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KV Cache and Attention Optimization: The Hidden Performance Layer
A deep technical guide to KV caching, attention optimization, and memory-aware serving for LLMs.
KV Cache Is the Core of Fast Decoding
During autoregressive decoding, the model repeatedly attends to prior tokens. KV caching stores past key/value tensors so they do not need to be recomputed.
Without KV cache, inference cost grows dramatically with sequence length. With it, latency becomes manageable for real-time use.
Memory Bandwidth Becomes the Bottleneck
KV cache shifts the bottleneck from compute to memory bandwidth and GPU RAM.
Serving long contexts requires careful memory planning, especially under multi-user load.
Techniques for Cache Optimization
Paged attention, grouped-query attention, and sliding windows reduce cache growth.
Quantizing KV cache trades a small quality drop for significant memory savings.
Batching and Cache Reuse
Batching improves throughput but can fragment cache memory if requests vary in length.
A scheduling layer that groups similar-length requests improves cache reuse and stability.
Observability for Attention Systems
Track cache hit rates, memory usage, and tail latency. These reveal whether your cache strategy is working.
If latency spikes, the cache may be thrashing or exceeding GPU memory limits.
Cache Lifecycle Management
Plan cache eviction rules that reflect user behavior. Short sessions can use aggressive eviction, while long running sessions need stable cache retention to avoid latency spikes.
If you serve multiple models, isolate cache pools per model or per tier. This prevents one noisy workload from evicting another and keeps tail latency predictable.
Use cache warming for predictable workloads. Pre-populating popular contexts reduces cold start latency during peak traffic.
Cap cache memory per request to prevent a single long session from starving others.
Expose cache metrics to autoscaling so capacity can grow before eviction storms occur.
Validate cache correctness after upgrades. A bad cache entry can silently corrupt outputs across many requests.
Consider encrypting cache entries for sensitive workloads, especially when using shared infrastructure.
FAQ: KV Cache
Does KV cache affect output quality? No, it is an optimization that preserves exact outputs.
When does cache help most? Long-context, multi-turn chat and document QA.
What is the biggest risk? Memory exhaustion under high concurrency.
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