AI Coding Live News

On 2026-11-06 Reflection's official account released the Beam model technical report (a 156-page PDF), detailing how its 501.3B-total / 23.4B-active open-weight MoE was trained: 23.8T pretraining tokens on 6,144 GB300 GPUs in under four weeks, then a fully asynchronous RL run on about 10.5K GB300s for four weeks that produced over 100M rollouts. The report says FP8 and NVFP4 checkpoints keep 99.93% and 98.98% of median benchmark scores, and the 1.93B DFlash 2 drafter speeds single-request decoding 1.96–3.03x (vendor figures).

Zoom

On Oct 28 DiffuSpace, whose members come from the HKU NLP group behind Dream 7B, released its first model, Clover-35B-A3B-Preview: a mixture-of-experts diffusion language model initialized from Qwen3.6-35B-A3B, with 35B total and about 3B active parameters, a 128K context window and block-parallel denoising. Vendor-reported results include 73.6 on SWE-bench Verified, 51.7 on Terminal-Bench 2.1 and 83.8 on GPQA Diamond, with 680 tokens/s on a single GB300 in agent workloads. Weights are Apache 2.0 on Hugging Face, ModelScope and GitHub, with SGLang and llama.cpp support.

Zoom

Hours after DeepSeek open-sourced DeepInfer, the vLLM project team released official benchmark parity data serving DeepSeek-V4.1-Flash. Utilizing DeepSeek's identical decoding setup (131K context window, nextn=3 speculative decoding), vLLM closed its throughput performance delta to within 6% of DeepInfer across batch sizes 8 through 256 on NVIDIA GB200 systems. On GB300 hardware, vLLM attained up to 1,000,000 tokens/second per GPU and 826 tokens/second per user, generating $38.96 in hourly GPU revenue at DeepSeek off-peak rates. vLLM simultaneously shipped day-0 runtime support for FastH3-Omni and Spark-X2.6-4B.

Zoom

On Oct 20, NVIDIA's GEAR lab (Jim Fan's team) open-sourced Flora, a model suite that turns human and robot manipulation video into timestamped action captions, process rewards, outcome rewards and rubric scores. It includes Flora-Captioner 27B and Flora-Rewarder 27B (both on Qwen3.5-27B) and Flora-TempSeg 9B (a LoRA on Qwen3.5-9B), with weights on Hugging Face under an NVIDIA non-commercial license. NVIDIA says it is the in-house data engine used to train GR00T, labeling 200,000 hours of video a day on 512 GB300s, matching GPT-6 Astra on annotation quality at roughly 125x lower cost.

Zoom

Meta's engineering blog on October 19 gave the first detailed look at Prometheus, the New Albany, Ohio supercluster that trained the Muse family, including Muse Wonder. Phase one is complete with hundreds of thousands of GPUs, and the full build targets about 1 million. Muse Wonder training reached more than 85% goodput in pretraining and more than 90% in reinforcement learning, with a mean time between interruptions of about 47K GPU hours, since raised above 54K by firmware fixes. It runs GB200 NVL72 and GB300 NVL72 platforms, with Vera Rubin NVL72 arriving soon.

Zoom

SemiAnalysis revealed the first Google TPUv7 benchmark submission for the 2.8T Kimi K3 MoE model, led end-to-end by Inferact (the creators of vLLM) and Google using the TorchTPU vLLM stack. At a hardware rental cost of just $1.21/chip-hour (vs $2.31 for GB300 and $1.86 for GB200), TPUv7 delivers ~2.2M tokens per $1 TCO in the ultra-high interactivity regime (~390 tok/s/user), outpacing NVIDIA GB200 NVL72, B300, and AMD MI355X on cost-performance.

Zoom

vLLM and NVIDIA unveiled a unified CuTe DSL decode megakernel (~12,000 LoC authored by NVIDIA CentML's Benjamin Chislett) for Moonshot AI's frontier Kimi K3 model on NVIDIA GB300 NVL72. By fusing attention, MoE, and cross-GPU communication, the optimization cuts per-layer latency from 130 µs (23 kernels) to 69 µs (13 kernels), delivering 842 tokens/s at batch=1 with DSpark speculative decoding (1.73x faster than stock vLLM) and up to 1.8x higher P90 per-user interactivity (403.2–423 tok/s/user) on SemiAnalysis AgentX, with DeepSeek-V4.1-Flash (PR #61868) and GLM-5.3 support coming next.

Zoom

A vLLM blog post by NVIDIA engineer Benjamin Chislett details an agent-assisted CuTe DSL decode path for Kimi K3 on NVIDIA GB300 that fuses attention, MoE and NVLink communication, cutting per-layer time from 130 µs across 23 kernels to 69 µs across 13. With DSpark speculative decoding on 16 GPUs it reaches 842 tok/s at batch 1, 1.73x stock vLLM; PR #61867 was merged on Oct 16.

Zoom

On Oct 9 the vLLM team, with Inferact, Red Hat and NVIDIA, shipped early Vera Rubin NVL72 support: CUDA 13.4 nightly images already serve DeepSeek, Kimi, GLM and MiniMax. Vendor-reported results: up to 7.84x per-GPU throughput vs GB200 on SemiAnalysis AgentX (MiniMax M3, matched interactivity) and up to 3.7x vs GB300 NVL72 on the MLPerf Inference v6.1 VLM benchmark (Qwen3-VL-235B-A22B).

Zoom