need SemiAnalysis to tell him to slow down stock buybacks to fun

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agnosis We evaluated a comprehensive suite of frontier generative AI models against the 304 NEJM cases. The foundation models tested included GPT, Llama, Claude, Gemini, Grok, and DeepSeek. Beyond baseline benchmarking, we also developed the Microsoft AI Diagnostic Orchestrator (MAI-DxO), a system designed to emulate a virtual panel of physicians with diverse diagnostic approaches collaborating to solve diagnostic cases. We believe that orchestrating multiple language models will be critical to

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oftware engineering, tool use, and long-context understanding. MiniMax-M1 leads among open-source models; Right: Theoretical inference FLOPs scaling with token length. Compared to DeepSeek R1, MiniMax-M1 uses only 25% of the FLOPs when generating sequences of 100k tokens Deploying MiniMax-M1 with vLLM We recommend deploying MiniMax-M1 using vLLM for optimal performance. Our tests demonstrate the following key benefits: Outstanding throughput; Efficient and intelligent memory management; Robust s

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ith model size and math- or reasoning-specific pre-training data (see also [4]). This observation aligns with the practices of similar strong, small reasoning models. For example, DeepSeek-R1-Distill-Qwen-1.5B [2] was trained on approximately 1M samples, with Nvidia’s OpenMath-Nemotron-1.5B [5] further training this model on an additional 3M samples. We found Nvidia’s OpenMathReasoning [5] and Llama Nemotron Post-Training [6] datasets particularly effective among other openly available datasets,

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t finding and can be further improved to create higher reward scores for lower-resource languages. We used the real-valued scoring approach to compare three models: Llama 3.3 70B, Deepseek v3, and Deepseek R1. For outputs in formal Indic languages (i.e., not code-mixed or romanised) we found that Deepseek R1 with English thinking tokens and Indic language output in non-thinking tokens, generates consistently the highest quality completions, with an average score of over 8 (on a 0 to 9 scale) acr

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ior open-source SoTA models by more than 6% points. When evaluated under the same test scaffold (OpenHands, provided by All Hands AI 🙌), Devstral exceeds far larger models such as Deepseek-V3-0324 (671B) and Qwen3 232B-A22B. In the table below, we also compare Devstral to closed and open models evaluated under any scaffold (including ones custom for the model). Here, we find that Devstral achieves substantially better performance than a number of closed-source alternatives. For example, Devstral

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ance, Mistral Medium 3 also surpasses leading open models such as Llama 4 Maverick and enterprise models such as Cohere Command A. On pricing, the model beats cost leaders such as DeepSeek v3, both in API and self-deployed systems. Additionally, Mistral Medium 3 can also be deployed on any cloud, including self-hosted environments of four GPUs and above. Mistral Medium 3 is designed to be frontier-class, particularly in categories of professional use. In the evaluations below, we use numbers rep

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ans designing for the real world: heterogeneous GPUs, public internet latency, and unreliable but abundant FLOPs. With the rise of reinforcement learning for reasoning models like DeepSeek R1, inference has moved to center stage, and is now a core component of the entire AI stack: Training: Generate rollouts during reinforcement learning (e.g. INTELLECT-2); Distillation: Creating synthetic data at scale (e.g. SYNTHETIC-1); Evaluation: Benchmarking model performance and safety That's why our next

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(PPO)—are often hindered by substantial computational overhead. This challenge is particularly pronounced with models that excel at complex reasoning tasks (such as OpenAI-o1 and DeepSeek-R1), where generating long chain-of-thought (CoT) outputs can account for up to 90% of total training time. These models must produce detailed, step-by-step reasoning that can span thousands of tokens, making inference significantly more time-consuming than the training phase itself. As a pioneering inference

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idth interconnects between each GPU, like NVLink or InfiniBand, to minimize overhead from the increased communication costs. Pipeline Parallelism For extremely large models (e.g., DeepSeek R1, Llama 3.1 405B), a single node may not suffice. Pipeline parallelism shards models across nodes, each handling specific contiguous model layers. How It Works Each GPU loads and processes a distinct set of layers; Send/Receive Operations: Intermediate activations are transmitted between GPUs as computation

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a powerful base for accelerating progress. Note that Mistral Small 3 is neither trained with RL nor synthetic data, so is earlier in the model production pipeline than models like Deepseek R1 (a great and complementary piece of open-source technology!). It can serve as a great base model for building accrued reasoning capacities. We look forward to seeing how the open-source community adopts and customizes it. Performance Human Evaluations We conducted side by side evaluations with an external t

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