328 summarised stories about Open-Weight Models, each linking back to the original source. Browse all topics →
TechCrunch 1 day ago 10
Tenstorrent, an inference-focused chip startup, raised $1.5 billion at a $6.2 billion valuation to scale its AI hardware. The round lifted its valuation from $2.6 billion in late 2024. The new funding will be used to scale chips tailored for serving open-weight AI models and video generation.
Trending Topics 1 day ago 47 ● 2 sources
Nvidia released Nemotron 3.5 Super VL, an open multimodal AI model, and introduced Nemotron 3.5 Bolt, a locally runnable laptop-focused model. Artificial Analysis reports Super VL scores 26 points on its 10-benchmark Intelligence Index and uses about 60% less text than the previous Nemotron 3 Super. Nvidia’s availability shifts toward faster, leaner open models for long-running corporate agent use, with improved API token pricing and laptop/edge deployment options via new quantized variants.
TechCrunch 1 day ago 15 ● 2 sources
Nvidia launched new open-weight models for always-on agent use in enterprises and for local execution on AI PCs. The Nemotron 3.5 Super VL is a 120-billion-parameter model aimed at enterprise agent workloads. Nvidia positions these models as enabling organizations to deploy and post-train agents on their own data centers (and run smaller models locally on its RTX Spark-based PCs), while also citing speed comparisons and naming customer-adopting companies.
International Business Times 2 days ago 37 ● 4 sources
Analysts say DeepSeek’s open models, led by V4-Pro, are challenging U.S. AI labs by showing strong coding results but lagging on several reasoning and agentic benchmarks. DeepSeek V4-Pro reached general availability on Aug. 13, 2026 and is priced at about $3.48 per million tokens versus about $25 for Claude’s top model. The market takeaway is that open-weight models and lower pricing raise pressure on premium closed-lab pricing, while hardware access, reliability, and Western trust issues could slow how fast DeepSeek closes performance gaps.
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21 October 2026
The day’s biggest throughline wasn’t new model announcements—it was the messy, expensive reality of getting AI to run at scale, profitably, and reliably. Google Cloud shipped llm-d 1.0, a Kubernetes-native distributed inference layer that routes requests to pooled vLLM or SGLang servers, while Google also unveiled TorchTPU, a PyTorch-focused path for developers trying to move off Nvidia GPUs (notably, the pitch leans on PyTorch’s dominance, cited as 85% of AI apps). Off the hardware side, Dutch startup Photon Bridge raised €10 million to build laser tech for optical links in large AI clusters, targeting first samples in 2027 and volume manufacturing in 2028, and framing the bottleneck as a manufacturing-scale problem: millions of lasers per year, delivered via multiplexing designed for higher yield.
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