Google open-sources TorchTPU, three cheap fast models, Anthropic's S-1 leaks

Google open-sources TorchTPU: TPUs become a native PyTorch device

At PyTorch Conference on 20 October, Google released TorchTPU, a native PyTorch backend for Cloud TPUs co-developed with Meta’s PyTorch team. Hugging Face shipped native TorchTPU support in Transformers, Diffusers and Kernels the same day.

• Usage: pip install torch_tpu, then torch.device("tpu"), with plain PyTorch tensors and no wrapper types.
  • Eager mode for debugging, including a new “DeferAndFuse” eager mode.
  • torch.compile via XLA for production; collectives through torch.distributed.
• PyTorch blog numbers:
  • ~450 ATen ops supported.
  • Custom kernels in JAX/Pallas or the Helion DSL.
  • Out-of-box execution validated on the 5,000 most-downloaded Hugging Face models (Qwen, DeepSeek, Gemma, Llama, Mistral, Whisper, CLIP, BERT, T5 and more).
• New engines and integrations: vllm-torchtpu (with Inferact) and sglang-torchtpu (with RadixArk), a TorchTitan fork for TPU training, and Ray, TorchRec, llm-d and VeRL integration. LiteRT was contributed to the PyTorch ecosystem.
• TPU agent suite: in preview on GitHub, coding-agent plug-ins that automate onboarding, diagnostics and inference tuning.
• Google says TorchTPU is already in production for some of its largest Cloud customers. It replaces the old torch_xla route.

Why it matters: This is Google’s answer to AWS TorchNeuron, and it’s more complete on day one (vLLM, SGLang, TorchTitan, HF). With both hyperscalers making their chips a one-line device swap, price per token on TPU vs GPU becomes a real procurement question for teams that never touched JAX.

Sources: Google Cloud blog · PyTorch: Introducing TorchTPU · vLLM and SGLang on TPU · TorchTitan on TPU · TechCrunch

MiniMax M3.1 Flash: near-DeepSeek intelligence at $0.60 per million output tokens

MiniMax released M3.1 Flash on 19 October, an agent- and coding-focused model it says runs nearly 3× faster than M3 and cuts agent usage costs by 70%. Independent tests confirm the jump, and also a very high hallucination rate.

• Price per million tokens:
  • Input: $0.15
  • Output: $0.60
  • Cached: $0.015
• 1M-token context; MiniMax claims 170–200+ tokens/s.
• Vendor benchmarks:
  • Automation-Bench: 62.2
  • BankerToolBench: 76.2
  • DeepSWE: 73.7
  • OSWorld 2.0: 61.7
  • CyberGym: 82.2
• Artificial Analysis Intelligence Index: 42 (M3: 29). Cost per index task ~$0.22 (M3: ~$0.51).
  • Peers: GLM-5.3 Flash 42, Gemini 3.8 Flash 41, DeepSeek V4.1 Flash-1017 45.
  • HLE: 48%; CritPt: 22% (4%).
• Output tokens for the index run: ~189M, vs ~324M for GLM-5.3 Flash and ~334M for Gemini 3.8 Flash.
• The catch: per VentureBeat, Artificial Analysis measured a 93% hallucination rate on AA-Omniscience; the model rarely admits it doesn’t know.
• Available via MiniMax API, M Plan and MiniMax Code; weights promised “at a later date”. Plotly’s data-analysis bench scored it 41/43, tying frontier models.

Why it matters: For high-volume agent loops where outputs get checked by tools or tests, this is now one of the cheapest competent options. For anything factual and unsupervised, the hallucination number is a reason to keep it behind verification.

Sources: MiniMax blog · VentureBeat · Plotly bench

Inception’s Mercury 3: a diffusion LLM at 1,000+ tokens/s

Inception Labs launched Mercury 3 on 19 October, a diffusion language model that drafts whole blocks of text and refines them in parallel. It claims to beat GPT-6 Luna and Claude Haiku 5.5 while running 4–8× faster.

• Speed and size: 1,068 tokens/s on Nvidia GPUs; 1M context (up from 260K in Mercury 2.5); over 40× the parameters of Mercury 1 (total not disclosed).
• Price per million tokens (input / output):
  • List: $0.20 / $0.70
  • Launch price, halved: $0.10 / $0.35
  • Cached input 90% off; 100M free tokens for new API keys.
• Access: OpenAI-compatible, on Inception’s API, OpenRouter, Baseten and Vercel AI Gateway. Mercury 2/2.5 close to new users; existing users keep access until 15 November.
• CEO Stefano Ermon told The Deep View swarms of Mercury 3 agents were used to improve the model itself, adding ~16% speed.
• First independent test (Bito, coding): the fastest model they’ve run.
  • Long agentic session: 5.8 min (Opus 5.5: 17.2 min)
  • One-shot coding: 76% (Haiku 5.5: 79%)
  • Long multi-turn sessions: 65% (Opus 5.5: 87%, Sonnet 5.5: 77%)

Why it matters: It’s the strongest evidence yet that text diffusion scales. Speed only helps generation, not reading the context, so the win is biggest for voice, chat and short tool calls, less so for long coding agents.

Sources: Inception blog · The Next Web · Bito coding benchmark

Cloudflare Clairic: an “extraction model” that returns typed values instead of tokens

Cloudflare previewed Clairic on Workers AI on 19 October, a model that reads input in one pass and returns typed values with confidence scores. Open weights are planned.

• Typed outputs: text spans, numbers, dates, durations, lists, estimates and forecasts.
• How it works: you send a state (text plus up to 4 images) and a schema of typed fields.
  • Each answer is a valid value of that type or null, with p_null and confidence.
  • Text answers are exact spans with character offsets, so it can’t misspell what it read.
• Estimates and forecasts: a median plus an 80% interval (p10–p90), optionally all 99 percentiles; forecasts up to 64 steps.
• Pricing and limits: @cf/cloudflare/clairic, $0.10 per million input tokens, 65,536-token context. Fields that don’t fit get split across parallel passes, so there’s no field limit.
• Cloudflare’s own caveats: one- and two-step arithmetic is reliable, long chains are not (extract inputs, do maths in code). Forecasts flatten toward the average several steps out.
• It complements Cloudflare’s earlier “decision model” Clef, which picks among predefined options.

Why it matters: A lot of production “LLM” work is really invoice totals, dates and amounts. A model that can’t hallucinate format and tells you when it’s unsure is a cleaner fit for routing to automation vs human review than a chat model with JSON mode.

Sources: Cloudflare blog · Workers AI changelog · Model docs

Leaked Anthropic S-1: hyperscalers now treat it as a rival, and compute is still short

According to an S-1 filing leaked to the WSJ (via TechCrunch), Anthropic struggles to secure enough capacity from AWS, Google and Microsoft. They increasingly see it as a competitor rather than a customer.

• Cloud spend: Anthropic believes it could spend more than $750B on cloud contracts, some still in negotiation.
• It is leasing its own data centres and using third-party operators it warns have a “limited operating history”.
• H1 2026 financials:
  • Revenue: $16.3B
  • Net loss: $80.7B
  • Two customers account for about a fifth of sales.
• The filing warns liability for harmful AI behaviour could exceed available insurance coverage.
• Context: this lands alongside the reported second ~$45B Nscale talks.
• Schmidt stake: Bloomberg reports Eric Schmidt owns about half as much of Anthropic as Dario Amodei, from a 2021 Series A at a $461M valuation.
  • At the top-end $2T IPO estimate, that could add up to $20B to his net worth.

Why it matters: Customer concentration, an enormous loss line and dependency on suppliers who also compete with it are the risks IPO investors will price. For users, it explains why Anthropic keeps signing with neoclouds and why capacity limits keep showing up.

Sources: TechCrunch: cloud partnerships · TechCrunch: Schmidt stake

SpaceX’s AI unit is now more than half its revenue

SpaceX reported $14.6B Q3 revenue, and its renamed “Super Intelligence” segment brought in $7.5B, now 52% of income, with $431M profit.

• The segment: Cursor, Grok, and its neocloud with 1.7 GW of compute.
• AI profit would have been higher without $1.2B one-time stock compensation tied to the Cursor acquisition.
• Company results:
  • Q3 net income: $4.4B (a $1B loss a year ago)
  • Nine-month result: still a $413M loss
• Capex: $19.6B in Q3, guided to $35–40B in Q4, for Colossus II in Memphis and Starship. Musk says he spends four days a week in Memphis.
• Musk argues power and cooling, not chips, are the constraint; SpaceX is casting turbine blades and vanes in-house to dodge 12–18 month waits.
• Outlook: Musk predicted Super Intelligence ARR of “$500 billion to $1 trillion” by end-2027. The CFO’s concrete guide is $33–37B total revenue for Q1 2027.

Why it matters: A neocloud plus a coding tool plus a model lab under one roof is now a profitable business line at a scale that rivals the labs. Treat the trillion-dollar ARR line as aspiration; the Q1 guide is the number to track.

Sources: TechCrunch

US orders federal agencies to rip out DeepSeek, including products built on it

The US ordered federal civilian agencies to stop buying DeepSeek services and remove them from government IT systems, citing data-access concerns under Chinese law. The order also covers third-party products built on DeepSeek technology, with exceptions on request.

• DHS flagged that US government users’ data could be accessible to Chinese authorities.
• It extends a December 2025 law that targeted DeepSeek in intelligence and national-security systems to all civilian agencies, for both new purchases and existing systems.
• China is reportedly weighing export restrictions on 23 AI-related technologies, including chip designs and some advanced models.
• Background: the US has accused DeepSeek and five other Chinese labs (including Alibaba and Moonshot) of large-scale distillation of American models.

China’s foreign ministry objected to “overstretching the concept of national security”.

Why it matters: The “built using DeepSeek technology” clause reaches open-weight fine-tunes and vendors who quietly ship DeepSeek-based models. Federal suppliers will need to audit model lineage.

Sources: TechCrunch

OpenAI buys Orchestra Bio to put agents into drug-development operations

OpenAI acquired Orchestra Bio on 20 October, a four-year-old startup whose agents help biopharma teams run drug development. Terms weren’t disclosed.

• What the agents do: monitor studies, track timelines and finances, and resolve clinical-trial scheduling issues.
• The team and tech join Rosalind, OpenAI’s life-sciences models, and Rosalind Workbench, its harness for planning experiments and analysing results.
• OpenAI’s example: a failed manufacturing batch threatens a trial, and the agents work out which activities and timelines are hit and weigh responses. People make the final call.
• Company: Orchestra had raised about $20M (a16z, 8VC, Insight, Atria); CEO Ashoka Rajendra founded it in 2022.
• Moderna and Novo Nordisk already work with OpenAI on Rosalind.

Why it matters: The labs are moving from “model for scientists” to owning the operational software around the science. For vertical AI startups in pharma, this is the absorb-or-compete pattern in action.

Sources: OpenAI · Axios · SiliconANGLE

Eka hits $1.45B on a “vision-force-action” model; Dream Labs opens a zero-shot robot API

Eka raised a $150M Series A (a16z, F-Prime) for a manipulation stack trained by simulation RL around force sensing. Dream Labs came out of stealth with a $90M seed and a public API for Dream-1.

• Eka: founded by MIT’s Pulkit Agrawal and ex-DeepMind Tuomas Haarnoja; a force-sensitive gripper plus a vision-force-action model.
  • Picks ~1,500 objects up to 1 kg per hour (a human: ~600), with >95% reliability.
  • Adapts to new objects without retraining.
  • Trained in a physics simulator that generates “thousands of hours of robot experience in minutes”.
• Outside roboticists quoted by TechCrunch called it a “wow factor” and compared it to the legged-locomotion breakthrough.
• Dream Labs (former Nvidia researchers): Dream-1 is a “World Action Model” that takes camera images, robot state and a task and returns actions.
  • Zero-shot on unseen bimanual robots with parallel grippers; a few hundred demos adapt it to new bodies.
  • API pricing: $3/hour (Dream-1), $2/hour (Dream-1-flash), plus a playground with a real robot.

Why it matters: Robot brains are becoming an API you rent by the hour. Eka’s results back the critics who said vision-only models can’t solve the “last millimetre” of grasping.

Sources: TechCrunch: Eka · Dream Labs: Dream-1 · Forbes

ElevenLabs Realtime v1: one speech-to-speech model instead of a four-part voice stack

ElevenLabs launched Eleven Realtime v1 Mini and v1 Max on 20 October, end-to-end speech-to-speech models that fold ASR, turn-taking, the LLM and TTS into one system for voice agents.

• Mini targets low-cost natural conversation; Max targets reasoning, customer support and multi-step tool use.
• 90+ languages and 17,000+ voices.
• Endpoint: OpenAI-compatible WebSocket (wss://api.elevenlabs.io/v1/convai/realtime), models eleven_realtime_v1_mini / _max.
• Pricing: from $0.08 per connected-audio minute plus LLM tokens, with higher token rates on Max. Available in ElevenAgents and the realtime API.

Why it matters: Wiring and tuning separate ASR, LLM and TTS components is where most voice-agent latency and awkward turn-taking comes from. An OpenAI-compatible endpoint also makes ElevenLabs a drop-in alternative to OpenAI’s realtime API.

Sources: ElevenLabs blog · The Decoder