Infrastructure, chips & efficiency
Infrastructure, chips & efficiency
- chips & hardware (37)
- efficiency & quantization (8)
- inference & serving (58)
- local/on-device (6)
- training systems (13)
122 resources · page 2 of 4
NVIDIA Dynamo
by Dynamo
ai-dynamo Dynamo GitHub repo: NVIDIA's datacenter-scale distributed, disaggregated inference serving framework.
NVIDIA NeMo Speech
by NVIDIA-NeMo
NVIDIA NeMo Speech GitHub repo: framework for speech recognition and TTS model training and inference.
NVIDIA/TensorRT-LLM
NVIDIA TensorRT-LLM GitHub repo: Python API and kernels for optimised LLM inference on NVIDIA GPUs.
NVIDIA VerilogEval
NVIDIA VerilogEval GitHub repo: benchmark for evaluating LLMs on generating Verilog hardware-description code.
Ollama
by Ollama
Ollama GitHub repo: runtime for running open LLMs locally, with API and cloud options.
OLMo training and inference
by Ai2
Ai2 OLMo GitHub repo: modelling, training, evaluation and inference code for OLMo open language models.
OpenROAD
OpenROAD GitHub repo: open RTL-to-GDS chip design flow, also used in ML-for-EDA research.
SageAttention
by TSAIL group
Tsinghua SageAttention GitHub repo: quantised low-bit attention kernels for faster inference.
onediff
by SiliconFlow
SiliconFlow onediff GitHub repo: acceleration library for diffusion model inference.
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.
TileKernels
by DeepSeek
DeepSeek TileKernels GitHub repo: library of GPU kernels written in TileLang.
Together cookbook
by Together
Together cookbook GitHub repo: notebooks with recipes for building with Together AI models.
torchtitan
by pytorch
PyTorch torchtitan GitHub repo: PyTorch-native platform for large-scale generative model pretraining.
fairseq: NLLB branch
Meta fairseq nllb branch on GitHub: code for the No Language Left Behind translation models.
Triton-distributed
ByteDance Seed Triton-distributed GitHub repo: distributed compiler and parallel kernels overlapping compute and communication.
TurboDiffusion (thu-ml)
by TSAIL group
Tsinghua TurboDiffusion GitHub repo: acceleration framework for video diffusion models.
Alibaba Cloud Model Studio: Prompt engineering guide
Alibaba Cloud Model Studio prompt engineering guide in Chinese with techniques for designing and optimising prompts.
Cloud.ru Foundation Models documentation
by Cloud.ru
Cloud.ru Foundation Models documentation: Sber subsidiary's unified API for hosted language models (in Russian).
fal Docs (fal.ai/docs)
by fal
fal documentation for deploying and calling generative media models on fal's serving platform.
GPU Puzzles (Sasha Rush)
by Sasha Rush
Sasha Rush's GPU Puzzles GitHub repo: interactive notebook exercises for learning CUDA programming.
GroqDocs: Prompt Basics
by Groq
Groq prompting guide covering roles, instructions, context, input and output formats, and sampling parameters.
LM Studio docs
by LM Studio
LM Studio app documentation: running Llama, DeepSeek, Qwen and other LLMs locally on a desktop.
GPU Glossary (Modal)
by Modal
Modal GPU Glossary: reference explaining GPU hardware and software terms from CUDA cores to memory hierarchy.
GigaChat docs: Prompt engineering basics
by Sber
Sber GigaChat prompt engineering basics in Russian: how to write effective prompts for GigaChat.
SiliconFlow docs: Platform introduction
by SiliconFlow
SiliconFlow platform introduction in Chinese: inference service offering API access to many open models.
Volcano Engine Ark (方舟) docs
Volcano Engine Ark documentation entry: ByteDance's model platform docs, redirecting to docs.volcengine.com.
Alibaba Cloud Model Studio: Create a knowledge base
Alibaba Cloud Model Studio documentation on creating knowledge bases for RAG, with parsing and chunking settings (in Chinese).
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.
Cloudflare Workers AI
by Cloudflare
Cloudflare Workers AI documentation for running ML models on serverless GPUs across Cloudflare's network.
Fireworks blog
by Fireworks AI
Fireworks AI blog on inference performance, fine-tuning and model releases on its serving platform.