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To reduce memory use immediately, unload idle models, limit concurrent requests, choose the smallest context that still supports your coding task, and verify whether the pressure is on system RAM, GPU VRAM, or both. In Ollama, ollama ps shows where each loaded model resides and ollama stop <model> releases it immediately. If memory is still tight, use a smaller or more heavily quantized model, reduce GPU layer offloading, enable supported Flash Attention and K/V-cache quantization, and close other workloads competing for RAM or VRAM.
First identify what is actually full
“RAM use” can mean two different resources. System RAM is the memory used by the operating system and CPU inference. GPU VRAM is dedicated graphics memory used when layers or caches are placed on a graphics card. A local model can occupy either one or both.
Do not choose a fix until you know the placement. In Ollama, run:
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ollama psThe processor column reports whether a model is using 100% CPU, 100% GPU, or a split of CPU and GPU. A CPU-only load consumes system RAM; a GPU-only load primarily consumes VRAM; a split load can exhaust either resource and may also incur transfer overhead. Your operating system’s memory monitor can confirm system-RAM pressure, while the GPU vendor’s monitoring utility can show VRAM allocation.
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A model that is not currently generating text can still occupy memory. Loaded weights and the key/value (K/V) cache remain allocated until the runtime unloads them, so an apparently idle coding assistant may be the reason another application cannot start.
Release memory that is already occupied
Unload an idle Ollama model
- List loaded models and placement with
ollama ps. - Identify the model you no longer need.
- Unload it with
ollama stop <model>.
The model is removed from memory immediately. Ollama’s documented default is to retain an idle model for five minutes before unloading it, so waiting may eventually free memory, but stopping it is deterministic and faster when you need capacity now.
Unload after every API response when appropriate
For a short script or one-off automation, send keep_alive: 0 in the request so Ollama unloads the model after responding. This minimizes idle retention but makes the next request pay the model-loading cost again. It is usually a poor choice for an interactive editor session that sends frequent prompts.
Change the idle-retention policy
The OLLAMA_KEEP_ALIVE setting controls how long models remain loaded. A shorter value reduces the time memory is occupied between requests; a longer value avoids repeated reloads. Set it according to your workload rather than treating either extreme as universally best.
Stop parallel work from multiplying allocation
Running several generations at once can consume substantially more memory than one request. Ollama’s OLLAMA_NUM_PARALLEL setting controls the number of parallel requests per model, with a documented default of 1. If you do not need simultaneous completions, leave it at 1 and avoid launching duplicate editor, terminal, or background-agent requests.
Ollama documents the relationship this way: “Required RAM will scale by OLLAMA_NUM_PARALLEL * OLLAMA_CONTEXT_LENGTH.” The multiplication is important. A context that is manageable for one request can become too large when several requests each need their own active context and cache.
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Practical concurrency checklist
- Set
OLLAMA_NUM_PARALLEL=1on a memory-constrained machine unless you have measured headroom. - Do not keep multiple large models loaded merely because switching between them is convenient.
- Disable or pause background indexing, autonomous agents, and queued completions while diagnosing memory pressure.
- Test one request at a time before increasing concurrency.
Lower concurrency can reduce throughput. If your machine has ample memory and your priority is serving several users or agents, a higher value may be worthwhile; measure peak RAM and VRAM rather than assuming the setting is free.
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Choose a context length that matches the task
Context length is the maximum amount of prompt and conversation history the model can consider. Larger contexts require more memory, especially for the K/V cache. A coding tool that repeatedly sends repository files, terminal output, and chat history can therefore use far more memory than a small completion prompt.
Reduce context without destroying useful code awareness
- Start with the editor or runtime’s current context setting.
- Reduce it to the smallest value that still includes the files, symbols, and recent instructions needed for the task.
- Repeat a representative task: navigation, a multi-file change, and a test-fix cycle.
- Watch peak system RAM and VRAM, response quality, and whether the model loses required definitions.
Do not reduce context blindly. Truncating the beginning of a long prompt can remove architecture notes or declarations that the model needs, while truncating the end can remove the latest error output. Configure the integration’s truncation or file-selection behavior when possible so that the saved memory represents less useful material rather than simply cutting at an arbitrary boundary.
Account for coding-integration requirements
Ollama’s current VS Code integration instructions warn that a displayed maximum supported context can differ from the smaller allocation actually used at runtime. The instructions for that workflow say to set context to at least 64k, and Ollama’s January 23, 2026 coding-tools announcement recommends at least 64,000 tokens for the listed coding integrations. Those are recommendations for the named integrations, not a universal minimum for every local assistant, model, or repository.
If your integration expects long context, dropping below its documented requirement can cause missing-file errors, truncation, or poorer code changes even though memory usage falls. Check the integration’s own current settings and test a realistic repository before settling on a lower value.
Why context can dominate memory
Model weights are a relatively stable allocation once loaded. The K/V cache grows as the active prompt grows, so a model that fits comfortably for short questions may fail during a long repository analysis. Parallel requests multiply this pressure. This is why reducing both context and concurrency often succeeds where changing only one setting does not.
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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. It is automatic when the selected backend and devices support it, so first make sure you are using a supported model/runtime and that the feature has not been disabled by a custom configuration.
With Flash Attention enabled, Ollama documents optional K/V-cache quantization:
| Cache type | Approximate memory versus f16 | Documented trade-off |
|---|---|---|
f16 |
Baseline | Highest cache precision and memory use among these options |
q8_0 |
About one-half | Small precision loss |
q4_0 |
About one-quarter | Small-to-medium loss; may be more noticeable at larger contexts |
These are approximate cache-memory figures, not guarantees for total process memory. The settings are global in the documented Ollama configuration, and quality impact depends on the model and task. A code-completion benchmark that passes with q4_0 does not prove that a difficult refactoring or debugging session will behave identically.
How to evaluate cache quantization safely
- Keep the model, prompt set, and context length constant.
- Run representative tasks such as symbol lookup, a multi-file edit, and test-error diagnosis.
- Compare peak memory, latency, compilation or test success, and the amount of correction needed.
- Use
q8_0first when you want a conservative reduction; tryq4_0only if the additional savings justify the possible quality loss.
Pick a smaller or more aggressively quantized model
Model weights are often the largest fixed allocation. A smaller parameter count or lower-bit weight quantization can make the difference between a model that loads and one that fails before generation starts. The trade-off is model capability, output quality, and sometimes speed; a smaller model may require more retries or produce less reliable multi-file changes.
Choose by task rather than by parameter count alone. A compact model may be sufficient for completion and simple explanations, while repository-wide planning, unfamiliar frameworks, and long debugging chains may need a larger model or longer context. Compare configurations using the same tasks and record:
| Measure | What to record |
|---|---|
| Peak system RAM | Highest resident memory during loading and generation |
| Peak VRAM | Highest GPU allocation, including the K/V cache |
| Quality | Tests passed, edits required, and factual or API errors |
| Speed | Time to first token and generation rate on your hardware |
| Usable context | Largest context that completes reliably without swapping or out-of-memory errors |
| Idle behavior | Whether and when the runtime unloads the model |
There is no universal ranking because model architecture, quantization, runtime, drivers, and hardware all change the result. Use the model that meets your coding acceptance criteria within your measured memory budget.
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Adjust GPU layer offloading when VRAM is the bottleneck
LocalAI’s troubleshooting guidance recommends offloading fewer layers to the GPU when the model and K/V cache do not fit in VRAM. Moving some layers back to the CPU increases system-RAM use and can reduce speed, but it may prevent a GPU out-of-memory failure. Conversely, if system RAM is the constrained resource and VRAM is available, more GPU offloading may help.
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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 problemsLayer placement is a balance, not a quality setting by itself. Changing it can alter latency and memory transfers while leaving model weights and context requirements otherwise unchanged. After each adjustment, inspect the runtime’s load report and monitor both resources during a long prompt, not only during startup.
Free competing memory before changing the model
Close applications that hold large allocations: browsers with many tabs, virtual machines, containers, game engines, video editors, data-science notebooks, and other model servers. On a GPU, terminate processes that reserve VRAM even if they are not visibly rendering. A model can fail because another process has fragmented or reserved the last available block.
Do not confuse storage capacity with runtime memory. Moving model files to a larger drive can solve a disk-space error, but it does not reduce the RAM or VRAM required while the model is running. Likewise, adding peripherals does not change inference memory. A RAM or GPU-memory upgrade adds capacity; it does not make a given model intrinsically smaller.
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The model loads, then the operating system becomes unresponsive
- Stop idle models with
ollama stop <model>. - Set parallel requests to 1.
- Lower context length in small steps.
- Close competing applications and check for swapping.
- Try a smaller or more heavily quantized model.
The GPU reports out of memory
- Run
ollama psto confirm whether the model is fully or partly on the GPU. - Stop other GPU workloads.
- Reduce context and concurrency, because the K/V cache also occupies VRAM.
- Enable supported Flash Attention and consider
q8_0orq4_0K/V-cache quantization. - In LocalAI, offload fewer layers to the GPU.
The model fails during loading
Read the backend log for the specific load error. LocalAI’s troubleshooting guidance emphasizes inspecting backend logs instead of relying on a generic interface message. The log can distinguish insufficient VRAM, a CPU-memory allocation failure, an unsupported quantization, and a driver or backend problem; each requires a different fix.
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Lower memory use can move layers from GPU to CPU, increase reloads, or force more frequent context truncation. Check whether the model is now split between CPU and GPU, whether OLLAMA_KEEP_ALIVE is causing repeated loads, and whether the chosen context is triggering swapping. Restore one change at a time until you identify the smallest reduction that preserves acceptable latency.
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Code quality falls after an aggressive reduction
Increase context before increasing model size if the failure is missing repository information. If the model sees the required files but makes poorer edits, revert K/V-cache quantization from q4_0 to q8_0 or f16, then compare again. If quality remains inadequate, select a larger or differently quantized model and reduce concurrency elsewhere to make room.
A repeatable low-memory setup procedure
- Measure the baseline. Record system-RAM and VRAM usage while the model loads and while it handles a long, representative coding request.
- Confirm placement. Use
ollama psand note CPU, GPU, or split allocation. - Unload what is idle. Stop unused models and set an appropriate idle-retention policy.
- Serialize requests. Keep
OLLAMA_NUM_PARALLELat 1 during diagnosis. - Trim context carefully. Lower the integration’s context only after checking its documented requirement; preserve enough history for your repository workflow.
- Reduce cache cost. Use supported Flash Attention and test
q8_0, thenq4_0if necessary. - Change the model or placement. Try a smaller weight quantization or offload fewer layers when VRAM is full.
- Retest the same tasks. Keep prompts and repository state constant so quality and speed comparisons are meaningful.
Change one variable at a time until you know which adjustment solved the problem. Combining every reduction at once makes it impossible to tell whether a quality loss came from context truncation, cache quantization, a smaller model, or CPU offloading.
How much memory should you plan for?
There is no single RAM number that applies to every local coding assistant. Required memory depends on model weights, quantization, context length, parallel requests, cache type, layer placement, runtime overhead, and other applications. Ollama’s January 23, 2026 coding-tools announcement gives an example of about 23 GB of VRAM for glm-4.7-flash at a 64,000-token context. That is an example for that model, quantization, context, and setup—not a general requirement for all models.
Plan with headroom rather than matching a model’s advertised size exactly. Loading may require temporary allocations, and a long prompt can expand the K/V cache after the weights are resident. A configuration that succeeds on a short test may still fail when an IDE sends a full repository excerpt or several concurrent requests.
When a hardware upgrade is the right answer
Software changes are preferable when the goal is to reduce current usage, but they cannot remove the model’s fundamental requirements. If a smaller model, lower context, serialized requests, cache quantization, and adjusted layer placement all fail your quality or latency requirements, additional system RAM or VRAM adds capacity. It is a capacity decision, not a memory-optimization technique.
Before buying hardware, verify which resource is limiting and measure peak use during the actual workflow. More system RAM will not fix a GPU-only out-of-memory error, and a larger GPU will not help if CPU memory or system-wide swapping is the constraint.
What to expect from changing each control
| Control | Usually reduces | Possible cost | Best first use |
|---|---|---|---|
| Unload idle model | Resident weights and cache | Reload delay next time | Immediate relief |
Lower OLLAMA_NUM_PARALLEL |
Concurrent request and cache allocation | Less throughput | Machines serving one user or agent |
| Lower context length | K/V-cache memory | Less repository history | Short prompts and focused files |
| Flash Attention | Growth of attention memory at longer contexts | Requires backend/device support | Long-context workflows |
q8_0 K/V cache |
Cache memory to about half of f16 | Small precision loss | Conservative cache reduction |
q4_0 K/V cache |
Cache memory to about one-quarter of f16 | Small-to-medium quality loss | Severe memory pressure after testing |
| Smaller or more quantized weights | Fixed model allocation | Capability or quality loss | Model does not fit at all |
| Fewer GPU-offloaded layers | VRAM use | More CPU work and possible slowdown | GPU out-of-memory errors |
Keep a configuration that is easy to recover
Save the settings that worked for your machine: model identifier and quantization, context length, parallel-request value, cache type, layer placement, and idle-retention policy. When an IDE update or runtime change alters defaults, you can restore a known-good profile and then test new settings systematically.
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One free scan finds every outdated or missing driver and matches the right update for your exact hardware.Free scan · exact hardware matchRecheck the current documentation for your exact Ollama, LocalAI, model, backend, driver, and IDE versions. Defaults and integration recommendations change; a setting that exists in one release may be renamed, moved, or unsupported in another. Treat the measured behavior on your own coding tasks as the final test.
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