🚦 LLM Serving Concurrency Calculator
Estimate memory-limited and traffic-required LLM concurrency from GPU VRAM, model memory, KV cache, context length, request rate, and latency.
Calculate LLM Serving Concurrency
What this LLM serving concurrency calculator calculates
LLM serving concurrency is constrained by both memory and traffic. A server needs enough active slots to hold the KV cache for simultaneous sequences, while the workload needs enough slots to cover requests that arrive during average latency.
This calculator estimates per-request KV memory using a uniform transformer approximation, subtracts model and runtime memory from the GPU budget, and compares memory-limited concurrency with the concurrency implied by request rate multiplied by latency.
LLM Serving Concurrency Calculator formula
Assumptions and limitations
The result is an engineering estimate based on the values entered. Model architectures, tokenizers, frameworks, accelerators, parallel strategies, billing rules, and production traffic differ. Validate important decisions with measured data from the exact model, runtime, hardware, and workflow you intend to use.
Worked example
At 1.5 requests per second and 12 seconds average latency, the workload needs about 18 simultaneously active requests. If the memory estimate supports only 12, additional replicas, shorter contexts, lower KV precision, or more memory are needed.
How to interpret and use the result
Plan with p95 context length and latency as well as averages. Continuous batching can improve throughput, but long requests can occupy cache for much longer than short requests. Use real queueing and preemption metrics to determine whether the system is compute-bound, KV-memory-bound, or limited by service-level latency.
- Start with measured or representative production assumptions.
- Run conservative, expected, and optimistic scenarios.
- Record model version, framework, precision, hardware, and review date.
- Replace estimates with observed p50 and p95 values after testing.
Common mistakes to avoid
- Equating batch size with sustainable concurrent requests across variable-length workloads.
- Using total installed VRAM without subtracting model and runtime allocations.
- Sizing from average context alone when a long-tail workload drives preemption and queueing.
Methodology references
These sources describe the underlying technology. The calculator remains an approximation because implementations and workloads differ.
Frequently asked questions
Why use requests per second times latency?
Little’s law gives the average number of active requests as arrival rate multiplied by average time in the system.
Does tensor parallelism always increase concurrency?
It can free per-GPU weight memory, but synchronization overhead and how KV cache is sharded affect actual gains.
What should I measure after deployment?
Track queue time, active sequences, KV-cache usage, preemptions, token throughput, p95 latency, and out-of-memory events.
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Methodology and privacy
This educational tool uses the formula and assumptions displayed on the page. Calculations run locally in your browser, and the page does not transmit the values you enter. Results are estimates rather than provider quotes, benchmark guarantees, financial advice, or capacity guarantees. Last methodology review: August 2, 2026.
Formula Explorer connections
Interpretation: This formula describes how token load, memory and throughput constrain an online model deployment. Assumption: Tokenizer, model architecture, batching, queueing, runtime, hardware and traffic distribution must match the intended production system.