Estimate a memory-based upper bound by dividing the serving engine’s available GPU KV-cache tokens by the tokens each active inference sequence is expected to occupy. Then load-test the real workload: cache capacity alone cannot tell you whether the GPU will meet your throughput and latency targets.
Define what “concurrent sessions” means for your workload
An AI agent session is not necessarily one continuously active model request. An agent may pause while a tool runs, then send another request; it may also issue multiple model requests during its lifetime. GPU capacity depends on the inference sequences active at a given time and the tokens they occupy, not simply the number of open agent sessions.
Before estimating capacity, record the model and serving engine, weight and KV-cache formats, prompt and output token-length distributions, request arrival pattern, expected active-session count, and latency targets. These details determine both the memory estimate and what a successful deployment must deliver.
Work out the GPU memory available for the KV cache
GPU memory has to accommodate more than model weights. Runtime allocations—including activation and input/output tensors—also consume space, leaving the remainder for the KV cache. 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 estimate or configured cache allocation rather than treating the GPU’s full memory capacity as cache space. For example, vLLM can infer KV-cache capacity from its GPU memory utilization setting or accept a byte limit directly. Consult the documentation for the specific serving-engine release and configuration you plan to run.
Calculate a first memory-based concurrency bound
Once you know how many KV-cache tokens are available, divide that total by the tokens an active inference sequence is expected to occupy:
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Memory-bound active sequences ≈ available GPU KV-cache tokens ÷ expected tokens per active sequence
The per-sequence estimate should account for the prompt or context retained and generated tokens. Because sessions vary, use a representative distribution or conservative percentile of token occupancy—not a single average that conceals long-context sessions. The result is a starting memory ceiling for active sequences, not a guaranteed number of agent sessions.
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vLLM’s parallelism and scaling guide shows how to read the engine’s reported cache capacity and an example concurrency calculation. In that illustrative configuration, the documentation reports 643,232 GPU KV-cache tokens and 15.70x maximum concurrency for 40,960 tokens per request. Those are example output values, not a general GPU benchmark or a capacity promise for another model or workload.
Check throughput and latency under representative load
A deployment can have enough cache for a given number of sequences and still miss its service targets. Prefill (processing context) and decode (generating tokens) have different performance demands; increasing concurrency or optimizing one latency measure can affect another. Measure the system at the prompt lengths, output lengths, request arrivals, and concurrency you expect in production.
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Track aggregate input and output token rates, time to first token, inter-token latency, KV-cache use, and memory pressure. NVIDIA’s Triton Inference Server metrics reference describes server measurements that include first-response latency and KV-cache usage. Compare latency percentiles such as p50, p95, and p99 with your targets, not just an average.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Adjust the deployment based on the limiting factor
- The model or runtime does not fit: Increase available GPU memory or distribute the model across GPUs or nodes.
- The cache fits, but throughput or latency targets fail: Test serving configuration and batching under the same workload, or add replicas or more capacity.
- The reported cache capacity is still too low: Evaluate parallelism and additional GPUs or nodes. vLLM’s scaling guide documents tensor- and pipeline-parallel options and advises adding GPUs or nodes when capacity is below requirements.
When comparing deployment options, assess model fit and memory headroom, cache tokens and 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 evidence-based universal “agent sessions per GPU” figure: the useful number depends on the workload and its service targets.
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