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1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problemsWhen a local AI note app slows down or fails with an out-of-memory error, the cause is usually somewhere in the chain between the note app, its model runtime, and the hardware—not necessarily the notes themselves. First identify whether the delay happens while the model loads, before the first token, or during generation. Then check memory use and GPU detection before changing settings or considering a hardware upgrade.
Start by locating where the slowdown happens
A note app may send work to a separate local model runtime, so its apparent performance does not by itself identify the bottleneck. Record the operating system, note app and plugin, runtime, model name or size, context setting, and when the slowdown occurs.
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- Slow only after the app or model has been idle: the runtime may need to load the model again.
- Slow before the first token on every request: check model loading, memory pressure, and whether the runtime detects the intended GPU.
- Slow while generating, or an out-of-memory error on longer prompts: investigate model size, context length, and simultaneous requests.
- Downloads fail or fill the drive, but inference is otherwise acceptable: this points to storage capacity rather than working memory.
For a controlled first check, try a short prompt with a smaller model and compare it with the current workload. If the smaller, shorter test behaves better, investigate model and context fit before assuming the device needs an upgrade. Model loading allocates memory for weights and other parameters; available system RAM and, when applicable, GPU memory limit what can run comfortably. LM Studio’s system requirements documentation explains its own recommendations, while its getting-started documentation describes model loading.
Check memory use, context length, and parallel requests
Check available system RAM and GPU memory while the model is loaded, not just when the app is idle. A model can fit when loaded but leave too little headroom for a long prompt or other simultaneous requests.
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Context length and concurrency both affect memory demand. Ollama documents that RAM needs for parallel requests scale with parallelism multiplied by context length. If memory is tight, try reducing the context length or the number of concurrent requests, then repeat the same test prompt. Ollama’s FAQ also documents Flash Attention and key-value (KV) cache quantization options that can reduce memory use. Treat cache quantization as a trade-off to evaluate: compare answer quality after changing it rather than assuming it is a free performance improvement.
For Obsidian users, the app or plugin may pass context settings to a separate runtime. The Hephaestus plugin documentation says its context length is passed to Ollama as num_ctx, that context size uses video memory, and that its interface can report GPU memory on supported setups. These details describe that plugin workflow, not a built-in feature shared by every Obsidian setup. See the Hephaestus plugin documentation.
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Verify GPU detection before changing hardware
If you expect GPU acceleration, confirm the runtime actually sees and can use the intended GPU. An app using CPU can be a detection, driver, or access problem rather than proof that the hardware is too weak. Check the runtime’s logs for GPU discovery and related errors. Ollama documents platform-specific log locations and troubleshooting checks for drivers and container access in its troubleshooting guide.
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Handle slow first requests after idle time
A pause followed by a slow first response can be a model-loading delay rather than slow generation. Ollama says models stay in memory for five minutes by default before unloading. If this pattern fits, compare the first request after a pause with a second request made while the model is still loaded. Ollama’s FAQ describes the default and the controls for model lifetime and preloading.
When memory is the limiting factor, unload models you are not using. In Ollama, run ollama stop <model> to stop a loaded model, or set keep_alive to zero for immediate unloading. If you prefer faster repeat use and have enough memory, adjust the keep-alive behavior using the documented controls rather than assuming every pause requires a reload.
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Separate disk-space problems from memory problems
Model files can fill a drive without causing an inference slowdown. If downloads are the problem, Ollama supports changing the model storage location with the OLLAMA_MODELS environment variable; consult its FAQ for the configuration details. An external SSD may provide more room for model files, but moving files to external storage does not add RAM or GPU memory and does not, by itself, make inference faster.
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Decide whether a hardware upgrade is justified
Consider an upgrade only after identifying the constrained resource—system RAM, GPU memory, GPU access, or storage—and checking whether your device can be upgraded. Recommendations also depend on the runtime and platform. For example, LM Studio’s current system-requirements documentation recommends 16 GB or more of RAM for Apple Silicon Macs, while noting that 8 GB may work with smaller models and modest context sizes. For Windows, it recommends at least 16 GB of RAM and 4 GB of dedicated VRAM. These are LM Studio recommendations for the listed platforms, not universal minimums for every local model app. Check LM Studio’s requirements for the current details.
Use the diagnosis to compare possible fixes: reducing context or concurrency can lower memory demand; unloading models frees memory between sessions; resolving GPU detection addresses access rather than capacity; and moving model files addresses disk space. Where a change reduces context or alters cache quantization, test whether answer quality remains acceptable for your notes.
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