KV Cache Explained for SREs: GPU Memory, Latency & Concurrency

Why KV cache exists

During autoregressive generation, an LLM produces tokens one at a time. Without a KV Cache, the model would repeatedly recompute attention information for tokens it already processed. The cache preserves key/value state so later decoding can reuse it.

Prompt tokens
   ↓
Compute attention state
   ↓
Store K/V in GPU memory
   ↓
Generate token 1
   ↓ reuse cache
Generate token 2
   ↓ reuse cache
Generate token 3 ...

The SRE trade-off

The cache improves generation efficiency but consumes VRAM. Longer contexts, larger batches and more concurrent sequences increase cache pressure. This is why a model can fit in GPU memory but the service can still hit GPU Out of Memory (GPU OOM) under real traffic.

What changes capacity

• Model architecture and number of layers.
• Context length already processed for active sequences.
• Number of concurrent requests/sequences.
• Precision and cache representation.
• Serving engine behavior such as paging or prefix reuse.

How it connects to latency

KV-cache pressure can reduce concurrency or trigger queueing, which then increases Time to First Token (TTFT). Serving engines use techniques such as Continuous Batching and Prefix Cache to improve utilization, but those also need telemetry and capacity tests.

What to put on an AI SRE dashboard

• GPU memory used/free.
• Active and queued sequences.
• Prompt/context length distribution.
• KV-cache utilization or block usage when exposed by the serving engine.
• TTFT, generation speed and rejection/OOM rates.