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FuTransformer boosts Llama inference with FP8 and INT4 on Hopper GPUs

By PulseAugur Editorial · [1 sources] ·

Researchers have developed FuTransformer, a novel CUDA implementation for Llama-style inference on NVIDIA Hopper GPUs. This system optimizes both prefill and decode stages by employing FP8 precision for prefill and INT4 for decode, significantly boosting performance. FuTransformer achieves up to 1.48x faster prefill than existing PyTorch FP8 implementations and offers INT4 decode speeds 1.3-2.4x faster than comparable methods, with minimal impact on perplexity. AI

IMPACT Enhances inference speed for Llama-style models on specific hardware, potentially reducing latency and computational costs.

RANK_REASON Research paper detailing a new implementation for LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

FuTransformer boosts Llama inference with FP8 and INT4 on Hopper GPUs

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Research paper detailing a new implementation for LLM inference. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.AI TIER_1 English(EN) · Wei Wang ·

    FuTransformer: FP8 prefill and INT4 decode for Llama-style inference on Hopper

    arXiv:2610.23796v1 Announce Type: new Abstract: Transformer prefill reuses weights across tokens and is dominated by matrix multiplication and activation data movement; single-token decode has limited reuse and is dominated by weight and KV-cache reads. We present FUTRANSFORMER, …

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