Extending Effective KV Cache Capacity for AI Inference

Modern AI inference systems increasingly encounter a memory wall before they reach a compute limit. As context windows grow and concurrency increases, KV cache demand can exceed available GPU memory, creating scheduler queues, increasing latency, and limiting throughput.

This whitepaper evaluates how a CXL-backed external KV memory tier on the Penguin Solutions MemoryAI™ KV Cache Server can expand effective KV cache capacity under sustained inference load. Using a representative enterprise RAG workload, the benchmark shows how preserving and restoring KV cache data can reduce latency, increase throughput, and improve GPU efficiency.

Readers will learn:

• How to identify the signs of memory-bound inference
• Which workload characteristics are most likely to benefit from expanded KV capacity
• How KV preservation and restore workflows work
• How external KV memory extends effective KV capacity

Compared to a baseline configuration using GPU-resident KV cache only, the report measured:

• 17.2× lower TTFT for interactive workloads
• Up to 2.43× throughput gain for interactive chat and copilot workloads
• 1.71× throughput improvement for long-form generation
• ~2.85× token efficiency (tok/s/W) under long-form inference
• ~50% lower GPU power consumption while sustaining higher throughput

Download the whitepaper to see the methodology, benchmark results, and practical guidance for evaluating external KV memory in production AI environments.