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If a local AI note app is slow or reports an out-of-memory error, first identify whether the delay is during model loading, first-token generation, or ongoing generation. Then check memory use and GPU detection, and test with a smaller model or shorter context before changing hardware. A note app may rely on a separate runtime such as Ollama, so the bottleneck may be outside the note app itself.
Identify where the slowdown or failure occurs
“Slow” can describe different problems, and each points to a different fix. Record your operating system, note app and plugin, model runtime, model identifier or size, context setting, and when the delay occurs. Note whether the app is sluggish while loading the model, waiting for the first token, or generating the rest of a response.
- Slow model loading: The runtime may need to load model weights into memory. If the first request after a pause is slow but later requests are faster, the model may have been unloaded between requests.
- Slow generation: Check whether the runtime is using the intended GPU and whether memory pressure is forcing the system to rely more heavily on other resources.
- Out of memory: The model, context, or number of simultaneous requests may exceed available RAM or GPU memory.
- Disk full: If downloads fail or storage fills up while inference otherwise works, the problem may be model-file storage rather than working memory.
To narrow the cause, compare the same task using a short prompt and a smaller model. If that works better, investigate model and context fit before considering a hardware purchase.
Check RAM, GPU memory, and GPU detection
Model loading requires memory for weights and other parameters. LM Studio explains that loading a model typically allocates memory for those items in the computer’s RAM. The amount available on your device—and GPU memory when the runtime uses a GPU—limits which workloads can run comfortably.
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Watch RAM and GPU memory while the model is loaded, then confirm that the runtime detects the GPU you expect it to use. Do not assume an app needs a more powerful computer until you have checked that detection and the relevant logs. Ollama’s troubleshooting documentation covers server logs, GPU discovery, driver issues, and container access checks: Ollama troubleshooting.
For context, LM Studio’s current requirements documentation recommends 16 GB or more of RAM for Apple Silicon Macs; it says an 8 GB Mac may work with smaller models and modest context sizes. For Windows, LM Studio recommends at least 16 GB of RAM and 4 GB of dedicated VRAM. These are LM Studio recommendations for its listed platforms, not universal minimums for every local runtime or model: LM Studio system requirements.
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Reduce memory pressure from context and parallel requests
A model’s memory use is not determined by its weights alone. Context length and simultaneous requests also matter. Ollama documents that RAM requirements for parallel requests scale with parallelism multiplied by context length. If the model loads but requests fail or slow down under a larger workload, reduce one or both variables and test again.
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Lower the context setting and retry the same task. A shorter context can reduce memory use, but it also means the model can consider less surrounding material. In an Obsidian setup using the Hephaestus plugin with Ollama, the plugin passes context length as num_ctx; its documentation notes that context size uses video memory. That behavior is specific to the plugin and is not a general Obsidian setting: Hephaestus plugin documentation.
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Limit simultaneous requests
If multiple requests are running at once, try processing fewer concurrently. This is especially useful when a model works for one request but runs out of memory when several requests overlap.
Evaluate cache-memory options carefully
Ollama documents Flash Attention and KV-cache quantization as options that can reduce memory use. Cache quantization may involve a quality tradeoff, so compare the answers after changing it rather than assuming the reduction is free. See the Ollama FAQ for the relevant configuration options.
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Account for models unloading between requests
Ollama says models remain in memory for five minutes by default. That can make the first request after a pause feel slower because the model may need to load again. If you want to free memory sooner, use ollama stop <model> to unload a model, or set keep_alive to zero for immediate unloading. Ollama also documents controls for preloading models and setting how long they stay loaded in its FAQ.
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Keeping a model loaded can avoid a reload on a later request, but it also keeps memory occupied. Choose based on whether you value quicker repeat requests or need that memory available for other work.
Separate disk capacity from working-memory limits
If model downloads fill your internal drive, Ollama lets you move model storage by configuring OLLAMA_MODELS; its FAQ explains the setting. An external SSD may provide space for model files, but it does not increase RAM or GPU memory and should not be expected to fix out-of-memory errors or slow inference by itself.
Choose a hardware change only after finding the bottleneck
Before upgrading, determine whether the constraint is disk capacity, RAM, GPU memory, GPU detection, model loading, or the workload’s context and concurrency. Then check whether your device’s memory or GPU can be upgraded and whether the change fits the runtime and operating system you use. The LM Studio figures above are guidance for that app and its listed platforms; they do not establish one best hardware configuration for all local AI note apps.
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