KV Cache
KV cache improves ongoing generation speed by reusing prior computation instead of recalculating everything from scratch.
Key terms
Without cache
Each generation step revisits prior context. Compute work repeats.
With cache
Earlier states are cached and reused. Only new work is computed.
Compute saved
Prior states reused
Memory cost
Cache grows with context
First token
Largely unchanged
Business impact
How this shapes cost, speed, risk, and control.
Ongoing latency
Materially lower
Especially visible in chat and long-form generation.
Throughput
Higher
More requests per GPU when long contexts are involved.
Memory use
Higher
GPU memory is the limiting resource; cache sizing becomes a planning item.
First-token delay
Unchanged
Cache mostly helps ongoing tokens, not the first one.
What can go wrong
Common failure modes to watch for when this concept shows up in production.
Assuming cache fixes everything
KV cache helps ongoing generation. It does not speed up model quality or first-token latency.
Memory pressure at scale
Long contexts and large batches can exhaust GPU memory if cache sizing is ignored.
Cache invalidation surprises
Changes in prompt structure or tool output can invalidate reuse assumptions.