Ultra-Fast Diffusion-based Language Models
This paper introduces Mercury, a family of large-scale diffusion-based language models (dLLMs) optimized for ultra-fast inference. Unlike standard autoregressive LLMs, Mercury models generate multiple tokens in parallel via a coarse-to-fine refinement process. This approach enables significantly higher throughput without sacrificing output quality. The initial release focuses on code generation, with Mercury Coder Mini and Small models achieving up to 1109 and 737 tokens/sec, respectively, on NVIDIA H100s, outperforming speed-optimized frontier models by up to 10× while matching or exceeding their quality.
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Mercury uses a Transformer-based architecture adapted for diffusion-based generation, enabling it to retain compatibility with existing LLM infrastructure.
On benchmarks such as HumanEval, MBPP, and MultiPL-E, the Mercury Coder models perform competitively with top proprietary models like Claude 3.5 Haiku and Gemini 2.0 Flash Lite, while being drastically faster.
Mercury achieves state-of-the-art results on fill-in-the-middle (FIM) code completion tasks, outperforming all evaluated models, including Codestral 2501 and GPT-4o Mini.
Human evaluations on Copilot Arena show Mercury Coder Mini is tied for second in Elo score and is the fastest model with just 25ms latency.