Tooling12d ago
Google and Meta open-source TorchTPU: native PyTorch on TPUs, where porting is as simple as .to("cuda") to .to("tpu"), with native TPU backends for vLLM and SGLang
At PyTorch Conference 2026 on Oct 20, Google open-sourced TorchTPU on GitHub, co-developed with Meta's PyTorch team, making TPUs a native PyTorch device alongside GPUs and CPUs: eager mode and torch.compile, about 450 ATen ops, and torch.distributed collectives. Google also released vLLM-TorchTPU and SGLang-TorchTPU engines, integrations with TorchTitan, Ray, VeRL and Hugging Face Transformers v5 (device_map="tpu"), and a preview TPU agent suite for coding assistants such as Claude Code and Antigravity. AWS shipped the comparable TorchNeuron a day earlier.
Release4d ago
Alphabet Q3 2026: revenue up 25% to $127.6B, Google Cloud up 103%, first-party model APIs near 32B tokens per minute
After the US market close on Oct 28, Alphabet reported Q3 2026 results: revenue rose 25% to $127.6B, and Google Cloud revenue more than doubled, up 103% to $30.7B, which the company attributes to enterprise AI infrastructure, AI solutions and TPU system sales. In the release and on X, Sundar Pichai said first-party model APIs now process nearly 32B tokens per minute, more than four times a year ago, and that the Gemini app has reached one billion users. The cost: Q3 capital expenditures of $51.4B and free cash flow of negative $9.1B.
Infrastructure15d ago
SemiAnalysis Breaks Down 1.3B Tokens/$ AgentX Record: vLLM + Inferact Disaggregated Serving Beats Aggregated B300 by 4x, While Cline Highlights Multi-Harness RL
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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