To reduce memory use in local AI evaluations, first lower the evaluation’s batch size or concurrency and set a context limit that still covers the task. If memory pressure continues, consider quantized weights, then tune backend-specific overhead such as vLLM’s CUDA graphs and caches. Change one setting at a time and compare peak memory, runtime and evaluation results; there is no universal savings figure or lowest-memory backend.
Identify which memory limit you are hitting
GPU memory and CPU RAM are separate constraints, and the useful controls depend on which one is exhausted. Before changing settings, record the model and backend, hardware, context length, batch size or concurrency, precision, and whether prompts include images or other multimodal inputs. A configuration that frees GPU memory may not address CPU RAM pressure.
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The controls below are documented for specific tools, particularly vLLM and lm-evaluation-harness. Their command-line interfaces can change, so check the options supported by the version you have installed.
Reduce parallel work first
Use an automatic batch size with lm-evaluation-harness
The harness supports --batch_size auto, which detects a batch size that fits the device. If examples vary in length, its README describes periodically recalculating the size with auto:N. A smaller batch can reduce simultaneous work, though it may also reduce throughput.
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Cap concurrent sequences in vLLM
For vLLM, lower max_num_seqs to limit how many sequences run concurrently. This can ease memory pressure from parallel requests, at the cost of less parallel throughput. Tune the limit to the evaluation workload rather than assuming a particular value will suit every model or machine.
Set a context ceiling that fits the evaluation
vLLM’s max_model_len sets a maximum model length and can be reduced to lower memory use when an evaluation does not need the model’s full context window. Choose a ceiling that covers the genuine input and output lengths required by the task.
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Do not shorten prompts or completions simply to make a run fit if doing so changes what the benchmark measures. In that case, the result is no longer directly comparable to an evaluation using the intended inputs.
Do these 3 things before closing this tab:
1Scan for outdated or missing drivers - takes under a minute2Repair Windows errors before they cause bigger problems3Fix the driver behind crashes, sound loss and screen glitchesConsider quantized weights, then check the results
vLLM documents that “Quantized models take less memory at the cost of lower precision.” Use a quantized checkpoint or configuration supported by your model and backend, then rerun the evaluation and compare its results with the original precision. The documented guidance does not establish a universal memory saving or score change: both depend on the particular setup.
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Tune vLLM overhead and caches where relevant
Reduce CUDA graph capture overhead
vLLM’s memory guide notes that CUDA graph capture uses extra GPU memory. It documents reducing capture sizes or setting enforce_eager=True to avoid that capture overhead. These changes may affect inference speed, so compare runtime as well as memory on your workload.
Review CPU cache settings
For the CPU backend, vLLM documents controls for CPU KV-cache space. Its documented default for VLLM_CPU_KVCACHE_SPACE is 4 GiB; this is a default setting, not a general memory-saving estimate or a recommendation for every workload. For multimodal models, vLLM also documents a processor-cache control.
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Limit multimodal input capacity only if the task allows it
For multimodal models, vLLM offers limits on multimodal items per prompt and supports disabling unused modalities. These controls are relevant only when the model and task use multimodal inputs. Restricting accepted inputs changes the workload’s scope, so do not apply it if the evaluation depends on those inputs.
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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Choose a local evaluation backend for the workload
lm-evaluation-harness supports Hugging Face Transformers and vLLM, as well as evaluation through a llama.cpp server for GGUF models. The available documentation does not establish one as the lowest-memory choice for every model, task and machine. Treat backend selection as a setup-specific comparison, not a guaranteed shortcut.
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Compare changes without losing track of validity
Make one change at a time and keep the model, task and other evaluation settings equivalent. For each run, log:
- Peak GPU memory and CPU RAM use.
- Runtime or throughput.
- Evaluation results and any change to precision or accepted input lengths.
This makes the tradeoff visible: less concurrency or a lower context ceiling constrains the workload; quantization lowers precision; and graph settings can affect speed. The vLLM and lm-evaluation-harness documentation provides no broadly applicable figure for how many gigabytes or what percentage any one intervention will save.
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