The most important GPU settings for serving multiple AI agents are the ones that determine how much memory remains for the model’s KV cache and how many requests the server can handle at once. Start with GPU memory utilization and cache capacity; then tune maximum context length and batch or sequence limits to match real agent traffic. Add multi-GPU parallelism only when the model or workload requires it, and configure the runtime to match the hardware layout.
Start with memory: weights, KV cache, and headroom
A serving GPU must hold the model weights and the active request state. In vLLM, the GPU memory utilization setting controls memory made available for the model and KV cache. That makes it a capacity control, not simply a speed slider: reserving too little can constrain concurrent requests, while allocating too aggressively can leave insufficient room for other allocations or cause failures.
vLLM’s optimization guidance discusses KV-cache sizing as a separate practical concern: a conservative fixed cache size can cap batch concurrency, while an optimistic setting can fail during allocation. Use the runtime’s documented behavior and your hardware’s available memory to establish a starting point, leave room for other GPU allocations, and validate at expected peak load. See vLLM’s Optimization and Tuning documentation.
NVIDIA’s Triton Inference Server vLLM Backend documentation states: “Note: vLLM greedily consume up to 90% of the GPU’s memory under default settings.” That describes the default behavior documented for that backend; it is not a guarantee for every vLLM release or configuration. Consult the NVIDIA Triton Inference Server vLLM Backend documentation for the applicable setup.
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Set context length for the requests agents actually send
Maximum model length affects how much serving memory long requests can require. A large configured context can reduce how many sequences fit concurrently, even if most agent turns are short. Set the limit to the longest context your application genuinely needs rather than automatically using the model’s maximum possible context.
Agent traffic can include accumulated conversation history, tool results, and new instructions. Include those in representative tests: a short user prompt alone may understate the context that an agent workflow eventually sends to the model. NVIDIA’s DGX Spark serving instructions identify maximum model length, batch size, and memory settings as tuning dimensions; their recommendations are specific to that platform and workload, not universal defaults. See NVIDIA’s DGX Spark vLLM serving instructions.
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Tune batching and sequence limits against concurrency and latency
Batch and sequence limits determine how many requests or sequences the server schedules together. Raising them may help serve more work together, but it also increases memory pressure; the largest permitted value is not automatically best. Tune these limits with the context lengths and concurrency your service expects, while watching both throughput and response latency.
For an agent service, test a realistic mix rather than identical prompts: vary prompt length, expected output length, and the number of simultaneous agent requests. Record throughput, latency (including tail latency), GPU memory use, and allocation or runtime failures. Change one relevant control at a time so you can identify which adjustment affected the result. These are operational testing recommendations, not benchmark results.
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Use multiple GPUs when a single GPU is not enough
Multi-GPU parallelism can help when the model cannot fit on one GPU or when the deployment needs a supported multi-GPU or multi-node arrangement. vLLM documents tensor parallel and multi-node options in its Parallelism and Scaling guide. The appropriate topology depends on the model, hardware, and serving stack.
Configuration must agree with the devices assigned to the server. NVIDIA Triton’s vLLM backend documentation says the selected GPU ID count must match tensor parallel size multiplied by pipeline parallel size. Check that relationship and confirm that the runtime and platform support the chosen topology before deployment; adding GPUs without matching parallelism settings does not by itself solve a configuration mismatch.
Prioritize settings in this order
- Memory utilization and KV-cache capacity: establish how much space is available for weights and active request state, with room for other allocations.
- Maximum model length: set a limit based on the longest context your application needs.
- Batch or sequence limits: tune against representative concurrency and the service’s latency target.
- GPU count and parallelism: use multi-GPU or multi-node options when capacity requires them, and make the runtime configuration match the hardware.
There is no universal best utilization, context length, batch size, or GPU configuration for multiple agents. The useful settings are the ones that keep the model stable under your real request mix while meeting your capacity and latency requirements.
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