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