Agentic data science with the Data Agent Kit
cloud.google.com·7h ago
Data Science
Towards Data Science·26d ago·5 min read
The KV Cache Tax: Why Inference Servers Run Out of Memory Before Compute
TL;DR
A VRAM budget formula for serving large language models (LLMs) reveals that inference servers often run out of memory due to the KV cache tax, not compute limitations. Three optimization strategies are proposed to mitigate out-of-memory (OOM) issues based on traffic patterns.
✦ Why It Matters
Evaluate your current memory allocation strategies for LLM inference to prevent OOM errors.
Key Takeaways
- Use PagedAttention to minimize KV cache memory waste.
- Implement prefix caching for shared tokens to improve throughput.
- Quantize KV cache only after validating quality on long-context tasks.
- Size deployments based on peak concurrency, not average load.
How It Works
PagedAttention allocates KV cache memory in fixed-size, non-contiguous blocks, allowing for dynamic memory usage based on actual request needs. This method reduces fragmentation and waste, enabling more efficient memory management.