Large Model Reasoning Acceleration Program
Key Technologies:Colossal-LLaMA offers the following low latency programs:
- Dynamic batch processing:pass (a bill or inspection etc)
continuous_batching=TrueEnabling request-level parallelism - KV Cache:start using
use_kv_cacheAvoid double counting, suitable for long text >128 tokens - Quantitative Reasoning:utilization
quant_mode='int8'Reduces 75% video memory requirements
Deployment Architecture:
- 7B models recommend 2GPU tensor parallelism
- 13B+ model combinations using pipeline parallelism (1 GPU per stage)
- utilization
colossalai.inferencemodular packaging service
Performance Metrics:Reasoning speeds of <100ms/token can be achieved with proper configuration (A100 measured). This can be achieved by--profileParameters to generate flame maps to localize bottlenecks.
This answer comes from the articleColossalAI: Providing Efficient Large-Scale AI Model Training SolutionsThe
May not be reproduced without permission:AI productivity tools " How to Optimize Distributed Reasoning Latency for LLaMA-like Large Language Models?
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