AI BriefingBLOOM Inference: DeepSpeed vs Accelerate
Key point
Compares the optimal hardware configuration and benchmarks for maximizing inference performance of the 176B-parameter BLOOM model using DeepSpeed and Accelerate.
Details
Analyzes the optimal hardware configuration and per-library performance for efficiently running inference on the 176B-parameter BLOOM model.
Hardware and Setup
- An 8x80GB A100 GPU configuration is the most efficient; if memory is insufficient, CPU/NVMe offloading or 8bit quantization can be used to compensate.
- When using quantization, memory usage is cut in half, but throughput decreases somewhat.
Benchmark Results
- Model loading speed: DeepSpeed-Inference is the fastest at around 1 minute, while Accelerate takes about 2 minutes.
- Inference throughput: DeepSpeed-Inference leverages Tensor Parallelism (TP) and custom CUDA kernels to achieve an overwhelming 0.69ms at a batch size of 128.
- Accelerate uses Pipeline Parallelism (PP), offering very high model compatibility and performance similar to DeepSpeed-ZeRO.
In conclusion, DeepSpeed-Inference is most advantageous when extreme inference performance is required, while Accelerate is suitable for those who prioritize general-purpose model applicability and ease of use.
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