BLOOM-176B Inference with DeepSpeed Inference and Accelerate — sub-1ms per-token on 8x80GB A100

AI Impact Summary

This CAPABILITY release demonstrates running BLOOM 176B inference at scale using DeepSpeed Inference and HuggingFace Accelerate, achieving sub-1 ms per-token throughput on an 8x80GB A100 configuration. Memory demands are the primary constraint (352 GB bf16 weights) and the article outlines viable paths including 8x80GB A100, 2x8x40GB A100, 2x8x48GB A6000, or 24x32GB V100, plus 8-bit quantization via BitsAndBytes to reduce memory at some latency cost. For deployment, teams can choose Tensor Parallelism with DeepSpeed-Inference or Accelerate-based offloading, while Deepspeed-ZeRO can enable parallel generate streams; the article provides concrete commands and scripts to reproduce benchmarks. Business consequence: achieving high-throughput BLOOM inference requires substantial GPU and interconnect resources; underprovisioned infrastructure will result in much slower generation times, limiting real-time or large-scale use cases.