Unlocking GPU Power: Mastering Batch Inference for LLMs
Batch inference is crucial for optimizing the performance of Large Language Models (LLMs) and other generative models in production. Running single requests can leave GPU resources underutilized, while batching boosts throughput significantly. Here’s what you need to know:
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Key Batching Methods:
- No Batching: Processes each request individually.
- Static Batching: Waits to fill a batch, increases latency.
- Dynamic Batching: Runs batches based on time or full capacity, improving latency.
- Continuous Batching: Processes requests token-by-token, maximizing GPU utilization.
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Choosing the Right Strategy:
- For LLMs, continuous batching is ideal.
- Dynamic batching works best for models with similar processing times.
Maximize your AI deployments by opting for the right batching approach. Dive deeper into the nuances and trade-offs in our full article.
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