NVIDIA B200

Headlines · 4

1. 3d
   ORL open-sourced: diffusion LLM hits 2,788 tokens/s on a single RTX 5090

   A research team posted ORL (On-Policy Residual Learning), a method for making diffusion language models converge in fewer forward passes so they can finalize more tokens per step, and open-sourced the models, training code, evaluation pipeline and a custom inference engine called dlmrt. The authors self-report 2,788 tokens/s for LLaDA-2.1-mini plus ORL in NVFP4 on a single RTX 5090, 7,708 tokens/s for DiffusionGemma 26B MoE on 8× B200, up to 6.9× higher end-to-end throughput on QwenDLM-4B, and a HumanEval pass@1 rise from 82.9% to 89.6%. They caution that all figures are batch-size-1 single-request peaks rather than averages over arbitrary prompts or aggregate throughput, and that the DFlash and EAGLE3 comparisons are end-to-end system tests in which ORL runs on dlmrt while the speculative decoders run on vLLM.

   r/MachineLearning · 🔥 0
2. 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
3. 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
4. Oct 8
   MiniMax H3 live video generation on a single RTX 5090

   A Comfy blog post presents ComfyStreamerH3, an open-source custom ComfyUI node that runs MiniMax H3 on one 32GB RTX 5090, producing 15 seconds of 448×256 video in 15 seconds or less with a four-step setup. The listed optimizations include sparse attention that applies full attention to only about 20% of video blocks, NicoLab28's ClipProj adapting Qwen3-VL-4B as a stand-in for H3's 32B text encoder (about 4.5GB vs 15.7GB), FastVideo's pruned INT8 checkpoint, a fused FP4 MLP, and Kijai's INT8 tiled decoder overlapped with NVENC encoding, cutting VRAM needs from 80GB to under 30GB. The author says running such models continuously can cost upwards of $6,000 per day while RTX 5090 rentals start near 70 cents an hour, and notes FastVideo's Preview V1 took 47.2 seconds for a 15-second 1344×768 clip on one B200. The clips still show visible artifacts, and the author suggests passing them through a cheap upscaler such as SolRefinery.

   Comfy Blog · 🔥 0

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