搜索“DeepSeek”

varies greatly across different model architectures, and access patterns also differ. As shown below, MiMo-V2.5-Pro and MiMo-V2.5 rank second in KVCache efficiency, trailing only DeepSeek-V4-Pro and DeepSeek-V4-Flash. It is worth noting that actual cost differences do not strictly correspond to KVCache size ratios, as there are fixed compute and memory access costs independent of sequence length. However, in long-context scenarios, the overall trend holds: the gains are marginal for short seque

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,024 response tokens): training converges as expected, with reward rising from about 45% to 95% over 50 rollouts (Figure 3), which matches the GB300 reward growth curve perfectly; DeepSeek-V4-Flash (4-layer, FP8) on GSM8K (32 prompts × 8 samples per step, up to 256 response tokens): the FP8 path runs end-to-end for both rollout and training Figure 3. GSM8K training reward over 50 rollouts, Qwen3-30B-A3B (256 prompts × 8 samples per rollout, up to 1,024 response tokens), GB300 vs Vera Rubin. Thes

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G edge network orchestration A new arXiv paper used Jev to interpret service contracts at the network edge. At matched correctness, Jev cut median decision latency by 22.4% versus DeepSeek and 61.9% versus Gemini. 5. Real-time game agents TypeSafe’s Doom demo ran Jev at 10 queries per second. The team estimated the cost at roughly $7 per hour. Competitor Landscape: 7 Decision Models Compared The table below covers Jev, its closest commercial rival, and the leading open-weight options. All specs

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epending on what is supported by the chips in the cluster. We use a common set of shapes for different gate up and down projections at varying batch sizes in the Kimi K2.5, K3 and DeepSeek V3, V4 Pro models. We then test GEMMs, GEMV bandwidth, and MAMF (Maximum Achievable Matmul FLOPS, from Stas Bekman), which runs a sweep for a while on each GPU, per precision, using cuBLASLt (or rocBLAS). We report FLOPs for all of our GEMM tests. We also test memory, through a GEMV test, and nvbandwidth . GEM

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ranks slightly lower overall might be excellent at the kind of coding work you do every day. For reference, the most popular open models in the space right now are GLM 5.3 Flash, DeepSeek V4 Flash , Kimi K3, and MiniMax M3 . These change very rapidly though. After you’ve selected a few models to try out, the next step is to find a provider that hosts them. The way these providers work is that you send them an API request and they run the model on their GPUs. You pay for the input you send to th

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why changing the effort invalidates the KV cache. I think closed GPT models call reasoning effort “juice,” since you can ask most models how much juice they have. In DwarfStar for DeepSeek with max reasoning this is added to the system prompt: Don’t Think The destination of reasoning tokens is therefore a learned convention: the model is trained to keep scratch work out of the final channel. Trick it into thinking it is in that channel and it may leak tokens. We have even seen older models, when

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t. Where the frontier labs go Seventy is what disciplined engineering gets you. Going further means changing the architecture itself, and here the labs are placing different bets. DeepSeek’s multi-head latent attention squeezes each token’s key-value pair into a small latent rather than storing it in full, which both speeds inference and stretches context toward a million tokens; it was one of the more copied ideas after DeepSeek-R1. Qwen swaps standard quadratic attention for a linear network o

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minute task timeout. Tasks took 6m 53s on average. That makes Fable slower per task than GPT 5.5, Opus 4.7, and BU 2.0, but faster than Gemini 3 Pro, Qwen 3.6 Plus, Kimi K2.6, and DeepSeek V4 Pro. The cost comparison makes the tradeoff clear. Fable completed many of the long research and browsing tasks that usually separate stronger web agents from weaker ones. These are tasks that require following many steps of reasoning, checking constraints, and returning a structured answer rather than extr

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came from treating inference as a full-stack problem: profiling end-to-end, identifying the most expensive operations, and eliminating them one by one. ThunderMLA. Kimi K2.5 uses DeepSeek's Multi-head Latent Attention (MLA) architecture. Standard implementations run two separate kernel launches per decode step. Our ThunderMLA — part of our ThunderKittens kernel library — fuses these into a single megakernel, eliminating launch overhead and the tail effects between them. On representative decode

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point that provides high quality multi-lingual transcription at an optimized speed. Then a large language model translates that transcript. Here we leverage the latest advances of Deepseek V4 Pro as default translator. We also enable the user's input of a predefined list of translation rules to maintain the faithfulness and accuracy. Finally, the TTS model generates translated speech, allowing users to specify their desired voice characteristics in plain text. Together-hosted Cartesia’s Sonic 3

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B (Feb 13, 2026); Qwen 3.5 (Feb 15, 2026); Ant Group’s Ling 2.5 1T & Ring 2.5 1T (Feb 16, 2026); Cohere’s Tiny Aya (Feb 17, 2026); Update 1: Sarvam 30B and 105B (Mar 6, 2026) (PS: DeepSeek V4 will be added once released.) Since there’s a lot of ground to cover, I will be referencing my previous The Big LLM Architecture Comparison article for certain technical topics (like Mixture-of-Experts, QK-Norm, Multi-head Latent Attention, etc.) throughout this article for background information to avoid r

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wise training-inference consistency a scalable and reusable feature. If you're interested or would like to contribute, please join these Slack channels: sig-post-training; sig-batch-invariant 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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From DeepSeek R1 to MiniMax-M2, the largest and most capable open-weight LLMs today remain autoregressive decoder-style transformers, which are built on flavors of the original multi-head attention mechanism. However, we have also seen alternatives to standard LLMs popping up in recent years, from text diffusion models to the most recent linear attention hybrid architectures. Some of them are geared towards better efficiency, and others, like code world models, aim to improve modeling performanc

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cial thanks to Vensen Mu, Jeff Aw, Jun Kang Chow, Tun Jian Tan, Pin Siang Tan, Amir Balwel, Ye Hur Cheong, Zhiyao Cen and Kaichao You for developing the Sleep Mode feature and this blog post. 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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is ongoing and there are an overwhelming number of improvements on the horizon. Looking ahead, we’re working to unlock significant throughput gains on cluster-scale inference for DeepSeek, Qwen, gpt-oss, and many more through speculative decoding and Data+Expert Parallel (DEP) configurations. With NVIDIA’s gpt-oss-120b-Eagle3-v2, which incorporates Eagle speculative decoding, we anticipate up to 2-3x improvement in throughput. Leveraging DEP, which takes advantage of the 1,800 GB/s low latency

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egorize PRMs as “step-level judges,” which are predominantly developed for training, not pure evaluation. (In practice, PRMs are difficult to train reliably at scale. For example, DeepSeek R1 did not adopt PRMs and instead combined verifiers for the reasoning training.) Judge-based evaluations offer advantages over preference-based leaderboards, including scalability and consistency, as they do not rely on large pools of human voters. (Technically, it is possible to outsource the preference-base

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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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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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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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