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When a local language model runs out of memory, the fix is not always “buy more RAM.” The first step is to identify which memory pool is full: system RAM used for CPU inference, GPU VRAM used for GPU inference, or both when a runtime splits work across CPU and GPU. Context length and simultaneous requests also affect the load. There is no single RAM requirement that applies to every model, computer, and runtime.
Does a local LLM use RAM or VRAM?
It depends on how the inference runtime places the model. Ollama’s FAQ distinguishes system memory for CPU inference from VRAM for GPU inference. Some setups use both, placing part of the work on the CPU and part on the GPU.
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- System RAM is the computer’s main memory. It matters for CPU inference and may also matter on systems with shared memory.
- GPU VRAM is the memory available to the graphics processor. GPU inference draws on this pool, and Ollama’s context defaults are organized by available VRAM.
- Hybrid placement uses CPU and GPU together. The llama.cpp project supports CPU/GPU inference for models that exceed GPU VRAM capacity, but that capability does not guarantee a particular speed.
So, a computer can have plenty of system RAM and still lack the VRAM needed for a desired GPU configuration. Conversely, a model that does not fit wholly in VRAM may still run with CPU/GPU placement, subject to the setup’s memory capacity and performance.
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The model’s weights are only part of the memory budget. The runtime also needs memory for the context—the tokens available to the model—and the demands of requests being processed. Ollama defines context length as “the maximum number of tokens that the model has access to in memory” in its context-length documentation.
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Ollama also documents that required memory for parallel request processing scales with the number of parallel requests multiplied by context length. A configuration that works for one request may therefore fail, or need more memory, when multiple requests are handled at once. Ollama’s FAQ also notes that multiple models can be loaded at the same time when system memory or VRAM is sufficient.
These factors explain why a model that starts successfully can still encounter memory pressure with a longer prompt, a larger context setting, multiple simultaneous requests, or additional models loaded in memory. They also explain why a model’s name or parameter count alone cannot establish whether it will fit on a particular machine.
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How much RAM do you need to run a local LLM?
There is no universal figure supported for every model and setup. To estimate fit, identify the runtime and model configuration you intend to use, determine whether inference will use system RAM, VRAM, or both, and account for the requested context length and concurrency. A memory figure without those details can mislead: it may describe a different runtime default, workload, or memory pool.
Ollama’s current rolling documentation lists context defaults of 4k below 24 GiB of VRAM, 32k for 24–48 GiB, and 256k at or above 48 GiB. Those are Ollama defaults by VRAM tier—not universal hardware requirements, recommendations for every task, or guarantees that every model will fit. Check the linked documentation for the current defaults when configuring Ollama.
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Which change should you try first?
Choose the adjustment that addresses the actual constraint. The effects below are general decision points; model- and workload-specific fit, quality, and speed are not established by a single rule.
| Option | Memory effect | Tradeoff to consider |
|---|---|---|
| Use a smaller model | Usually reduces the model-weight footprint, though exact use depends on the model and runtime. | Capability and output quality depend on the model and task; there is no universal quality ranking here. |
| Use a more quantized model | llama.cpp lists multiple integer quantization levels and describes quantization as reducing memory use. See the project documentation. | Quality and speed tradeoffs depend on the model and workload; do not assume identical results across configurations. |
| Shorten the context | Reduces the requested token context, which Ollama treats as a memory setting. | Less conversation or document history can be available to the model. |
| Reduce concurrent requests | Ollama documents memory scaling with parallel request count multiplied by context length. | Lower concurrency can limit throughput for multiple users or jobs. |
| Use CPU/GPU hybrid inference | llama.cpp can place work across CPU and GPU when a model exceeds GPU VRAM capacity. | Actual performance depends on the setup; no universal speed estimate is established. |
| Upgrade system memory | May increase available system-memory capacity if the computer supports an upgrade and the workload uses that pool. | Check upgradeability, memory type, and compatibility for the specific computer before buying. |
When is a RAM upgrade the right answer?
More system RAM is relevant when system memory is the limiting resource—for example, in a CPU-based inference setup or a system that uses shared memory—and the computer can be upgraded. It will not automatically add GPU VRAM, nor does it resolve every memory problem. First check your computer or motherboard specifications, confirm whether memory is upgradeable, and identify the memory pool your inference setup actually uses.
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If the constraint is GPU VRAM, consider a smaller or more quantized model, a shorter context, fewer parallel requests, or CPU/GPU hybrid placement before assuming a system-RAM upgrade will solve it. Whether any option is practical depends on the model, runtime, and task.
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A practical way to diagnose memory pressure
- Identify the inference path. Check whether your runtime is using CPU inference, GPU inference, or a mix; do not treat RAM and VRAM as interchangeable.
- Check the workload. Note the context length, number of simultaneous requests, and whether other models are loaded.
- Reduce demand before buying hardware. Try a shorter context or lower concurrency, or test a smaller or more quantized model if those changes suit your task.
- Consider hybrid placement where supported. It may allow a model larger than available VRAM to run, but performance is setup-dependent.
- Verify upgrade compatibility. Consult the computer or motherboard specifications and confirm the system is upgradeable and that system memory is the constraint.
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