Search “Reasoning”

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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Ant inclusionAI Open-Sources Ming-3.0-asr-tiny: Supports Chinese-English Transcription, Dialect Recognition, and Context ASR

Ant inclusionAI has open-sourced Ming-3.0-asr-tiny on GitHub, combining the Ling-tiny-v3 language model with an audio encoder to support Chinese-English transcription, dialect recognition, and Context ASR. Dialect evaluation covers 15 test sets including Hunan, Minnan, Cantonese, and Sichuan-Chongqing; Context ASR can recognize proper nouns via hotwords in prompts or preceding dialogue. The inference stack uses Transformers 5.12.1, fla-core 0.5.2, and PyTorch 2.11.0, and provides an SGLang accelerated deployment example.

Ant Group's inclusionAI publishes the sglang_ling_v3 repository on GitHub

Ant Group's inclusionAI launches the sglang_ling_v3 repository on GitHub. Its contents are based on the open-source inference framework SGLang, which targets large language models, vision-language models, and diffusion models, and is optimized for agent workloads, large-scale serving, and RL rollout. The repository README shows that SGLang supports GPU, TPU, NPU, CPU, and Apple Silicon, and includes the built-in image and video generation engine SGLang Diffusion.

Ant Group's inclusionAI open-sources the Ming-3.0-audio-flash native audio model

Ant Group's inclusionAI has open-sourced the Ming-3.0-audio-flash native audio model on GitHub. It uses a 126B-parameter MoE architecture with 6.5B activated parameters, and there is also a Ming-3.0-audio-tiny version with 9.3B parameters and 2.7B activated parameters. The models cover Chinese and English recognition across multiple dialects and accents, long audio understanding, audio reasoning, and end-to-end speech dialogue. They support three inference methods: vLLM-Omni, SGLang, and Transformers, and are released under the MIT license.

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Amazon SageMaker JumpStart 2026 Progress Review

From January to September 2026, Amazon SageMaker JumpStart added more than 100 models from 30 providers, of which Alibaba (Qwen) contributed 29, NVIDIA 17, Mistral 7, Google 6 Gemma 4 variants, IBM 4, and Cohere 4. The new models cover multimodal, speech, image, and video generation, and introduce quantized and compact models such as GLM-5.2 FP8 and JetBrains Mellum2-12B to reduce inference costs.

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.

HardenedLinux Releases SonicBoom: An AI Runtime Framework That Says Goodbye to Manual Tuning

The HardenedLinux community-led AI runtime framework SonicBoom has been released under the GPLv3 license, offering a language-agnostic C API and currently officially supporting GNU Guile. It has built-in LLVM MLIR to perform compile-time optimization of computation graphs and directly reuses existing llama.cpp kernels; with kernel optimizations unchanged, it boosts raw performance by 20% on an RTX 3050 8G. v0.0.3 currently supports only CPU and CUDA; the community edition provides only single-node inference, with distributed inference planned for the enterprise edition.

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Interview with 0G Co-founder and CEO Michael Heinrich: Building a Decentralized Trust Layer for AI

In the interview, 0G Co-founder and CEO Michael Heinrich explained that 0G is building a decentralized AI infrastructure stack that integrates verifiable computing, encrypted decentralized storage, private AI inference, and an AI-native EVM-compatible Layer 1 blockchain into a trust layer for AI. He noted that compute has become a strategic resource as important as capital and energy; the four hyperscale cloud providers' combined AI capital expenditure in 2025 exceeded $500 billion and is expected to double this year, yet compute itself still lacks a transparent market and independent verification.

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