Local AI Model Won't Load? Fixing Out-of-Memory Errors

A local AI model that won't load and throws an out-of-memory error is almost always telling you one thing: the model is too big for the memory you have. The fix is rarely new hardware. It is choosing a smaller model or a more aggressive quantisation so the file and its working memory fit inside your combined VRAM and system RAM.

Quick Answer

A local AI model fails to load with an out-of-memory error when the model file plus its context memory exceeds your available VRAM and system RAM combined. Fix it by picking a smaller model, choosing a more compressed quantisation such as Q4, or reducing the context length. Match the model size to your memory rather than buying more hardware first.

Why the error happens

When you load a local large language model, two things have to fit in memory: the model weights themselves and the context window, the working space that holds your conversation or input. The weights are by far the larger chunk. If the weights plus context exceed the memory your machine can give them, the loader cannot allocate the space and aborts with an out-of-memory error.

Your machine can use two pools: the VRAM on your graphics card and your system RAM. Tools can split a model across both, but the total still has to fit. A model that needs more memory than VRAM and RAM combined will not load, full stop.

Fix 1: Choose a more compressed quantisation

Quantisation compresses a model by encoding its weights at reduced bit-depth. A model at full precision is large; the same model brought down to a 4-bit format like Q4 is dramatically smaller, with only a modest drop in output quality for most everyday use. If a model will not load, dropping from a higher-precision version to a Q4 or similar GGUF quant is the quickest fix.

The trade is small: lower quantisation means a slightly less capable model, but for most local tasks a Q4 model that actually runs beats a full-precision one that refuses to load.

Fix 2: Pick a smaller model

Model size is measured in parameters, often shown as 7B, 13B, 70B and so on. A larger parameter count needs more memory. If a 70B model will not load on your machine, a 7B or 13B model of the same family often will, and for many tasks the smaller model is perfectly capable. Start with a size your memory can comfortably hold, then scale up only if the hardware allows.

Fix 3: Reduce the context length

The context window also consumes memory, and a very large context can tip a model that was nearly fitting over the edge. Lowering the context length frees memory and can be enough to get a borderline model loading. If you do not need a huge context for your task, a smaller one is an easy saving.

When it really is the hardware

If even a small, heavily quantised model struggles, your machine genuinely lacks the memory for local inference, and the answer is more VRAM or more system RAM rather than further shrinking the model. Machines configured for this kind of work, with ample VRAM and RAM, are grouped in Evetech's AI-ready PC range, which is the right place to look if local models are central to what you do. For a sense of which builds people actually run, the PC best sellers at Evetech show what builds people are actually running.

Frequently Asked Questions

What does an out-of-memory error mean for a local AI model?

It means the model weights plus the context window need more memory than your VRAM and system RAM can supply, so the loader cannot allocate the space and stops. The model is too large for the memory available.

What is quantisation and will Q4 hurt quality?

Quantisation stores the model's weights at lower precision to shrink its size. A Q4 format is much smaller than full precision with only a modest quality drop, which is acceptable for most everyday local tasks.

Does VRAM or system RAM matter more?

Both count. Tools can split a model across VRAM and system RAM, so the total available memory is what matters. VRAM is faster, but the combined pool determines whether a model can load at all.

My model still won't load after switching to Q4. What now?

Try a smaller parameter count, such as moving from a 13B to a 7B model, and reduce the context length. If even a small quantised model fails, your machine likely needs more memory for local inference.