Here is the Technical Intelligence Analyst report for 2026-03-10.

Executive Summary

  • Competitor Scale-Out: NVIDIA has announced a massive strategic partnership and investment in Thinking Machines Lab (led by Mira Murati), committing to deploy at least 1 gigawatt of power for its next-generation Vera Rubin architecture.
  • Ecosystem Lock-in: The collaboration includes co-designing training and serving systems specifically for NVIDIA architectures, further solidifying NVIDIA’s grip on next-generation frontier AI development.

🤼‍♂️ Market & Competitors

[2026-03-10] NVIDIA and Thinking Machines Lab Announce Long-Term Gigawatt-Scale Strategic Partnership

Source: NVIDIA Blog

Key takeaway relevant to AMD:

  • NVIDIA is securing unprecedented gigawatt-scale infrastructure deployments for its upcoming Vera Rubin architecture, establishing a massive footprint before AMD’s competing next-generation Instinct accelerators hit the market.
  • NVIDIA’s direct financial investment and co-design partnership with Thinking Machines Lab creates a highly optimized, vendor-locked ecosystem that AMD will struggle to penetrate for this specific frontier model developer.

Summary:

  • NVIDIA and Thinking Machines Lab have formed a multi-year strategic partnership to build out at least one gigawatt of AI infrastructure.
  • NVIDIA has made a significant financial investment in the lab to accelerate the development of customizable, collaborative frontier AI models.
  • The deployment will utilize NVIDIA’s next-generation Vera Rubin systems, with a target launch of early next year.

Details:

  • Infrastructure Scale: The partnership commits to a massive deployment of at least one gigawatt of next-generation NVIDIA Vera Rubin systems. This represents one of the largest single architectural commitments publicly announced.
  • Hardware Generation: Confirms that the deployment will rely on the “Vera Rubin” platform (NVIDIA’s successor to Blackwell), highlighting aggressive forward-purchasing and data center planning.
  • Timeline: Deployment of the Rubin-based systems is officially targeted for early next year (2027).
  • System Co-Design: The partnership goes beyond hardware procurement; it includes a joint effort to actively design both training and serving systems highly optimized specifically for NVIDIA architectures.
  • Workload Focus: The gigawatt cluster will be utilized to support Thinking Machines’ frontier model training, as well as platforms designed to deliver customizable AI at scale.
  • Leadership Context: Thinking Machines Lab is co-founded and led by Mira Murati (former OpenAI CTO), positioning the lab as a top-tier competitor in the frontier model space with significant, exclusive backing from NVIDIA.
  • Market Strategy: The initiative aims to broaden enterprise, research, and scientific community access to frontier and open models, ensuring these models are deeply optimized for the CUDA/Rubin software and hardware stack.

📈 GitHub Stats

Category Repository Total Stars 1-Day 7-Day 30-Day
AMD Ecosystem AMD-AGI/GEAK-agent 69 0 0 +8
AMD Ecosystem AMD-AGI/Primus 79 +1 +5 +6
AMD Ecosystem AMD-AGI/TraceLens 63 0 +3 +5
AMD Ecosystem ROCm/MAD 31 0 0 0
AMD Ecosystem ROCm/ROCm 6,235 +6 +23 +85
Compilers openxla/xla 4,059 +3 +30 +88
Compilers tile-ai/tilelang 5,348 +6 +48 +253
Compilers triton-lang/triton 18,605 +12 +71 +222
Google / JAX AI-Hypercomputer/JetStream 415 0 +1 +10
Google / JAX AI-Hypercomputer/maxtext 2,165 +1 +9 +31
Google / JAX jax-ml/jax 35,039 +7 +52 +226
HuggingFace huggingface/transformers 157,702 +95 +411 +1483
Inference Serving alibaba/rtp-llm 1,060 +2 +4 +19
Inference Serving efeslab/Atom 335 -1 -1 -1
Inference Serving llm-d/llm-d 2,592 +1 +35 +132
Inference Serving sgl-project/sglang 24,280 +17 +264 +863
Inference Serving vllm-project/vllm 72,723 +181 +951 +2936
Inference Serving xdit-project/xDiT 2,565 +2 +14 +38
NVIDIA NVIDIA/Megatron-LM 15,580 +24 +91 +422
NVIDIA NVIDIA/TransformerEngine 3,193 +3 +13 +44
NVIDIA NVIDIA/apex 8,928 0 +2 +17
Optimization deepseek-ai/DeepEP 9,036 +3 +22 +69
Optimization deepspeedai/DeepSpeed 41,784 +13 +68 +217
Optimization facebookresearch/xformers 10,363 +1 +8 +32
PyTorch & Meta meta-pytorch/monarch 987 0 +5 +27
PyTorch & Meta meta-pytorch/torchcomms 347 0 +3 +17
PyTorch & Meta meta-pytorch/torchforge 637 +2 +11 +23
PyTorch & Meta pytorch/FBGEMM 1,538 0 +3 +11
PyTorch & Meta pytorch/ao 2,727 +1 +15 +60
PyTorch & Meta pytorch/audio 2,835 0 +1 +13
PyTorch & Meta pytorch/pytorch 98,175 +102 +282 +934
PyTorch & Meta pytorch/torchtitan 5,121 +3 +17 +76
PyTorch & Meta pytorch/vision 17,555 +5 +14 +59
RL & Post-Training THUDM/slime 4,661 +22 +125 +952
RL & Post-Training radixark/miles 963 +2 +27 +112
RL & Post-Training volcengine/verl 19,794 +40 +239 +733