Fix local-model slowdowns by first identifying whether the delay or out-of-memory (OOM) error happens while loading the model, processing the prompt, or generating tokens. Those stages use different resources and need different adjustments: a model can fit in GPU memory as weights but still run out of room for its context cache or other runtime allocations.
Identify which stage is slow or failing
Before changing settings, record the model and its parameter size, quantization or precision, runtime and version, CPU and GPU, available system RAM and GPU VRAM, context length, batch size, and concurrent request count. Then note when the problem occurs:
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- Loading: the model takes a long time to load or the process fails as weights are placed in memory.
- Prompt processing: the model loads, but takes a long time to work through the input before responding.
- Token generation: the first token arrives, but subsequent output is slow.
- Later allocation or warm-up: loading succeeds, but the runtime reports OOM while allocating a cache, capturing a graph, or starting inference.
Use the same prompt and output length when comparing a change. There is no meaningful universal tokens-per-second target without specifying both the model and hardware.
If the model takes a long time to load
Separate downloading from loading
A model may still be downloading, or its files may be read from slow shared or network storage. vLLM recommends downloading the model first and using a local model path to separate download time from load time. Its troubleshooting guide also notes that large models on shared filesystems can load slowly: vLLM troubleshooting.
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Check for system-memory pressure and swapping
If system RAM is heavily used, the operating system may swap memory to disk, slowing both the system and model loading. Check system memory use while the model starts. More system RAM may help if measurements confirm a RAM shortage, but it does not increase GPU VRAM.
If the model reports an out-of-memory error
Find the failed allocation in the logs
An OOM during weight loading differs from one during cache allocation or warm-up. Read the runtime logs to identify the phase and allocation that failed; NVIDIA’s NVIDIA NIM performance guide distinguishes weight-loading failures from later memory needs.
Budget for more than model weights
Model weights are only part of the GPU memory requirement. KV cache, activations, communication buffers, CUDA graphs, adapters, and other runtime state also need space. NVIDIA gives Llama 3.1 8B in BF16 at one-way tensor parallelism as an example requiring 16 GB for weights. That is a weight-memory estimate, not a guarantee that the model will run in 16 GB of VRAM.
A rough weight estimate starts with parameter count multiplied by bytes per parameter, but the actual allocation depends on the model representation and runtime. Leave room for the additional allocations, and do not treat a weight estimate as the required total VRAM.
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Match the adjustment to the failure
Depending on the failed allocation and the runtime, options include choosing a smaller model, using lower-memory precision or quantization, reducing context length, reducing batch size or concurrency, freeing GPU memory used by other processes, or placing fewer model layers on the GPU for hybrid CPU/GPU inference. Check the documentation for the installed runtime and test output quality after changing precision or quantization.
If the model loads but responds slowly
Check where the model is running
A model that runs on the CPU, or partly on the CPU, may respond more slowly than one placed fully on the GPU. In Ollama, run ollama ps and inspect its processor column to see where a loaded model is running. The Ollama FAQ documents this check: Ollama FAQ.
In llama.cpp, --gpu-layers sets the maximum number of model layers placed in VRAM. Placement depends on what fits and on the machine and backend; offloading fewer layers can make a model fit, but changes how much work is performed on the CPU. See the llama.cpp CLI documentation.
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Tell prompt processing apart from generation
If the delay is before the first token, prompt processing may be the bottleneck. llama.cpp documents a physical batch-size setting: increasing it may improve prompt-processing throughput but uses more memory. Reduce it when memory is tight; increase it only when prompt processing is slow and memory headroom is available. Its completion documentation also notes that some systems benefit from using more threads for batch processing than for generation. These are workload-dependent tuning options, not guaranteed speedups: llama.cpp completion documentation.
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Reduce context and KV-cache memory when appropriate
Longer context can require more memory for the KV cache. Use only the context your task needs. Ollama’s FAQ documents a default context window of 4096 tokens, but that default should be checked against the Ollama release and settings in use. It describes OLLAMA_CONTEXT_LENGTH and the num_ctx setting for controlling context.
The same FAQ describes these Ollama KV-cache types and their memory trade-offs:
| Cache type | Published memory and precision description |
|---|---|
f16 |
Default cache type. |
q8_0 |
Uses approximately half the memory of f16, with a small precision loss. |
q4_0 |
Uses approximately one quarter of the memory of f16, with a small-to-medium precision loss that may be more noticeable at higher context. |
These are Ollama’s published descriptions, not guarantees for every model or task. Test whether the output remains suitable for your use case. The FAQ also says Flash Attention can significantly reduce memory use as context grows; Ollama enables it automatically on supported backends and devices, so do not assume it is available everywhere: Ollama FAQ.
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For repeated requests, Ollama says a model remains loaded for five minutes by default and its API supports keep_alive controls. Keeping a model resident can avoid reloading it between requests made within that period, but the resident model continues to occupy memory. Other runtimes have their own residency behavior. Check the installed version’s documentation before relying on a particular default: Ollama FAQ.
Choose a fix based on the constrained resource
| Observed constraint | Options to investigate | Trade-off or limit |
|---|---|---|
| GPU VRAM OOM | Reduce context, batch size, or concurrency; use a smaller or lower-memory model representation; free GPU memory; or use supported hybrid CPU/GPU placement. | Lower precision or cache settings can affect output quality. CPU placement may change speed. |
| System RAM pressure or swapping | Reduce memory use from other processes; consider more system RAM if measurements confirm it is the constraint. | System RAM does not add GPU VRAM. |
| Slow model-file loading | Separate downloading from loading and check whether model files are on shared or network storage. | Storage throughput affects loading; it does not resolve a GPU VRAM shortage. |
| Slow prompt processing with memory headroom | Test a larger physical batch size or the runtime’s batch-processing thread settings. | Larger batches use more memory and may not help token generation. |
Runtime controls and defaults vary by framework, version, and backend. Check the current documentation for the software you have installed, then change one relevant setting at a time so the effect is clear.
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