Accelerating AI inference with IBM Storage Scale

Accelerating AI inference with IBM Storage Scale

Modern AI inference, particularly with Large Language Models (LLMs), is constrained not just by GPUs, but also by network and storage infrastructure. IBM Storage Scale addresses this bottleneck by providing a persistent, high-performance tier for storing key (K) and value (V) tensors, crucial intermediate data in LLM processing.

Why This Matters

Current LLM inference demands are exceeding the capacity of GPU memory for KV caching, leading to redundant computation and increased latency. Without efficient KV cache management, LLMs struggle to deliver interactive response times, increasing costs and limiting scalability. The size of KV cache for a 128K input token Llama3-70B model is approximately 40GB, quickly overwhelming GPU resources.

Key Insights

• KV Cache Size: A Llama3-70B model with 128K input tokens generates a 40GB KV cache.
• llm-d & vLLM: Software frameworks designed to optimize resource management for LLM inference.
• IBM Storage Scale: Offers a scalable storage solution with up to 100K+ nodes, 300 GB/s bandwidth, and sub-microsecond latency.

Working Example

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Practical Applications

• LLM-powered chatbots: Companies like those utilizing vLLM can leverage IBM Storage Scale to accelerate response times and reduce infrastructure costs.
• Pitfall: Relying solely on GPU or CPU RAM for KV caching limits scalability and increases latency as the model size and context window grow.

References:

• https://research.ibm.com/blog/accelerating-ai-inference-with-ibm-storage-scale?utm_medium=rss&utm_source=rss