Data and Prefix Caching Practices for Large-Scale Inference
Learn how to build reliable data paths for online inference, align throughput and cost metrics, and use prefix caching to reduce repeated prefill computation. The article connects request ingestion, workload scheduling, cache design, and capacity planning into an operational framework for large-scale language model services.
1. Reading Online Data: From Traffic to Schedulable Requests
In online inference, requests rarely go straight from raw text to a model. They pass through an observable, replayable, and rate-limited data path. A typical flow starts at the ingress layer, where authentication, quota checks, and protocol conversion take place. The request body is then normalized into a consistent message structure that may include system prompts, multi-turn conversations, tool results, and metadata for multimodal attachments. Finally, requests are routed to a buffer queue or scheduler based on tenant, model version, and priority.
At large scale, data reads must meet three requirements:
- Low latency: Keep the hot path in memory or local caches where possible, rather than accessing remote object storage for every request.
- Consistency: Later turns in a conversation need the full history and the relevant cache keys. Without them, prefix matching is not possible.
- Auditability: Record request IDs, arrival times, queue delays, token counts, and failure reasons to support reconciliation and capacity analysis.
A useful design separates reads into three layers:
- Metadata: Read routing rules, quotas, and model configuration.
- Context: Use a session key to retrieve message history and descriptions of cached key-value state.
- Payload: Fetch large attachments only when needed.
Keep active session context close to the inference service, while loading cold sessions from remote storage asynchronously. This approach can support aggregation at the scale of tens of trillions of tokens per day without tying every decode step to remote I/O.
The scheduler must also turn readable requests into schedulable work. Long prompts should support chunked prefill and pipeline overlap, while short prompts can be combined into batches. The read path should expose relevant features, including prompt length, prefix-match eligibility, and the presence of non-cacheable segments such as timestamps or random nonces. These features determine whether a request uses full prefill, a prefix-cache hit, or bypasses the cache.
2. Throughput and Cost: Turning “Busy” Into Comparable Metrics
Business teams focus on latency and availability per thousand requests. Finance teams track accelerator hours and electricity costs. Engineering teams measure effective token throughput. If these metrics are not aligned, optimization efforts can work at cross-purposes. A shared measurement framework is essential.
At minimum, report throughput separately as:
- Prefill tokens per second
- Decode tokens per second
- End-to-end completed requests
- Effective output tokens
Prefill depends on compute and memory bandwidth, while decode is especially sensitive to memory bandwidth and batch size. Reporting only a single “tokens per second” figure can hide whether prefill is saturated while decode capacity sits idle, or the other way around.
Cost metrics also need a fixed denominator. One common approach is to use accelerator-hours as the denominator and include depreciation or rental, electricity, networking, and storage amortization in the numerator. Costs can then be expressed per effective output token or billable token to show whether the cost of producing useful output is falling.
The accounting rules should specify whether to include training workloads, idle warm-up capacity, failed retries, and wasted speculative sampling. They should also clarify whether savings from cache hits reduce the cost numerator or are reflected only as higher utilization. Once defined, the same rules should be used for weekly reporting, capacity planning, and financial reconciliation.
Teams can also track effective compute utilization: the number of useful tokens completed divided by the theoretical peak for the accelerator type, precision, and parallelism strategy in use. When utilization is low, investigate batch formation, pipeline bubbles, communication overlap, and cache misses before adding more accelerators. When utilization is high but latency is worsening, look for queue buildup and long-tail requests that are holding up batches.
3. Prefill Caching: How Hit Rates Change the Cost Curve
Autoregressive inference has two stages: prefill and decode. Prefill encodes the prompt into a key-value cache; decode generates tokens step by step. When requests share a system prompt, tool instructions, or the same retrieved document prefix, the corresponding key-value state does not need to be recomputed. It can be retrieved from GPU memory or a nearby cache, then combined with the new suffix. This is the core idea behind prefix caching, also known as prompt caching.
A high cache hit rate shifts prefill from computation toward data movement. Session-based, customer support, and code-completion workloads often contain substantial prefix reuse. By removing repeated attention computation from the hot path, caching leaves more accelerator time for decoding and genuinely new context. With a low hit rate, prefill can become a bottleneck again: long prompts constrain batch size and increase unit costs.
Improving hit rates is not simply a matter of adding GPU memory. Cache-key design and lifecycle management matter just as much. Generate prefix hashes from normalized token sequences, and explicitly exclude fields that cannot be shared. Use a tiered cache: keep the hottest prefixes in GPU memory, the next tier in host memory, and use remote storage only as a cold backup.
Eviction policies should account for both recency and prefix length. Missing a long prefix can cost substantially more than missing a short one. Correctness is equally important: invalidate cache entries when the model version, quantization strategy, or parallel sharding changes, so stale state cannot produce silent errors.
Monitor cache hits and misses, average matched-prefix length, evictions caused by GPU memory pressure, and whether prefixes can be shared across instances. If hit rates are already high but performance is still below expectations, check whether private user data has been included in a supposedly shared prefix or whether microbatch splitting has disrupted prefix alignment.
4. Connecting the Components Into an Operational Loop
Data-read quality determines whether cache keys remain stable. Cache hits shape prefill load. The balance between prefill and decode determines accelerator-hour usage, which in turn affects unit costs. Monitoring should bring these metrics together: read latency and remote-fetch rates, queue depth, time spent in prefill versus decode, cache hits and evictions, accelerator-hour cost, and cost per effective token.
Capacity planning should not rely on peak QPS alone. Estimate accelerator requirements from the token budgets for cache-miss prefill and decode, and reserve capacity for the cache working set. Changes such as prompt updates or retrieval-index switches can sharply reduce hit rates, so rollouts should be gradual and include cache warm-up.
For security and isolation, multi-tenant prefix sharing must not expose data across tenants. Public prefixes and private suffixes should be kept strictly separate in the cache-key space.
For teams, a shared understanding of what is read, computed, and cached is more valuable than a list of isolated optimizations. Make the online data path replayable, cost metrics comparable, and prefix caching measurable. That is how large-scale inference moves beyond simply adding accelerators and makes each accelerator-hour produce more useful output.
