Model Training for advanced learners
93 resources · page 2 of 3
NVIDIA Dynamo
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ai-dynamo Dynamo GitHub repo: NVIDIA's datacenter-scale distributed, disaggregated inference serving framework.
NVIDIA NeMo Speech
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NVIDIA NeMo Speech GitHub repo: framework for speech recognition and TTS model training and inference.
NVIDIA/TensorRT-LLM
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NVIDIA TensorRT-LLM GitHub repo: Python API and kernels for optimised LLM inference on NVIDIA GPUs.
NVIDIA VerilogEval
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NVIDIA VerilogEval GitHub repo: benchmark for evaluating LLMs on generating Verilog hardware-description code.
Ollama
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Ollama GitHub repo: runtime for running open LLMs locally, with API and cloud options.
OLMo training and inference
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Ai2 OLMo GitHub repo: modelling, training, evaluation and inference code for OLMo open language models.
OpenROAD
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OpenROAD GitHub repo: open RTL-to-GDS chip design flow, also used in ML-for-EDA research.
SageAttention
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Tsinghua SageAttention GitHub repo: quantised low-bit attention kernels for faster inference.
onediff
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SiliconFlow onediff GitHub repo: acceleration library for diffusion model inference.
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.
TileKernels
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DeepSeek TileKernels GitHub repo: library of GPU kernels written in TileLang.
Together cookbook
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Together cookbook GitHub repo: notebooks with recipes for building with Together AI models.
torchtitan
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PyTorch torchtitan GitHub repo: PyTorch-native platform for large-scale generative model pretraining.
fairseq: NLLB branch
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Meta fairseq nllb branch on GitHub: code for the No Language Left Behind translation models.
Triton-distributed
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ByteDance Seed Triton-distributed GitHub repo: distributed compiler and parallel kernels overlapping compute and communication.
TurboDiffusion (thu-ml)
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Tsinghua TurboDiffusion GitHub repo: acceleration framework for video diffusion models.
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.
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.
vLLM documentation
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vLLM documentation: open-source engine for high-throughput LLM inference and serving.
PyTorch FSDP tutorial
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PyTorch tutorial on Fully Sharded Data Parallel (FSDP2) for training large models across GPUs.
SGLang documentation
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SGLang documentation: serving framework for large language and multimodal models.
Triton tutorials
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Triton tutorials: ordered examples from vector addition to fused softmax, matmul and fused attention GPU kernels.
ruGPT training and fine-tuning examples
Inspect Russian-model fine-tuning and pretraining scripts, with explicit warnings about the legacy environment.
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.
Agentic inference optimization (Baseten)
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Baseten post on optimising inference engines for agent workloads, reporting decode-speed and latency gains over vLLM.
Baseten documentation
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Baseten documentation for running hosted models, deploying custom models and training on GPU infrastructure.
BentoML documentation
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BentoML documentation: open-source inference platform for packaging, deploying and scaling AI models.
Code execution with MCP
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Anthropic post on code execution with MCP: agents write code to call tools, reducing token use and context load.