Estimate GPU capacity by dividing the serving engine’s available KV-cache tokens by the tokens each active inference sequence is expected to occupy. Treat that result as a memory-based ceiling—not a promise of how many agents the GPU can serve at your latency target. Throughput and response time may become limiting first.
What counts as a concurrent agent session?
An agent session is not always one continuously active model request. An agent may pause while a tool runs, then send another request using an expanded conversation context. Size for active inference sequences and the tokens they actually occupy, rather than counting every open or logged-in agent as if it were generating simultaneously.
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Before estimating capacity, specify the deployment and its workload:
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- Memory formats: Record the formats used for model weights and the KV cache.
- Token lengths: Estimate prompt/context and generated-output lengths, including their distribution rather than only an average.
- Traffic pattern: Describe request arrivals, expected active sequences, and pauses between an agent’s model calls.
- Latency goals: Set acceptable time to first token and time between generated tokens, as well as aggregate throughput needs.
Build the GPU memory budget
GPU memory is shared by model weights, runtime allocations, activations, I/O tensors, and the KV cache. The amount left for the cache therefore cannot be calculated from the GPU’s advertised memory alone. NVIDIA’s TensorRT-LLM memory documentation identifies weights, internal activation tensors, and I/O tensors as major inference memory contributors.
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Use the serving engine’s reported cache capacity for the exact model and configuration where possible. For example, vLLM can infer KV-cache capacity from its GPU memory-utilization setting or use a specified byte limit; its optimization documentation describes these settings. Keep enough memory headroom for the engine’s non-cache allocations and operational needs rather than treating all unallocated memory as safely available cache.
Estimate the memory-limited concurrency
Once you know the total GPU KV-cache token capacity, divide it by the number of tokens a typical active sequence retains: prompt/context tokens plus generated tokens that remain in context. This gives a first-pass estimate of how many such sequences can fit in the cache at once.
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For example, vLLM’s parallelism and scaling guide shows illustrative startup output of 643,232 GPU KV-cache tokens and a maximum concurrency of 15.70× for requests configured at 40,960 tokens each. Those are configuration-specific example values from the documentation, not a hardware benchmark or a general guarantee. Your model, cache format, memory settings, and actual token lengths will produce different results.
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Do not assume every active sequence has the same length. Use a representative distribution or a conservative percentile for the workload. A long-context tail can consume cache disproportionately, reducing the number of sequences that fit compared with an estimate based only on a short average prompt.
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Check throughput and latency under load
A cache-fit estimate answers only whether sequences can fit in memory. It does not show whether the GPU can process them quickly enough. Prompt processing (prefill) and token generation (decode) have different performance demands; a configuration that increases concurrency or improves one latency measure can affect another. NVIDIA’s LLM metrics reference covers measurements including first-response latency and KV-cache usage.
Load-test the actual model, serving engine, token-length mix, arrival pattern, and concurrency you expect in production. Track:
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- Aggregate input and output tokens per second.
- Time to first token and inter-token latency, including p50, p95, and p99 where relevant to your service target.
- KV-cache utilization and GPU memory pressure.
- Whether latency or throughput targets fail as concurrency rises, even while cache capacity remains available.
Use the results to size the deployment
If the model and its runtime allocations leave too little cache, increase available GPU memory or distribute the model across GPUs or nodes. If sequences fit but measured throughput or latency misses the target, test serving configuration and batching, or add replicas and capacity. vLLM’s scaling guide describes tensor and pipeline parallelism and advises adding GPUs or nodes when reported capacity is below throughput requirements.
When comparing deployment options, use measurements from the same workload: model fit and memory headroom, available cache tokens, workload-specific concurrency, aggregate tokens per second at target load, latency percentiles, GPU count and interconnect, scaling behavior, and cost at measured utilization. There is no universal sessions-per-GPU figure or cross-vendor price/performance ranking established by these sources.
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