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When an LLM runs out of GPU memory during decoding, or serves fewer tokens per second than expected, the KV cache is often a key constraint. It grows with the context retained for each active request, consuming accelerator memory; attention over that growing context also makes decoding frequently memory-bound. Improving production throughput therefore means managing cache capacity and data movement as well as choosing efficient kernels and keeping requests usefully batched.
Why the KV cache becomes a production bottleneck
Autoregressive generation produces one token at a time. To attend to earlier tokens without recomputing their key and value representations at every step, an inference engine stores them in a KV cache. The cache grows as requests accumulate tokens, and its total demand rises with both context length and the number of concurrent requests.
That creates two related limits. First, the cache competes with model weights and other runtime needs for finite GPU memory, eventually limiting how many requests or how much context can be served. Second, each decoding step must access cached data, so generation can be constrained by memory bandwidth even when the accelerator still has arithmetic capacity available. The vLLM, AWS, and Red Hat AI authors’ 2026 FP8 KV-cache analysis says the cache can dominate GPU memory at contexts of 128k tokens or more; that observation is a warning about long contexts, not a threshold that applies to every model or deployment.
More concurrent requests can improve aggregate utilization only while the runtime can keep their work and caches resident and efficiently scheduled. Simply increasing the batch or concurrency limit can instead lead to memory pressure, queueing, or worse latency.
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Which changes can improve throughput?
There is no universal throughput multiplier for a given optimization. Results depend on the model, GPU, attention pattern, prompt and output lengths, traffic shape, cache format, and serving policy. Treat each lever as a hypothesis to measure against representative traffic.
Use paged allocation and reuse common prefixes
PagedAttention stores a sequence’s KV cache in fixed-size blocks and maps its logical blocks to physical memory. Block-based allocation reduces the waste and fragmentation associated with reserving large contiguous regions, while enabling sharing in cases such as common prefixes and multi-sequence operations. Automatic prefix caching can also avoid repeating prefill work for matching prefixes, when the engine supports it and the workload actually contains reusable prefixes.
Published performance figures illustrate potential, not a promise for a new deployment. The vLLM project’s 2023 launch post reported up to 24× higher throughput than HuggingFace Transformers and up to 55% lower memory use for complex sampling through PagedAttention sharing. A 2023 peer-reviewed PagedAttention paper from UC Berkeley’s Sky Computing Lab and collaborators reported 2–4× throughput over FasterTransformer and Orca at comparable latency on its evaluated workloads. Those results use different baselines and workload conditions, so they should not be compared as if they were a single head-to-head test.
Keep useful work in the batch
Continuous batching admits and retires requests at iteration boundaries rather than waiting for an entire batch to finish together. This can keep decode work packed as request lengths vary. The benefit depends on arrivals, request lengths, cancellation behavior, scheduling policy, and the latency target; an aggregate tokens-per-second number alone cannot reveal whether users are waiting longer for the first token or between tokens.
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Attention implementations such as FlashAttention or FlashInfer can affect performance, but the suitable backend depends on GPU architecture, model attention pattern, and runtime configuration. Eligibility also changes as hardware and software support evolves. Verify which backends are available for the exact deployment rather than assuming that a named kernel is universally faster or enabled.
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Consider FP8 KV-cache quantization
Storing the cache in FP8 can reduce its memory footprint, potentially making room for more concurrent requests or longer contexts. That capacity gain does not by itself establish better end-to-end latency or preserved output quality. Benchmark the exact model and workload, and compare quality metrics as well as latency and throughput before making FP8 the production setting.
Balance prefill against decoding
Long prompts require prefill work, while generated tokens require repeated decode work. Chunked prefill and scheduling controls can prevent a large prompt from monopolizing resources while active requests wait for their next token. Examine the prefill-to-decode token mix and latency objectives; optimizing only aggregate output tokens per second can conceal starvation or poor inter-token latency.
Offload cache only when transfer costs make sense
Moving KV-cache data to CPU DRAM can expand effective capacity beyond what fits in GPU memory, but using that data again requires transfers over PCIe or another interconnect. Offloading is useful only if the capacity it unlocks outweighs the transfer cost for the workload. Where possible, overlap transfers with compute, then measure host-device transfer volume and request latency to check whether data movement erases the gain.
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Match parallelism to the model and machine topology
Tensor, pipeline, data, expert, and context parallelism distribute model computation or request load in different ways. The appropriate choice depends on model size, hardware topology, and latency objectives; adding parallelism is not automatically beneficial if communication or coordination costs outweigh its advantages. Evaluate the available strategies with the actual model and deployment topology.
How should you compare vLLM and TensorRT-LLM?
Both are inference-engine options, but a label such as “high throughput” is not enough to select one. An EMNLP industry paper characterizes vLLM as a high-throughput distributed engine and TensorRT-LLM as an industrial NVIDIA runtime with paged KV-cache and batching capabilities. Use those descriptions as context, then verify support and behavior for the target deployment.
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Compare the engines on the same representative traces and hardware where possible. Check supported accelerators, attention backends, batching controls, quantization formats, prefix caching, distributed-parallelism options, observability, and upgrade cadence. A vendor benchmark is evidence for its stated test, not a portable forecast for another model or traffic pattern.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to measure an inference optimization
Establish a baseline before changing the cache dtype, scheduler, backend, or engine. Keep the tested model, hardware, software versions, and request mix visible in the results so that a throughput change can be interpreted rather than detached from its conditions.
Capture throughput, latency, and capacity together
- Throughput: output tokens per second, alongside the number of active requests.
- Latency: time to first token, inter-token latency, and p50, p95, and p99 request latency.
- Scheduling and queueing: admitted queue depth and active concurrency.
- Memory and cache behavior: GPU memory utilization, KV-cache occupancy, and cache hit rate where prefix reuse is enabled.
- Work mix and transfers: prefill-to-decode token ratio and host-device transfer volume when offloading is used.
- Output quality: task-relevant quality metrics after any cache quantization change.
Test the traffic that will actually arrive
Use production-like prompt and output lengths, arrival bursts, cancellations, prefix reuse, and sampling settings. Include the interaction between long prompts and active decodes, rather than testing only a steady, uniform batch. Report the hardware, software versions, batch policy, cache dtype, context length, and deployment geography with the results. Compare both aggregate throughput and latency tails: a setting that raises total tokens per second may still be unsuitable if it breaches the service’s latency objectives.
What to check when decoding runs out of memory
Diagnose the constraint before increasing concurrency or changing precision. The cache is a likely contributor when memory pressure worsens with longer contexts or more simultaneous requests, but the right remedy depends on whether the limit is allocation waste, cache capacity, repeated prefix work, scheduling imbalance, or data movement.
Quick Recap
- Pressure rises with concurrent requests: inspect cache occupancy and admission behavior; paged allocation may reduce allocation waste, but it does not remove the memory required to retain active contexts.
- Long contexts sharply reduce capacity: evaluate cache quantization or offloading with quality, transfer, and latency measurements included.
- Shared prompts recur: check whether prefix caching is enabled and whether requests actually share reusable prefixes.
- Long prompts disrupt active generations: examine chunked prefill and scheduling controls, then review inter-token and tail latency.
- Memory fits but tokens per second disappoint: investigate batching effectiveness, backend eligibility, and whether decode is constrained by memory bandwidth rather than arithmetic throughput.
- Offload increases capacity but hurts responsiveness: measure transfer volume and determine whether transfers are adequately overlapped with compute.
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