The fastest way to reduce memory use is to find out where the model is loaded, then remove idle models, limit concurrent requests, right-size the context window, and use a smaller or more heavily quantized model when necessary. System RAM and GPU VRAM are separate resources, so confirm the bottleneck before changing settings.
For Ollama, start with ollama ps, stop anything you are not using, and check the model’s CPU/GPU split. For LocalAI, read the backend log for the actual load error. The safest optimization is the smallest change that keeps your coding quality and context sufficient.
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Understand which memory is full
A local coding model can use the computer’s system RAM, a graphics card’s VRAM, or both. Model weights, the conversation’s key/value (K/V) cache, and temporary working buffers all occupy memory while a request is running. Freeing system RAM will not solve a pure VRAM shortage, and reducing GPU use may move work into system RAM instead.
Check placement in Ollama
Run:
ollama psThe process-placement column gives the first useful diagnosis:
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| What you see | Where the model is loaded | What to change first |
|---|---|---|
100% CPU |
System RAM and CPU | Unload idle models, reduce context or parallel requests, and consider a smaller quantization. |
100% GPU |
GPU VRAM | Free VRAM held by other applications, use a smaller model or quantization, or reduce GPU offload. |
| A CPU/GPU split | Both system RAM and VRAM | Decide which side is limiting performance, then adjust layer placement or model size. |
The command shows what is loaded now; it does not measure every application on the machine. Use your operating system’s memory monitor as well when diagnosing total RAM pressure.
Separate model memory from storage
Deleting model files can recover disk space, but it does not reduce the memory used by a model that is already loaded. Runtime memory is released by unloading the model or ending the process. A larger SSD therefore does not fix an out-of-memory error by itself.
Release memory immediately
Stop an idle Ollama model
- Run
ollama psand copy the model name exactly. - Unload it with
ollama stop <model>. - Run
ollama psagain to confirm that it is no longer resident.
Ollama’s current FAQ says models remain in memory for five minutes by default after becoming idle. Stopping a model releases that runtime allocation immediately; it does not remove the model from your local library.
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If an application calls Ollama through its API, send keep_alive as zero when you want the model unloaded after the response:
{"keep_alive":0}This is useful for a script that runs occasionally rather than a coding assistant that should answer continuously. For a longer or shorter idle retention policy, adjust Ollama’s OLLAMA_KEEP_ALIVE setting. A short retention period limits idle memory, while a longer period avoids the reload delay between requests.
Avoid keeping several models resident
Switching between a chat model, a code model, and an embedding model can leave more than one allocation active. Stop the models you are not using before launching a large coding session. If an editor or helper service automatically reconnects, its next request may load the model again; check ollama ps after closing the client to identify that behavior.
Limit simultaneous work
Every active request needs context and temporary buffers. Ollama documents OLLAMA_NUM_PARALLEL as the per-model parallel-request control, with a documented default of 1. Its FAQ summarizes the relationship this way: required RAM scales by OLLAMA_NUM_PARALLEL * OLLAMA_CONTEXT_LENGTH. Raising either value can increase the allocation even when the model weights themselves have not changed.
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Use one request at a time when memory is tight
Keep OLLAMA_NUM_PARALLEL at 1 unless you have a deliberate reason to serve concurrent requests. Multiple editor panes, background indexing prompts, terminal agents, or two users sharing one local server can otherwise multiply context allocations. If you need concurrency, increase it gradually and watch peak RAM and VRAM rather than relying on the value that worked for a smaller model.
Find hidden concurrent clients
- Close duplicate editor windows and terminal agents.
- Pause background code-review or indexing tasks while testing a memory change.
- Check whether a web interface and an IDE are connected to the same Ollama server.
- Run
ollama psduring a request and again after it finishes; a model that remains loaded is subject to the idle-retention policy.
Concurrency is different from model size. A small model serving several long requests can use more memory than the same model serving one short request.
Right-size the context window
Context length is one of the most effective memory controls because the K/V cache grows with the amount of conversation and code supplied to the model. Lower it when the task does not need a long repository history, but do not cut it below the requirement of the integration you are using.
When a smaller context is appropriate
- Single-file edits with a short prompt.
- Small bug fixes where only a few functions are relevant.
- Short-lived scripts that do not need a complete project history.
- Machines that begin swapping or hitting an out-of-memory condition during long prompts.
Reduce context in the model or editor configuration, then test a representative task. A lower limit can cause older messages or files to be truncated, so the memory saving is not free.
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Ollama’s current VS Code integration instructions say to set context to at least 64k for that workflow, and its coding-tools announcement dated January 23, 2026 recommends at least 64,000 tokens for the listed coding integrations. Those are integration-specific recommendations, not a universal minimum for every local assistant. The integration may display a maximum context that differs from the smaller allocation actually used at runtime.
Confirm the context requirement for your exact editor, model, and task before reducing it. If the tool expects repository-scale context, a very small value can produce apparently plausible but incomplete edits because relevant files fall outside the window.
Reduce the amount of code sent without changing the model
When the editor permits it, select only the relevant files or symbols, remove duplicated tool output, and start a fresh conversation after a large task is complete. These actions reduce the tokens that reach the K/V cache while preserving the model’s available maximum. They are especially useful when a coding workflow needs a long maximum context for occasional repository-wide work but normally handles focused changes.
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Use Flash Attention and K/V-cache quantization when supported
Ollama says Flash Attention can significantly reduce memory use as context grows and is enabled automatically when the selected backend and devices support it. Verify that the feature is actually available on your model and hardware; automatic support is not the same as guaranteed support on every combination.
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| K/V-cache setting | Approximate memory compared with f16 |
Documented trade-off |
|---|---|---|
f16 |
Baseline | Highest cache precision and memory use. |
q8_0 |
About one-half | Small precision loss. |
q4_0 |
About one-quarter | Small-to-medium loss, potentially more noticeable at larger contexts. |
These settings are global in the documented Ollama configuration, so changing them can affect every model using that installation. Test code completion, refactoring, and long-context retrieval after changing the cache type. A lower-memory cache is worthwhile only if the resulting quality remains acceptable for your work.
Choose a smaller or more quantized model
Change model size only after measuring the workload
A smaller model usually lowers weight memory, but it can also change code quality, instruction following, and the amount of context that is practical. Compare models on your own tasks rather than assuming a universal ranking: the available documentation describes memory controls, not a controlled cross-model benchmark on identical hardware.
Use a smaller model when your work is routine enough that the quality difference is acceptable, or when the current model cannot leave enough memory for the required context. Keep the larger model for tasks where its reasoning or repository understanding materially improves the result.
Use a lower-weight quantization carefully
Quantization stores model weights with fewer bits and can make an otherwise un-loadable model fit. The memory saving can come with slower responses, lower accuracy, or more visible mistakes, depending on the model, runtime, and hardware. Treat the quantization level as a task decision: test a real fix, refactor, and completion rather than judging only by whether the model starts.
Do not confuse model quantization with K/V-cache quantization
Model quantization changes the stored weights. K/V-cache quantization changes the memory used by the current context. You can use one, the other, or both, and their quality effects are not identical. If the model loads successfully but memory grows during a long conversation, the cache—not the weights—is the more likely target.
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Adjust CPU/GPU layer placement in LocalAI
LocalAI’s troubleshooting guidance recommends smaller quantization, a shorter context, offloading fewer layers to the GPU, or freeing VRAM held by other processes when the model and K/V cache do not fit.
When to offload fewer layers
If the error is a GPU out-of-memory failure, reduce the number of layers placed on the GPU. The remaining layers run through system memory and the CPU, so VRAM use falls but system RAM use and response time may rise. Confirm the new placement in the backend log or runtime status instead of assuming the change worked.
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- Close games, 3D applications, browser tabs using GPU acceleration, and other model servers.
- Stop unused LocalAI or Ollama instances.
- Retry after the operating system reports that the graphics allocation has been released.
If the model still fails, inspect the specific LocalAI backend log. A generic interface message can hide whether the failure came from insufficient VRAM, an invalid layer setting, an unsupported quantization, or a context allocation.
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Change one variable at a time so you know which adjustment solved the problem and which one affected quality.
- Record a baseline. Note the model, quantization, context setting, parallel-request setting, CPU/GPU placement, peak system RAM, peak VRAM, response time, and the quality of one representative coding task.
- Confirm the bottleneck. In Ollama, use
ollama psand the operating system monitor. In LocalAI, capture the backend log during the failed or slow load. - Unload idle allocations. Run
ollama stop <model>for unused Ollama models, or stop unused LocalAI services. - Set concurrency to one. Keep
OLLAMA_NUM_PARALLELat its documented default of 1 while diagnosing. - Match context to the task. Lower it for focused edits only after checking the editor’s requirement. Preserve at least the context recommended by an integration that depends on repository-wide prompts.
- Enable supported cache savings. Let Flash Attention operate when supported, then test
q8_0orq4_0K/V caching if the quality trade-off is acceptable. - Reduce model or GPU load if necessary. Choose a smaller quantization, smaller model, or fewer GPU-offloaded layers, then rerun the same coding task.
- Keep the first configuration that meets all constraints. A configuration that fits but truncates essential context or produces unusable code is not a successful optimization.
Configuration strategies by situation
| Situation | Recommended order of changes | Main risk |
|---|---|---|
| System RAM is exhausted while the GPU is mostly unused | Stop idle models, set one parallel request, shorten context, then select a smaller or more quantized model. | Moving work from GPU to CPU can increase system-RAM use and latency. |
| GPU VRAM is exhausted but system RAM is available | Free other GPU processes, reduce GPU-offloaded layers, shorten context, then use a smaller model or quantization. | CPU fallback can be substantially slower. |
| Memory rises only during long conversations | Reduce context, avoid unnecessary parallel requests, and consider Flash Attention with K/V-cache quantization. | Older code may fall out of context, and lower-precision cache can affect long-context quality. |
| The model loads but the editor quality falls | Restore the required context, test a higher-precision cache, or move back to a larger model for that task. | Memory savings may have removed information or precision the workflow needs. |
| Several users or tools share one server | Keep concurrency controlled, unload models after occasional API calls, and avoid retaining multiple models. | Requests may queue or reload models more often. |
Recognize common failure modes
The process is killed or the operating system starts swapping
This indicates total system-memory pressure, even if the model itself appears to fit. Stop idle models, reduce parallel requests, shorten context, and close unrelated memory-heavy applications. If the model remains too large, use a smaller quantization or model.
The GPU reports out of memory
Check for other GPU consumers first. Then reduce context or GPU-offloaded layers, and verify the actual placement in the runtime log. A CPU/GPU split can solve the allocation failure but may shift the pressure to system RAM.
Responses become much slower after a memory change
Fewer GPU-offloaded layers, a smaller cache precision, or CPU execution can all alter speed. Compare the same prompt before and after the change. If the response time is unacceptable, restore some GPU layers or use a model that fits without aggressive offloading.
The assistant forgets files or earlier instructions
The context limit is probably too low for the integration’s prompt. Restore a larger context, reduce unrelated material sent with each request, or use a workflow that focuses the prompt on relevant files. Do not solve a context problem by continually increasing parallel requests.
A stopped model appears again
Another client has likely sent a new request, or the normal idle-retention period has not been distinguished from a fresh load. Check ollama ps immediately after stopping and again after closing connected editors and services.
Quantization fixes memory but code quality drops
Move back from q4_0 to q8_0 or f16 for the K/V cache, or select a higher-precision model quantization. Test the change on the coding tasks that exposed the problem; quality loss can be more noticeable at larger contexts.
Know when hardware is the right answer
Software settings reduce peak use; they do not create additional capacity. More system RAM can help a CPU-loaded or split model, while more VRAM helps a GPU-loaded model. Neither upgrade reduces the model’s inherent allocation. A storage upgrade addresses model-file capacity, not runtime RAM or VRAM.
Consider hardware only after confirming placement, stopping idle processes, controlling concurrency, right-sizing context, and testing a smaller or quantized model. If the required editor workflow genuinely needs a long context and the selected model cannot fit without unacceptable quality or speed, additional capacity may be more practical than further reductions.
How to verify that the change worked
- Capture
ollama pswhile the model is loaded and confirm the CPU/GPU placement changed as intended. - Measure peak system RAM and VRAM during a representative request, not just at startup.
- Check whether a second request causes a new allocation or waits behind the first.
- Confirm that the context still includes the files and instructions required for the task.
- Compare response speed and code quality on the same prompt before accepting the change.
- For LocalAI, save the backend log for a failed load and verify the reported setting rather than relying on a front-end status message.
Ollama’s defaults and integration recommendations are live documentation and can change. The five-minute idle retention is the current FAQ behavior described in the October 2026 documentation snapshot; the 64,000-token coding recommendation comes from the January 23, 2026 launch announcement. Recheck the settings for the version you actually run.
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