Thrilled to announce we're open-sourcing the CUDA Tile dialect and bytecode! github.com/NVIDIA/cuda-ti… What's included:     • CUDA Tile MLIR dialect     • Bytecode serialization/deserialization support     • MLIR Python bindings for programmatic IR construction     •

UC Berkeley's "Machine Learning" lecture notes PDF: people.eecs.berkeley.edu/~jrs/papers/ma…

Fascinating Google paper: just repeating your prompt 2 times can seriously boost LLM performance, sometimes pushing accuracy from 21% to 97% on certain search tasks. An LLM reads your prompt left to right, so early words get processed before the model has seen the later words

🚨 Anthropic dropped a FREE 33-page playbook revealing Claude's very own cheat code: The 'Skills' folder. Spend 30 minutes building it, and you’ll never have to explain your process again. Top-tier users don't just type commands, they build systems. Grab your free copy of

Anthropic CEO: “50% of all entry-level Lawyers, Consultants, and Finance Professionals will be completely wiped out within the next 1–5 years." grad students and junior hires are cooked.

Introducing TurboQuant: Our new compression algorithm that reduces LLM key-value cache memory by at least 6x and delivers up to 8x speedup, all with zero accuracy loss, redefining AI efficiency. Read the blog to learn how it achieves these results: goo.gle/4bsq2qI

Really exciting to see KV-cache compression getting attention. A similar bottleneck shows up beyond LLMs: for world models and autoregressive long-video generation, KV cache can quickly dominate memory and limit long-horizon consistency. Our recent work, Quant VideoGen,

Inside NVIDIA GPUs: Anatomy of high-performance matmul kernels One of the finest and in-depth posts that everyone MUST read. Amazing work by Aleksa Gordić (水平问题)!! aleksagordic.com/blog/matmul

NEW research from NVIDIA. Post-training agents with RL is powerful but expensive. Every parameter update needs full multi-turn rollouts with environment interactions, making end-to-end RL prohibitively costly for long-horizon agentic tasks. This research offers a practical

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Intel Arc Pro B70 is now Newegg’s No. 1 best seller in workstation graphics cards videocardz.com/newz/intel-arc…

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more people should be talking about this github.com/NVIDIA-NeMo/Da…

30 second explanation of the MemPalace by Milla Jovovich. By day she’s filming action movies, walking Miu Miu fashion shows, and being a mom. By night she’s coding. She’s the most creative, brilliant, and hilarious person I know. I’m honored to be working with her on this

Improve latency up to 1.68x with NVFP4 and MXFP8 using Diffusers and TorchAO on Blackwell across a suite of different models 🔥. Squeeze out maximum performance with recipes involving selective quantization and regional compilation. 🔗 Read our latest blog from Vasiliy Kuznetsov (Meta)

Open-source software never stops. It only accelerates. Dynamo, SGLang, TensorRT LLM, and vLLM are constantly optimized by a vast ecosystem of developers building on top of the NVIDIA platform. The result: your token output keeps improving and token cost keeps

⚡🔧 PyTorch inference optimization just got a lot simpler Introducing AITune — NVIDIA's new library that automatically finds the fastest inference backend for any PyTorch model. It covers TensorRT, Torch Inductor, TorchAO and more, benchmarks all of them on your model and

This is a masterclass in optimization. A deep dive into how cache locality, tiling, SIMD/AVX2, and memory management can turn a "simple" matrix transpose into a 71× speedup. Essential reading for anyone interested in high-performance computing.

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