AI Coding Live News

On Nov 2, semiconductor and AI research firm SemiAnalysis released GDPx, a benchmark of agentic capability across 11 domains: chip design, cybersecurity, biology, finance, legal, quant trading, recursive self-improvement (RSI), hardware design, materials science, alignment and software engineering. Its 206 tasks were built by 14 specialist data partners, with SemiAnalysis doing final QA. On the official leaderboard Claude Opus 5.5 leads at 12.0%, followed by Gemini 4 Carbon at 10.9% and GPT-6 Astra at 9.6%; nearly every model scores 0 on finance and legal. SemiAnalysis says safety classifiers drag down Opus 5.5 and GPT-6 Astra, and plans a new version every 3–6 months. All scores are as reported by SemiAnalysis.

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SemiAnalysis published its major forward-looking industry analysis 'Datacenters 2027: The Year of Acceleration'. The report reveals an unprecedented +50GW of capacity added in a single year, 60GW under construction, and over $1.3T in signed lease contracts year-to-date. As massive power and real-estate infrastructure projects come online by 2027, the primary bottleneck in the AI supply chain is projected to pivot from grid power back to leading-edge accelerator silicon.

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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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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.

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Ollama launched US- and EU-hosted Ollama Cloud with strict zero data retention and zero training, adding immediate support for DeepSeek-V4.1-Flash-1017 via one-line launch commands for Claude Code and OpenCode alongside Ollama v0.40.6. Meanwhile, SemiAnalysis revealed that GLM-5.3 was built entirely by post-training the previous GLM-5.2 base model without pretraining from scratch, achieving a 'night and day difference' in cybersecurity and agentic capabilities.

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SemiAnalysis published the architectural breakdown behind the 1.3B tokens per dollar TCO record on AgentX: vLLM and Inferact utilized Prefill/Decode disaggregated serving at hyperscaler volume pricing to achieve 4x better cost-efficiency than an aggregated NVIDIA B300 configuration. Concurrently, open-source coding agent Cline reported that DeepSeek-V4.1-Flash-1017 beats GPT-5.6-Sol on Terminal-Bench at ~17x lower cost (+35% over prior V4.1-Flash) purely by scaling RL training across multiple coding agent harnesses.

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Following the release of DeepSeek-V4.1-Flash-1017, vLLM announced Day-0 serving optimization delivering 1.3B tokens per dollar TCO and 830K tok/s per GPU at 100 tok/s/user on SemiAnalysis AgentX (5x agentic throughput gain since Day 0), while OpenRouter launched live multi-provider routing starting at $0.10/M input and $0.50/M output tokens.

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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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