LLM Batching and Caching: How to Improve Inference Throughput

Batching and caching raise LLM throughput by avoiding idle hardware and repeated work. Inference engines use continuous batching to process many requests together and paged KV caches to fit more of them in memory. Prefix and provider prompt caching reuse processing of identical prompt beginnings, so put stable instructions and documents first. Application caches return stored answers for repeated requests, keyed by everything that affects the answer. Batch APIs process offline work cheaply. Invalidate caches when data changes and never share personalized results across users.

This is a deep dive within AI inference optimization. Budgets and spend controls are in LLM cost optimization and engine setup in LLM model serving.

Static batching waits to collect a group of requests, processes them together and returns all results when the longest finishes, so short requests wait for long ones. Continuous batching, also called in-flight batching, schedules at the level of each generation step: finished requests leave and new ones join immediately. Combined with chunked prefill, which splits long prompt processing into pieces interleaved with decoding, it keeps GPUs busy and reduces latency spikes. Engines such as vLLM implement these by default; the original PagedAttention paper explains the memory management that makes large batches practical.

Within a request, the KV cache stores attention keys and values for every processed token so each new token does not recompute the whole context. It is what makes generation feasible, but it consumes GPU memory proportional to context length and the number of active requests, and it often limits how many users a GPU can serve. Paged allocation reduces waste; KV cache quantization and shorter contexts reduce size.

Many requests share the same beginning: system instructions, tool definitions, a long document being discussed. Self-hosted engines can reuse cached KV data for identical prefixes. Hosted providers offer prompt caching with lower prices and latency for cached tokens: OpenAI documents automatic prompt caching for supported models, while Anthropic documents prompt caching that you control by marking cacheable sections. Minimum lengths, retention and pricing differ by provider and model, so check current documentation.

Design prompts for caching: stable content first, variable content last, and avoid inserting timestamps or request IDs early in the prompt, which break prefix matches.

Cache keys must include everything that changes the right answer: user or tenant where results are personalized, permissions, prompt and model versions, and the version of underlying data or index. Set time-to-live values that match how quickly the data changes, and purge entries when sources are updated. Semantic caches need conservative similarity thresholds and are best limited to non-personalized, low-risk content. A cache that ignores permissions becomes a data leakage channel; see AI data leakage.

Classification of backlogs, enrichment of catalogues, evaluation runs and re-summarization jobs rarely need real-time answers. Several providers offer batch interfaces that accept large sets of requests and return results within a stated window at reduced prices. Self-hosted, schedule such jobs into low-traffic periods with throughput-optimized settings.

Batching and caching can increase throughput and cut costs substantially, often with no quality change. Their limits: batching trades some latency for throughput, caches add invalidation complexity and correctness risks, and cache hit rates depend entirely on how repetitive your workload is. Measure hit rates and latency before and after.

Track hit rates at each cache layer, latency and cost with and without hits, and correctness: sample cached responses and verify they are still valid for the request and user. For prefix caching, provider usage reports often show cached input tokens separately, which lets you measure savings directly. Low hit rates usually mean prompts vary too early or requests are less repetitive than assumed; high hit rates with complaints about stale answers mean invalidation is too slow.

Embedding generation and offline classification are ideal for batching: send many items per request where APIs allow, run self-hosted embedding models with large batches and schedule bulk jobs for low-traffic periods. Respect rate limits with queues and backoff, and cache embeddings keyed by content hash and model version so unchanged content is never re-embedded. See data pipelines for AI.

In SaaS products, caching must respect tenant boundaries. Response caches should include the tenant ID in every key, and often the user's permission set. Prefix caches are generally safe when the cached prefix contains only shared content, such as system instructions and tool definitions, but avoid placing one tenant's documents in a prefix that could be matched by another tenant's request in self-hosted engines. Check how your provider isolates prompt caches between organizations. Tenancy design is covered in AI-powered SaaS development.

An illustrative scenario, not a client case: a contract review tool sends a long contract plus a different question each time, with the question placed before the contract and a timestamp at the top. Moving instructions and the contract to the front, removing the timestamp and enabling prompt caching lets follow-up questions reuse the cached prefix, reducing time to first token and input cost for the second and later questions on each contract.

Batching keeps hardware busy and caching avoids repeated work. Structure prompts for prefix reuse, let engines batch continuously, cache answers with complete keys and invalidation, and send offline work to batch processing.