NVlabs/Fast-dLLM

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1090 stars · 145 forks observed · 2026-09-23

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

NVIDIA's official implementation of Fast-dLLM, a family of training-free and fine-tuning-based acceleration techniques for diffusion-based large language models, vision-language models, and vision-language-action models. It enables KV cache reuse and confidence-aware parallel decoding to achieve up to 27.6x throughput improvement over standard diffusion LLM inference with minimal accuracy loss.

Use cases

• accelerate diffusion llm inference
• speed up llada and dream text generation
• enable kv cache for bidirectional diffusion models
• parallel decode multiple tokens in diffusion language models
• convert autoregressive vlms to diffusion vlms
• efficient end-to-end autonomous driving with diffusion models

When to choose

• you are running diffusion-based LLMs like LLaDA or Dream and need faster inference
• you want to reproduce ICLR 2026 research on diffusion LLM acceleration
• you need block-diffusion VLMs for multimodal or autonomous driving workloads
• you have NVIDIA GPUs and want training-free throughput gains

When to avoid

• you use standard autoregressive LLMs where conventional inference is already fast
• you need a production serving stack rather than research code
• you lack GPU hardware or work outside the supported model backbones

Facets

library · maturity active

llm-inference machine-learning deep-learning gpu-computing large-language-models artificial-intelligence deep-learning gpu-computing autonomous-vehicles computer-vision python diffusion-llm kv-cache parallel-decoding block-diffusion speculative-decoding inference-acceleration vision-language-model research-code nvidia iclr-2026 gpu linux

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