The Tool Desk
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First, identify what is slow
Run the same model on the same short writing prompt while noting when the wait occurs. Model loading, the time before the first token, ongoing token generation, and work on a long document can have different causes. Change one thing at a time and compare against that fixed prompt; otherwise, it is difficult to tell whether a change helped.
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- Slow model loading: Focus first on model and memory allocation.
- Long wait before the first token: Consider prompt processing, context size, and whether this is a cold start.
- Slow token generation: Check device allocation and, for llama.cpp specifically, thread settings.
- Slow only on long documents: Test whether the task can use a smaller context without omitting needed material.
Do not judge performance from a single first run. In llama.cpp’s OpenVINO backend, the first inference token can take longer while the runtime converts the model to an OpenVINO graph; the project says later tokens and runs are faster. That explanation is specific to this backend, not a general rule for every local model. llama.cpp OpenVINO backend documentation
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Check whether the model is using the expected hardware
A model may be running fully on a CPU, fully on a GPU, or split across both. Check actual allocation rather than assuming that a detected GPU is doing the work. Partial CPU/GPU allocation is a useful clue to investigate, but it does not by itself prove why generation is slow.
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Ollama
With the model loaded, run ollama ps and inspect the Processor column. Ollama documents that this reports whether the model is allocated to GPU, CPU, or both. Its FAQ describes the display.
llama.cpp
Check the startup output for the GPU-offload diagnostics described in llama.cpp’s token-generation performance guide. These lines help establish whether layers are being offloaded; they are more useful than assuming GPU use from the machine’s specifications alone.
LM Studio
Review the model’s load configuration and GPU settings. The available controls and their labels can vary by version; LM Studio documents load options in Load a model and inference controls in Configuring the Model.
Reduce context only as far as the task allows
A larger context is not automatically better for a short editing request. It can consume more memory and may reduce performance. Test a smaller context that still accommodates the instructions, the text being edited, and any relevant surrounding material. Compare results using the same prompt and model.
Ollama documents configuring context length in its FAQ. LM Studio exposes context length among its model load options. The OpenVINO backend documentation warns that a very large resolved default context can lower performance and shows passing an explicit -c value as a way to set it. Do not copy an example context value without checking what your prompt and model need.
If memory is tight, Ollama also documents KV-cache options. Its FAQ identifies f16 as the default cache type and says cache quantization can reduce memory when Flash Attention is enabled. This is a memory-management option, not a demonstrated way to improve writing accuracy.
Tune CPU threads only for the runtime that supports the setting
If token generation is unusually slow in llama.cpp, its performance guide suggests testing one thread with -t 1. If that improves generation, increase the thread count cautiously, using physical cores as a starting reference, and measure again. The guide warns that too many threads can oversaturate the CPU; it recommends increasing until a bottleneck appears and then reducing the count.
These are llama.cpp-specific instructions. Do not assume that its flags or thread behavior apply to Ollama, LM Studio, or another runtime; check that runtime’s own documentation for supported controls.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Improve suggestion quality by checking the model and request first
Before changing sampling settings, verify that the intended model is selected and that the application is using the expected prompt template and instructions. Then make the editing task concrete: provide the text, say what kind of edit you want, and state constraints such as “preserve the meaning” or “return only suggested edits.” A vague request can produce unsuitable suggestions even when the model is operating as configured.
If the output still needs adjustment, change one inference parameter at a time and compare against the same passage and task. LM Studio documents controls including temperature, maxTokens, topP, context length, and GPU configuration in Configuring the Model. These controls shape generation or resource use; the documentation does not establish a setting that reliably makes writing suggestions more accurate across models.
Compare runtimes or hardware only after troubleshooting
If you are considering a different runtime, compare it on a repeatable editing task with the same model, quantization, prompt, context, and device. Record the first-token delay, generation rate, memory use, context capacity, backend support, and whether the suggestions meet your editing goal. A speed figure is meaningful only alongside the model, runtime version, hardware, context, workload, and measurement method.
The cited project documentation does not provide a controlled cross-runtime leaderboard. The llama.cpp OpenVINO page says accuracy validation and performance optimization are ongoing, and notes that CPU, GPU, and NPU tool coverage is not uniform. Treat comparisons accordingly rather than assuming one runtime is universally faster or more accurate.
Do not buy RAM or a GPU based only on a slow response. First establish the device, selected model, measured allocation, and actual bottleneck; then check that a specific upgrade is compatible. The diagnostics above can narrow the problem, but they cannot identify a suitable part for an unspecified computer.
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