For multiple AI agents, prioritize GPU memory and KV-cache capacity first, then tune maximum context length and batch or sequence limits to match real concurrent requests. If the model and serving state will not fit on one GPU, configure multi-GPU parallelism and make the runtime’s device selection match that topology. There is no universally best setting: the right values depend on the model, context lengths, and workload.
Why GPU memory and the KV cache come first
Serving uses GPU memory for model weights and for request state, including the key-value (KV) cache that holds information needed as sequences are processed. The share of memory made available to the runtime therefore affects how much serving capacity remains for active requests.
In vLLM, GPU memory utilization is a runtime control affecting memory available for weights and KV cache. Its optimization and tuning documentation warns that a conservative fixed KV-cache allocation can limit batch concurrency, while an overly optimistic allocation can fail. Leave room for other GPU allocations and validate the setting under the peak concurrency you expect rather than treating maximum allocation as automatically best.
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 figure describes the default behavior documented for that backend; it is not a universal setting or a guarantee for every vLLM release and configuration.
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How context length and concurrency interact
Maximum model length and batch or sequence limits should be tuned together. Longer contexts consume more serving memory and can leave room for fewer simultaneous sequences. Batch and sequence limits influence how many requests the scheduler handles together, affecting both memory pressure and throughput.
NVIDIA’s DGX Spark vLLM serving instructions identify batch size, maximum model length, and memory settings as tuning dimensions. Their recommended values are specific to that platform and workload, not universal defaults for other systems.
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- Maximum model length: Set it for the longest context the application actually needs; do not assume the model’s full possible context is required for every deployment.
- Batch and sequence limits: Adjust them for the expected mix of prompt and output lengths and the service’s latency target. A larger limit is not automatically better if it creates memory pressure or harms response times.
- KV-cache budget: Check whether cache capacity allows the desired simultaneous sequences at those context lengths, then verify allocation stability under load.
When multi-GPU parallelism matters
Adding GPUs can address a capacity problem when one device—or one node—cannot hold the model and its serving state. It is not enough to expose more devices: the serving framework must support the topology, and parallelism settings must agree with the GPUs selected.
vLLM’s parallelism and scaling guidance covers tensor parallel and multi-node deployment options. For NVIDIA Triton’s vLLM backend, the selected GPU ID count must match tensor parallel size multiplied by pipeline parallel size, as described in its backend documentation.
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A practical tuning order
- Define the workload. Estimate concurrent agent requests, prompt lengths, generated output lengths, and the latency target. Include tool-use patterns if they change how often or how long requests remain active.
- Check model fit and memory headroom. Account for model weights, KV cache, and other GPU allocations. Start from the serving runtime and hardware documentation rather than copying a setting from a different platform.
- Set maximum model length. Choose the longest context the application needs, not merely the largest context the model advertises.
- Tune batch or sequence limits and cache capacity together. Test whether the chosen limits support the intended concurrent workload without allocation failures or excessive memory pressure.
- Configure parallelism if a device or node is insufficient. Confirm platform and runtime support, then match selected GPU count to the configured tensor and pipeline parallelism.
- Measure and adjust one relevant control at a time. Use representative concurrent requests; record throughput, response latency (including tail latency), memory use, and failures. Keep settings that meet the service target with stable headroom.
This is an operating method, not a reported benchmark: the cited documentation identifies tuning dimensions but does not establish a universal winning configuration or a specific performance gain from changing one.
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Settings to prioritize at a glance
| Setting or factor | Why it matters | How to approach it |
|---|---|---|
| GPU memory utilization and KV-cache budget | Determines memory available for weights and active request state; too little can constrain concurrency, while an optimistic allocation can fail. | Start with runtime and hardware documentation, preserve headroom for other allocations, and validate at expected peak concurrency. |
| Maximum model length | Longer contexts require more serving memory and can reduce the number of sequences that fit simultaneously. | Set the limit to the longest context the application actually needs. |
| Batch and sequence limits | Influence how many requests are scheduled together, throughput, and memory pressure. | Tune against the real request mix and latency target; do not assume the largest limit is best. |
| GPU count and parallelism | Can let a model that does not fit on one GPU or node run across multiple devices. | Verify runtime and platform support and align selected devices with tensor and pipeline parallel settings. |
| Workload and service target | Agent requests differ in context, output, tool-use cadence, and concurrency. | Evaluate representative concurrent traffic and track throughput, tail latency, memory headroom, and stability. |
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