dario begged me in dms to not do agent swarms at home but whatever, this is nemotron 120B A12B running 64 concurrents at a bit over 100 tok/sec each for an aggregate of almost 7K tok/sec on a single GB300 (Asus DGX Station) believe it or not, the agents did NOT magically just start hacking the planet tho. weird.

Every great machine begins on a workbench. Now you can build a humanoid robot on yours. docs.menlo.ai/

As someone has done my share of Python, this is so true.

🚀 DeepSeek-V4.1-Flash-1017 is here! Stronger benchmark performance, faster task completion, and shorter outputs than the previous version—a Pareto improvement in performance and cost. Now live on our website, app, and API. 🛠️ Optimized for @DeepSeekHarness, Tencent CodeBuddy, WorkBuddy, and more. Upgrade to DeepSeek Harness v0.2.1-rc.1 or later for the best experience with the new model. 1/4

Nemotron 3.5 Super VL is the highest-scoring open weights model from the US, at 26 on the Intelligence Index. It edges out Thinking Machines' Inkling Small (26) and passes NVIDIA's previous flagship, Nemotron 3 Ultra (23), and Meta's Muse Glimmer (17) Despite the advance, the US still trails the United Arab Emirates (K2 Horizon 375B A23B, 31) and China (MiMo-V2.6-Pro, 46) on open weights intelligence.

okay seriously, why the hell are so many people leaving openai? now their computing chief is heading out too. i’m actually starting to get concerned about what’s happening at openai.

We’ve added DeepSeek V4.1 Flash to AA-AgentPerf, with results for NVIDIA and AMD hardware. At 100 tokens/s per user, NVIDIA GB300 is in first place with throughput up to 261M tokens/s per MW. AA-AgentPerf is our agentic inference performance benchmark. It replays real agentic coding sessions, including prompt caching and tool-call delays, to give a realistic measure of inference performance in day-to-day agentic workloads. We measure total system throughput per megawatt of runtime power, and per-agent output speed at varying concurrency levels, to show the trade-off between system utilization and individual user experience. DeepSeek V4.1 Flash joins the leaderboard today. It brings the leaderboard to four featured models, joining DeepSeek V4 Pro 0813, Kimi K3, and GLM-5.2/5.3. We are evolving the featured models as popular new open-weights models release, and will feature day-0 results for major future releases. Background on DeepSeek v4.1 Flash: ➤ It scores 39 on the Artificial Analysis Intelligence Index, ahead of DeepSeek’s earlier V4 Pro 0813 at 36, with about a third of the parameters (552B ‘backbone’ and 16B active, against 1.6T and 49B) ➤ It is built on a new architecture, with a causal encoder-decoder design that activates 8B parameters per input token and 16B per output token. It uses a further 196B parameters of Engram n-gram memory that can sit in host memory rather than on the accelerator, and compressed sparse attention that needs about a quarter of even the earlier V4 Flash model’s KV cache per token ➤ This makes it much more efficient to serve, since the weights fit on fewer GPUs and the smaller KV cache leaves room for more concurrent agents per GPU Initial AA-AgentPerf results for DeepSeek V4.1 Flash: ➤ V4.1 Flash runs on as few as 2 GPUs, so rack-scale systems get less advantage over 8-GPU single nodes like B300, which stays within about 20% of rack-scale GB300 NVL72 across the curve, and leads at both ends ➤ At 100 tok/s per user, GB300 leads with ~261M tokens/s per MW, ahead of B300 (~220M), B200 (~193M), GB200 (~178M) and MI355X (~128M) ➤ Every Blackwell system and MI355X can reach around 600 to 670 tok/s output speed for a single agent (ie. at batch size of one), against 372 to 392 tok/s on H100 and H200 ➤ V4.1 Flash is both more intelligent than V4 Pro 0813 and much cheaper to serve, yielding ~1.3x (GB300) to ~6.2x (B200) the throughput per MW at 100 tok/s per user compared to its larger cousin

never larp

Prime Intellect has acquired Moondream We’re bringing Moondream’s open vision models and inference optimizations into our stack Train and deploy multimodal models across vision, video, voice, decision models and computer use

Ready to supercharge your local AI hardware? 👀 Build an optimized docker for your @NVIDIAAI RTX 6000 Pro or DGX Spark at docker.local-inference-lab.a…