A high-throughput and memory-efficient inference and serving engine for LLMs

SGLang is a high-performance serving framework for large language models and multimodal models.

TensorRT LLM provides users with an easy-to-use Python API to define Large Language Models (LLMs) and supports state-of-the-art optimizations to perform inference efficiently on NVIDIA GPUs. TensorRT LLM also contains components to create Python and C++ runtimes that orchestrate the inference execution in a performant way.

TokenSpeed is a speed-of-light LLM inference engine.

OpenLake is a high performance storage engine for efficient LLM inference and GPU Training

Parallax is a distributed model serving framework that lets you build your own AI cluster anywhere

cuDNN Frontend is NVIDIA's modern, open-source entry point to the cuDNN library and a growing collection of high-performance open-source kernels.

A Python DSL to write Nvidia PTX for Hopper and Blackwell in JAX and PyTorch

Fully uncensored, capability-enhanced abliteration of Qwen3.6-27B. NVFP4 + z-lab DFlash speculative decoding (n=12) on the unified ghcr.io/aeon-7/aeon-vllm-ultimate:latest container, tuned for long-context draft acceptance on DGX Spark. 6 HF variants (BF16/NVFP4/MTP/MTP-XS), docker-compose, and QuickStart.

QuTLASS: CUTLASS-Powered Quantized BLAS for Deep Learning

One-command vLLM installation for NVIDIA DGX Spark with Blackwell GB10 GPUs (sm_121 architecture)

GLM-5.2-NVFP4-REAP-469B serving on SM120 (4× RTX PRO 6000 Blackwell) — one-command vLLM launch recipe, 250K context, DeepSeek Sparse Attention + MTP speculative decode

Pre-built wheels for llama-cpp-python across platforms and CUDA versions

Practical local LLM recipes and benchmarks for RTX 5060 Ti setups

DFlash vLLM for DGX Spark — Plug & Play Block-Diffusion Speculative Decoding

Prebuilt DeepSpeed wheels for Windows with NVIDIA GPU support. Supports GTX 10 - RTX 50 series. Compiled with pytorch 2.7, 2.8 and cuda 12.8

Bleeding-edge ComfyUI for NVIDIA DGX Spark (GB10/Blackwell/sm_121a). CUDA 13 + SageAttention v3 (sm_121a) + NVFP4 + 14 custom-node packs + Flux 2 Dev / LTX 2.3 22B / ACE-Step v1.5 XL Turbo pre-bundled with abliterated text-encoder paths.

RTX 5090 & RTX 5060 Docker container with PyTorch + TensorFlow. First fully-tested Blackwell GPU support for ML/AI. CUDA 12.8, Python 3.11, Ubuntu 24.04. Works with RTX 50-series (5090/5080/5070/5060) and RTX 40-series.

GPU-accelerated WhisperX on NVIDIA Blackwell (SM_121) - DGX Spark compatible