AI Coding Live News

On November 5 Meta AI (@AIatMeta) said Muse Realtime Avatar, previewed at Connect, is rolling out in the US Muse app, turning Muse Realtime Voice into interactive live avatars. The research blog describes an audio-driven Diffusion Transformer that shares the speech-token stream with voice for lip-sync and expression; a single GB200 session streams 448×768 at 25 fps with about 870 ms from end of user turn to first synchronized audio-video byte, with Meta Video Seal invisible watermarking. Muse is for users 18+; no global schedule or developer API was announced.

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On 2026-10-23 (16:53 UTC) the official @blocks account unveiled Dessa, and Jack Dorsey called it the most authentic full-duplex speech model ever built. The official page block.xyz/dessa says Dessa is a true speech-to-speech model trained only on recordings of human conversation, with no text and no pretrained models. It is full-duplex, so it listens and talks at the same time, cuts in and backchannels naturally, runs faster than real time, and runs locally on a MacBook Pro M4 Max or better. Block says it was trained on a single NVIDIA GB200 NVL72 and learned language from hundreds of times fewer words than LLMs. There is no public API or weights yet; a small group of AI safety researchers is getting early access.

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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.

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On Oct 20, the FastVideo team at UCSD's Hao AI Lab open-sourced FastH3 Omni, a distilled version of MiniMax H3's reference-to-video-and-audio (Ref2VA) model that generates video with synchronized stereo audio from text plus up to nine images, video clips and voice references. Sampling drops from 50 steps to eight transformer evaluations, cutting end-to-end latency 5.6-11.9x on eight GB200s by the team's measurements. Training code is open, with NVFP4 weights for DGX Spark and RTX 5090 and ComfyUI support.

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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.

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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.

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On 2026-10-15 Microsoft AI published a technical post and PDF saying it has finished pre-training its in-house base model MAI-Base-2: MoE in every layer (1 shared + 7-of-512 routed experts), 80B active / 2.8T total, 102 layers, native encoder-free vision, a 50T-token mixture and a March 2026 knowledge cutoff. Per MAI, it beats every open-weight model tested on held-out BPB and its recipe is 4.2–12.3x more pre-training-FLOPs-efficient than DeepSeek-V4-Pro-Base. It is still in post-training: no API, no weights and no release date yet; Microsoft plans to license weights to trusted partners via Foundry.

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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).

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Inferact open-sourced inferact/tpu-megakernels on 2026-09-23: a Pallas megakernel collection for TPU v7. Their blog reports Kimi K3 at 709 tok/s on 16× TPU v7 Ironwood vs 452 tok/s on 16× GB200 under DSpark speculative decoding (acceptance length 6), and roughly 1.4–2× the GB200 baseline at batch sizes 1–8 without speculation. All 92 MoE layers of Kimi K3 run in a single kernel with cross-layer weight prefetch.

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