
Meta Commits $21B More to CoreWeave, Total Hits $35B
Meta expands its CoreWeave partnership by $21 billion through December 2032, bringing total commitments to $35 billion and locking in early NVIDIA Vera Rubin deployments.
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Meta expands its CoreWeave partnership by $21 billion through December 2032, bringing total commitments to $35 billion and locking in early NVIDIA Vera Rubin deployments.

Intel's Arc Pro B70 launched on March 25 with 32GB GDDR6 and 367 TOPS for $949, undercutting NVIDIA's RTX Pro 4000 by $850. The hardware case is strong. The software story is not.

RightNow AI releases AutoKernel, an open-source MIT-licensed framework that runs an autonomous LLM agent loop overnight to produce optimized Triton kernels for any PyTorch model.

Meta's KernelEvolve AI agent autonomously generates and optimizes hardware kernels across NVIDIA, AMD, and MTIA chips, delivering over 60% inference gains in production.

AMD Instinct MI325X specs, benchmarks, and analysis. 256GB HBM3e at 6 TB/s, 2.6 PFLOPS FP8, CDNA3 architecture - the memory-capacity upgrade to the MI300X targeting large model inference.

Mistral AI secures $830M in debt financing from seven banks to build a 13,800-GPU Nvidia GB300 cluster near Paris, targeting 200MW of European compute by 2027.

Jensen Huang confirmed at GTC 2026 that NVIDIA has export licenses for multiple Chinese customers and is restarting H200 production, with 82,000 GPUs ready to ship after nearly a year of zero deliveries.

Tencent's 2025 results beat estimates, with the company spending $2.6B on AI last year and planning to at least double that in 2026 despite ongoing GPU supply constraints from US export controls.

NVIDIA opens GTC 2026 with the Vera Rubin platform - six co-designed chips delivering 50 PFLOPS of inference per GPU and 10x lower token cost than Blackwell.

AMD's flagship CDNA 4 AI GPU with 432 GB HBM4, 40 PFLOPS FP4, and 2nm chiplet design targeting H2 2026.

NVIDIA's Rubin-based rack system with 144 R200 GPUs, 3.6 ExaFLOPS FP4, 20 TB HBM4 - arriving H2 2026.

A Hugging Face survey of 16 open-source reinforcement learning libraries finds the entire ecosystem has converged on async disaggregated training to fix a single brutal bottleneck: GPU idle time during long rollouts.