A local AI agent can feel slow for several different reasons: the model may take time to load, process a long prompt, generate tokens, wait on CPU or GPU resources, or repeat inference around tool calls. First find out where the time goes; then change the setting or hardware path that matches the bottleneck. The steps below apply across local runtimes, with specific checks for Ollama, llama.cpp and vLLM.
Find out which part of the agent is slow
“Slow” can mean a long pause before the first answer token, slow streaming after generation starts, time spent waiting for a tool, or too many model calls across an agent workflow. These point to different causes. A faster GPU will not fix a slow external tool, and an SSD can help load model files but does not necessarily increase token-generation speed after the model is loaded.
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Record a useful baseline
Run the same task a few times and note when the delay occurs: before the first token, between tokens, during a tool call, or between agent steps. LocalAI recommends using debug logs with per-token timing and a simple streaming request to help distinguish these stages. Keep the model, prompt, context setting and concurrency the same when comparing later changes.
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Do not assume that installing a GPU or enabling acceleration means the model is actually using it. A model may run on the CPU or be split between CPU and GPU, particularly when memory is constrained.
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Ollama
Run ollama ps and inspect the PROCESSOR column. It reports whether the model is running on the GPU, CPU, or a mix of both. Ollama’s guide to this command is at Ollama’s FAQ.
llama.cpp
Check the startup output for messages showing whether GPU layers were offloaded. If the model is on the CPU when you expected GPU acceleration, check that the runtime build, GPU backend and hardware are compatible before changing thread counts. See llama.cpp’s performance guidance.
vLLM serving
Low GPU utilization in a serving setup does not automatically mean the GPU is the problem. CPU contention in tokenization, scheduling, media loading or output handling can limit the work reaching the GPU. Check runtime diagnostics and CPU load as well as GPU utilization. vLLM’s documentation focuses on serving and memory-management behavior; its guidance is not a claim that it is the best choice for every single-user agent. See vLLM documentation.
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Check memory before increasing context or forcing more GPU work
GPU memory must accommodate both model weights and the key-value (KV) cache used for context. If the combined demand exceeds available VRAM, the runtime may offload some work or fail to fit the intended model and context. The appropriate fix depends on the model and workload.
- Try a smaller model or a smaller quantization, then check that answer quality and tool-call reliability are still acceptable.
- Reduce the context window if the task does not need as much history.
- Free VRAM used by other processes, or adjust how many layers are offloaded to the GPU.
- After each change, verify placement and measure the same task again.
More context is not automatically better for speed. A longer conversation can increase prompt-processing work and KV-cache demand. Keep the context large enough for the task, but remove irrelevant history instead of maximizing the setting by default.
Set context to fit the task
Ollama’s checked FAQ describes a configurable context window and states a 4096-token default; defaults can change between releases, so check the installed version’s documentation and configuration. LocalAI notes that the prompt plus generated output must fit within the context window. If a request exceeds that limit, it may not work as intended; setting an unnecessarily large window can also consume memory that could otherwise support model execution.
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For an agent, retain the instructions and history needed to complete the current task, and trim unrelated conversation or tool output. When changing the context setting, test with a representative task rather than judging from a short prompt alone. See LocalAI’s getting-started guidance for context and inference troubleshooting.
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Tune CPU threads systematically
More CPU threads do not guarantee faster inference. A high thread count can oversaturate the CPU, while too few threads may leave capacity unused. llama.cpp recommends testing thread counts and increasing gradually until performance stops improving or a bottleneck appears, then backing down. LocalAI suggests matching physical cores as a starting point, not as a universal optimum.
- Choose one representative prompt and keep the model, context and other settings fixed.
- Test a low thread count, then increase it in measured steps.
- Record generation rate and overall latency for each run.
- Use the fastest stable setting for the actual workload; do not generalize a setting from another machine.
llama.cpp’s performance tips include an example measured on an A6000 with 48 GB VRAM, a 7-physical-core CPU, 32 GB RAM and a specified 30B Q4 model. The tokens-per-second results vary with flags and apply to that setup, not to other computers as a performance promise.
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Reduce cold starts and avoidable agent work
Keep a frequently used model available
If the long pause occurs after the agent has been idle, the model may need to load or wake before answering. Ollama documents preloading and a default five-minute model residency period in its checked FAQ, with keep_alive controls. Verify the behavior for your installed version. LocalAI recommends storing model files on an SSD rather than an HDD, which can help with loading; it does not establish that SSD storage speeds generation once the model is resident.
Reduce redundant model and tool calls
Agent latency is cumulative: repeated inference and serial tool waits can make a workflow slow even when each response streams at a reasonable rate. Keep only task-relevant context, ask the model for the amount of output the next step needs, and avoid redundant serial tool calls. Preserve the information the agent needs to make a correct decision; shorter is not better if it causes retries or mistakes.
Match the runtime to the workload
Compare runtimes only after you know the workload and have a baseline. Relevant factors include operating system, model format, GPU architecture and memory, API needs, and whether your priority is single-user latency or throughput across concurrent requests. NVIDIA’s guidance covers these compatibility and workload considerations at its LLM deployment guide.
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vLLM and PagedAttention authors reported 2–4× throughput at the same latency against the systems they compared on the workloads evaluated in their 2023 paper. That is a serving-system benchmark result, not a guaranteed speedup for a personal local agent. See the PagedAttention paper.
For a meaningful local comparison, use the same model, prompt, context, concurrency and machine, then compare time to first token, generation rate, memory fit, answer quality and tool-call reliability. A throughput result under concurrent serving does not necessarily predict latency for one user.
Quick Recap
Match the symptom to the first check
| What you observe | Where to look | First useful action |
|---|---|---|
| Long pause before output, especially after idle | Model loading, cold start, disk or prompt processing | Check timing or logs; try preloading or keep-alive; if model files are on an HDD, consider SSD-backed storage for loading. |
| Slow generation and the model is shown on CPU | GPU placement, backend compatibility or insufficient VRAM | Check offload messages and compatibility; then consider a smaller model or supported quantization. |
| CPU and GPU are busy while VRAM is full | Partial offload or memory pressure | Reduce model or context footprint, free VRAM, or adjust layer offload, then retest. |
| Performance gets worse in long conversations | Context length, KV-cache demand and prompt-processing load | Trim irrelevant history and use a task-appropriate context setting that fits memory. |
| GPU utilization is unexpectedly low in a serving setup | CPU-side tokenization, scheduling, media loading or output processing | Inspect CPU contention and runtime diagnostics as well as GPU activity. |
| Many slow agent cycles despite acceptable token speed | Repeated inference and serial tool waits | Remove unnecessary rounds and pass compact, relevant results between steps; measure end-to-end task time. |
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