NVIDIA H100

Headlines · 6

1. 6d
   Modal tests mixed-precision, mixed-accelerator RL samplers on Qwen3.6-35B-A3B

   Modal published an engineering post asking whether large-model reinforcement learning can use samplers that run different precisions and accelerators than the trainer, each with its own kernels. Its mixed samplers span six accelerator types — B200 and B300 serving NVFP4 weights, H100 and H200 serving FP8 and BF16, and A100 and RTX PRO 6000 serving BF16 — all with FP8 KV caches, feeding a single BF16 trainer on B200. Modal says off-policy correction plus autoscaling serverless GPUs let it train Qwen3.6-35B-A3B fully asynchronously on agentic coding tasks with up to 256K-token contexts, reporting that the mixed samplers' mismatch averaged 4.7× that of homogeneous samplers.

   Modal Blog · 🔥 0
2. 6d
   Anthropic study: small AI models could self-replicate across devices

   Anthropic researchers tested whether small models could spread across poorly secured consumer devices, distributing Gemma4-26B-A4B across 15 Android TV boxes priced at about $55 each and generating roughly 0.39 tokens per second. A Qwen3.5-9B model set up an inference environment on 8 of 9 hardware types, while GLM-5.2 completed the full replication chain across eight simulated nodes in four of five trials, according to reports on the study. The researchers also found that passing notes between Qwen3.5-9B generations raised deployment success from 45% to 61–84%, but no full deploy-and-replicate loop was shown on real TV boxes, and the replication test relied on unauthenticated ADB access.

   虎嗅 AI · 🔥 13
3. 7d
   Flo Health fine-tunes open models with Databricks to boost medical safety and usefulness

   A Databricks blog post describes how Flo Health, a women's health app with 81 million monthly active users, built safer and more useful custom open models on Databricks AI Runtime. Flo first created an evaluation system of 26 AI judges (12 medical-safety, 6 micro-behavior, 8 usefulness) to turn medical standards into a training signal, generated more than 15,000 privacy-first synthetic questions, then ran supervised fine-tuning and DPO on 32 NVIDIA H100 GPUs using LLM Foundry and Compose-RL with MLflow tracking. Flo reports medical safety rising from 34% to about 61%, usefulness from 42% to 76%, and the share of responses meeting both from 16% to 47%, and estimates tailored models can cut inference costs by an order of magnitude versus frontier APIs; it plans to apply the pipeline to newer open-weight models such as Gemma, Qwen and Kimi.

   Databricks Blog · 🔥 0
4. Oct 11
   Biren plans HK$4.02B placement, total raise hits HK$17.26B

   Biren Technology announced on Oct. 8 a placement of 130 million H shares at HK$31.8 apiece to no fewer than six placees, about 4.76% of post-issue share capital, for estimated net proceeds of roughly HK$4.02 billion. The price was a 9.76% discount to the previous close, and the stock fell 11.85% to HK$30.36 that day, closing at HK$31 on Oct. 9 for a market value of about HK$80.6 billion. Including a roughly HK$6.204 billion net raise at its January listing and a HK$7.038 billion placement in July, Biren has raised about HK$17.261 billion (about RMB 14.7 billion) since listing, the most among listed Chinese GPU makers. The company says about 70% of this round goes to strategic supply-chain procurement, production preparation and commercialization for its next-generation flagship BR20X, which it benchmarks against NVIDIA's H200 and expects to enter post-tape-out validation and customer testing in Q4, with large-scale deployment in 2027.

   钛媒体 · 🔥 0
5. Oct 11
   Microsoft open-sources TRELLIS.2, a 4B image-to-3D model with PBR output

   Microsoft has published TRELLIS.2 on GitHub, a 4-billion-parameter image-to-3D generative model built on a "field-free" sparse voxel representation called O-Voxel, which the repo says handles open surfaces, non-manifold geometry and internal enclosed structures and predicts PBR surface attributes such as base color, roughness, metallic and opacity. The project reports generation times on an NVIDIA H100 of about 3 seconds at 512³ (2s shape + 1s material), roughly 17 seconds at 1024³ and about 60 seconds at 1536³, plus under 10 seconds on a single CPU to convert a textured mesh to O-Voxel and under 100ms on CUDA for the reverse. Code, data tooling and training scripts are provided and the TRELLIS.2-4B weights are on Hugging Face, but the code is Linux-only and needs an NVIDIA GPU with at least 24GB of memory (verified on A100 and H100, CUDA 12.4).

   GitHub Trending · Python · 🔥 0
6. Oct 9
   Ai2 replaces priority scheduler with fair-share GPU budgets and time-slicing contracts

   Ai2's AI Infrastructure team says it replaced its priority-based cluster scheduler with GPU time budgets, hierarchical fair-share allocation and a time-slicing contract for dividing scarce compute among research projects. Ai2 manages thousands of NVIDIA H100, B200 and B300 GPUs in clusters ranging from 88 to 1024 GPUs, serving about 150 internal researchers, with submitted workloads requesting 2–3x the available capacity at any time. The post says the old setup led to GPU "squatting," priority inflation and manual negotiations over shutting down jobs, and that the new contract asks workloads to declare a minimum runtime during which they cannot be preempted before being requeued. Ai2 also reports a 74% reduction in repairs requiring human intervention, a self-reported figure, and says it is investigating whether capacity fragmentation could increase queue waits for the largest workloads.

   Ai2 Blog · 🔥 0

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