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Tenstorrent raises $1.5B at a $6.2B valuation to expand open-weight AI chips

AI chip startup Tenstorrent has completed a $1.5 billion funding round at a valuation of $6.2 billion, more than double its $2.6 billion valuation at the end of 2024. The round was led by Trace Capital and Innovation X, with participation from Cadence Ventures, Eclipse, Fidelity, SanDisk, and T. Rowe Price. CEO Jim Keller said the company decided to remain independent and continue raising funds after demonstrating that its Galaxy servers can run DeepSeek models, and that the new capital will be used to expand chips for open-weight model inference and video generation.

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Turing Award winner Joseph Sifakis calls for establishing global AI regulatory standards

Joseph Sifakis, winner of the 2007 Turing Award, called for establishing global standards and regulatory standards in the field of AI at the "2026 Sustainable Global Leaders Conference," and said this is a shared responsibility of humanity. Citing ChatGPT and DeepSeek scoring higher than humans on medical exams as an example, he pointed out that large models do not understand social norms and value systems and therefore cannot replace doctors. He also warned that large companies may use regulatory gaps to shirk responsibility and seek commercial gain.

Young people today are paying to "hand-craft" AI personas

AI role-play prompts have spawned a niche, clearly priced business on social platforms. Custom AI personas generally cost 5–8 yuan, single-character prompts usually stay within 10 yuan per 1,000 characters, delivery takes 2–3 days, and rush orders or buyouts cost double. Users try models such as DeepSeek, Doubao, Gemini, and ChatGPT, and some keep paying because they find DeepSeek "the most human-feeling." The business is still at a low price point, popular settings are easily copied, and disputes remain over ownership of settings and the boundaries of originality.

Memory Tensor Open-Sources MemT 1.0 Series of Memory-Native Models

Memory Tensor has open-sourced the MemT 1.0 series of memory-native models, comprising two variants: Nano with 36B parameters and 3B activated, and Flash with 288B parameters and 17B activated, built on the Qwen3.6-35B-A3B and DeepSeek-V4-Flash bases, respectively. The models add a memory branch parallel to attention within selected Transformer modules, encoding experience into an updatable matrix state that participates in subsequent generation. Across 500 SWE-bench Verified tasks, Nano achieved an average success rate of 69.73% at key checkpoints, versus a no-experience baseline of 66.07%; under a 128K long history, end-to-end inference speed is approximately 1.86 times that of the full-context approach, and the persistent state footprint per trajectory is one sixty-sixth that of the full-context KV Cache.

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prime-rl 0.10.0 Released: Faster Training, New RL Loss IPO, and a Unified Evaluation Stack

Prime Intellect has released prime-rl v0.10.0 and verifiers v0.4.0, adding the RL algorithm IPO and setting it as the default, replacing the previous DPPO-based default loss. In tests comparing Qwen3-235B-A22B SFT against torchtitan, prime-rl achieved a 1.15× speedup (3732 vs 3253 tokens/s/GPU), and the MFU of full GLM-5.3 SFT rose from 22.3% to 40.1%. The evaluation CLI has moved from verifiers into prime-rl, with added support for models such as DeepSeek V4 Flash, Qwen3.8 Flash Next, and Nemotron-H.

Artificial Analysis launches HealthBench and HealthBench…

Artificial Analysis has launched leaderboards for OpenAI's two health dialogue benchmarks, HealthBench and HealthBench Professional; the former contains 5,000 conversations with general users and health workers, while the latter consists of 525 conversations all aimed at clinicians. On HealthBench Professional, GPT-6 Astra (max) ranks first with a length-adjusted score of 66.0%, GPT-6.1 Sol (max) is at 65.1%, and Claude Sonnet 5.5 (max with fallback) is at 63.0%. On HealthBench, Claude Sonnet 5.5 (max with fallback) leads at 62.1%, while DeepSeek V4.1 Flash (max) is at 60.9%; Claude Opus 5.5 and Claude Fable 5.1 average about 5,900 characters per answer and lose more than 11 points after length adjustment.

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MiniMax M3.1 Flash released: output 200+TPS, price halved

MiniMax released M3.1 Flash, a lightweight model positioned for Coding/Agent optimization, with a 1 million token context, output speed reaching 200+TPS in everyday scenarios, with P50 stable at 170+TPS. Its input price is 1 yuan per million tokens, output 4 yuan per million tokens, and cache read 0.1 yuan per million tokens, roughly half of DeepSeek V4.1 Flash-1017's peak-hour price. On Automation-Bench v1.0.6 it scored 62.2 points, surpassing DeepSeek's 60.5 points; on DeepSWE v1.1 it scored 73.7 points, lower than DeepSeek's 77.0 points.

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Hugging Face shared training compute rises from $40 million to about $1 billion

Thomas Wolf, citing his own post, said that the value of training compute shared on the Hugging Face Hub by Meta, NVIDIA, Alibaba, DeepSeek, Moonshot, Mistral and about 30 other labs has approached $1 billion, compared with less than $40 million at the end of 2023. He called this a conservative lower bound: about half comes from official compute figures, the rest is estimated, and it does not yet include the roughly 900,000 community fine-tunes.

The training compute shared on Hugging Face is worth nearly $1 billion

Thomas Wolf estimates that the value of training compute shared on the Hugging Face Hub by about 30-plus labs, including Meta, NVIDIA, Alibaba, DeepSeek, Moonshot, and Mistral, is approaching $1 billion, and it can be downloaded for free; at the end of 2023, that figure was still less than $40 million. He calls this number a lower bound: about half comes from official compute data, the rest is reasonable inference, and it does not yet include the roughly 900,000 community fine-tuned models.