chips & hardware: 37 curated resources
chips & hardware
37 resources · page 1 of 2
Apple MLX
by ml-explore
Apple MLX GitHub repo: array framework for machine learning on Apple silicon.
DeepGEMM
by DeepSeek
DeepSeek DeepGEMM GitHub repo: GPU matrix multiplication kernel library including FP8 GEMMs.
Etched
by Etched
Etched: company building transformer-specific AI inference chips, racks and software.
Huawei Ascend
by Huawei
Huawei Ascend community site: Ascend AI processors, CANN software stack, frameworks and developer tools.
KTransformers
by kvcache.ai
KTransformers GitHub repo: framework for heterogeneous CPU-GPU inference of large LLMs.
Kunlunxin (昆仑芯)
Kunlunxin site: Baidu-affiliated maker of AI accelerator chips (in Chinese).
Liger Kernel
by LinkedIn
LinkedIn Liger Kernel GitHub repo: Triton kernels for LLM training.
llm.c
by Andrej
llm.c GitHub repo (Andrej Karpathy): LLM training in plain C and CUDA.
NVIDIA VerilogEval
NVIDIA VerilogEval GitHub repo: benchmark for evaluating LLMs on generating Verilog hardware-description code.
OpenROAD
OpenROAD GitHub repo: open RTL-to-GDS chip design flow, also used in ML-for-EDA research.
Tenstorrent
by Tenstorrent
Tenstorrent: company building RISC-V based AI accelerators and computers.
ThunderKittens
by HazyResearch
HazyResearch ThunderKittens GitHub repo: tile primitives framework for writing fast GPU kernels.
GPU Puzzles (Sasha Rush)
by Sasha Rush
Sasha Rush's GPU Puzzles GitHub repo: interactive notebook exercises for learning CUDA programming.
GPU Glossary (Modal)
by Modal
Modal GPU Glossary: reference explaining GPU hardware and software terms from CUDA cores to memory hierarchy.
AMD ROCm
by AMD
AMD ROCm developer hub with documentation, training and tools for GPU-accelerated computing on AMD hardware.
Cerebras blog
by Cerebras
Cerebras blog on wafer-scale chips, fast inference, model performance and developer tooling.
Fireworks blog
by Fireworks AI
Fireworks AI blog on inference performance, fine-tuning and model releases on its serving platform.
Google TPU docs
by Google Cloud
Google Cloud TPU documentation for running machine-learning training and inference on Tensor Processing Units.
GPU MODE
GPU MODE site: community for learning GPU programming through lectures, hackathons, kernel competitions and working groups.
GPU MODE lectures
by GPU MODE
GPU MODE lectures GitHub repo: slides and code from community lectures on GPU programming and CUDA kernels.
GPU MODE resource stream
by GPU MODE
GPU MODE resource-stream GitHub repo: curated links to CUDA and GPU programming books, papers, posts and videos.
Groq blog
by Groq
Groq blog with posts on its LPU inference hardware, cloud API and supported models.
Making Deep Learning Go Brrrr From First Principles (Horace He)
by Horace He
Horace He's Making Deep Learning Go Brrrr: first-principles guide to whether models are compute-, memory- or overhead-bound.
LM Studio blog
by LM Studio
LM Studio blog with release notes and technical posts on running LLMs locally.
Modal blog
by Modal
Modal blog on serverless GPU computing, infrastructure engineering and running AI workloads.
Ollama blog
by Ollama
Ollama blog with release notes, supported model announcements and guides for running models locally.
SGLang documentation
SGLang documentation: serving framework for large language and multimodal models.
Together AI blog
by Together AI
Together AI blog with product launches, research and technical guides on inference, fine-tuning and GPU clusters.
Triton Puzzles
by GPU MODE
Triton Puzzles GitHub repo: interactive exercises for learning to write GPU kernels in Triton.
Triton tutorials
Triton tutorials: ordered examples from vector addition to fused softmax, matmul and fused attention GPU kernels.
Unsloth blog
by Unsloth AI
Unsloth blog with guides and release notes for fast, memory-efficient LLM fine-tuning.
A graph placement methodology for fast chip design
by Azalia Mirhoseini et al. (Google)
Nature paper (2021) by Mirhoseini et al. introducing the reinforcement learning chip floorplanning method later named AlphaChip.