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1Repair Windows errors before they cause bigger problems2Scan for outdated or missing drivers - takes under a minute3Clear out junk files and repair common Windows errorsTo fix a local LLM out-of-memory error, first identify whether it occurs while loading model weights, processing the prompt, or generating tokens. Then reduce demand in that order: use a supported quantized or smaller model, lower batch size, context length, or concurrency, and use CPU or disk offload when the runtime supports it. Leave headroom for CUDA kernels, caches, other processes, and allocator overhead; the model’s advertised parameter size is not the same as the VRAM available to inference.
There is no universal VRAM number or command that fixes every runtime. The right remedy depends on the model, precision, accelerator, inference engine, context setting, and exact failure point.
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What an out-of-memory error actually means
“Out of memory” describes a failed allocation, not one specific defect. A local inference process can run out of GPU memory while loading weights, while building prompt-processing buffers, or later when the key-value (KV) cache grows during generation. The same model may load successfully with one context length or batch size and fail with another.
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| Memory consumer | When it matters most | What to check |
|---|---|---|
| Model weights | Initial loading and device placement | Parameter count, precision, quantization, and whether every layer is being placed on the accelerator |
| CUDA and runtime kernels | First allocation and runtime initialization | Headroom beyond the nominal weight size; Hugging Face documents roughly 1–2 GB for CUDA kernels in the cited scenario |
| KV cache | Long prompts, long conversations, and generation | Context-length setting, cache representation, and the runtime’s reported cache allocation |
| Batch and prompt buffers | Prompt processing and multiple simultaneous requests | Batch size, microbatch size, concurrency, and prompt length |
| Other processes | Any stage of inference | Whether another application, service, or previous job is holding VRAM |
| Allocator-reserved memory | Allocation failures despite apparently free space | The error’s allocated, reserved, and free figures, interpreted for the specific framework version |
How do I fix out-of-memory errors when running a local LLM?
Work through the following sequence. Stop after the first change that gives you a stable run, then retest the workload you actually need rather than only a short test prompt.
1. Capture the failing stage and configuration
Save the complete error and the lines immediately before it. Record:
- Inference runtime and exact version.
- Model name, parameter size, weight format, and quantization format.
- GPU or other accelerator model, total VRAM, and the amount reported as available at launch.
- System RAM and whether CPU or disk offload is enabled.
- Context-length setting, prompt length, batch or microbatch size, and request concurrency.
- Operating system, driver or backend version, and any custom build options.
- Other GPU workloads running at the same time.
Distinguish a failure during loading from one during prompt processing or generation. A failure before the first token usually points toward weight placement, kernels, or initialization overhead. A failure only with a long prompt, a larger batch, or several simultaneous requests points toward workload allocations or the KV cache.
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2. Check what else is using the accelerator
Use the monitoring facility documented for your operating system, driver, and runtime to inspect active processes and reported memory. Do not assume one command works on every platform. Stop or reconfigure unrelated workloads where appropriate, then retry from a clean process. A process that has reserved memory, another user’s job, a desktop workload, or a second inference server can reduce the headroom available to your model.
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Reduce model-weight demand first
Use a supported quantized model
Quantization stores weights with fewer bits. The Hugging Face Transformers quantization guide states that quantizing a model in 8-bit halves the memory usage for the quantized model weights. That is not a promise that the whole process will consume half as much memory: unquantized components, runtime workspaces, kernels, caches, temporary tensors, and unsupported layers still consume memory. Output behavior and speed also depend on the model, implementation, hardware, and workload.
For a Transformers workflow using the documented bitsandbytes integration, the pattern is:
from transformers import AutoModelForCausalLM, BitsAndBytesConfig
model_id = 'a model identifier supported by your runtime'
quantization = BitsAndBytesConfig(load_in_8bit=True)
model = AutoModelForCausalLM.from_pretrained(
model_id,
quantization_config=quantization,
device_map='auto'
)Use a model and layer set supported by the versions installed in your environment. Confirm from the runtime’s current documentation which modules can be quantized and whether your accelerator supports the chosen format. Compare the complete process memory and the latency of a representative prompt; the weight reduction alone does not establish either result.
Choose a smaller model or a lower-memory format
If the model still cannot load, select a smaller model family or a supported format with lower weight memory. This changes the model’s capabilities and may change output quality, so test the tasks that matter to you. Keep the context, batch, and concurrency settings constant while comparing; otherwise a workload change can hide the actual cause of the failure.
Leave room for initialization
Do not target 100% of nominal VRAM with weights. Accelerate’s Big Model Inference documentation specifically warns that CUDA initialization consumes memory beyond the model weights and recommends leaving headroom when setting max_memory. A model that appears to fit on paper can fail as soon as kernels, buffers, and the cache are allocated.
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Use device mapping and offload when the accelerator is too small
If the complete model cannot fit on the GPU, a supported runtime can place some weights elsewhere. Accelerate describes device maps named auto, balanced, and sequential. Its Big Model Inference documentation explains that weights can be offloaded to CPU or disk when they do not fit on the GPU. This changes the execution path and can increase latency; the documentation establishes capacity and placement behavior, not a speed guarantee for a particular computer.
The Transformers example above uses device_map='auto', which lets the integration distribute supported weights according to available devices. For multi-GPU systems, the quantization guide says that large models can use this mapping to distribute weights across available GPUs. Check the runtime’s map report after loading so you know which layers are on each device and whether a disk path is involved.
Set a realistic memory limit
When a runtime accepts per-device limits such as max_memory, reserve space for kernels and inference allocations instead of assigning every reported byte to model weights. The correct value is specific to the GPU, driver, runtime, and other processes. If a map fails at the limit, lower the assigned weight budget and retry; if it succeeds but generation fails, reduce the workload allocations as well.
llama.cpp unified-memory fallback
For llama.cpp, the moving master build documentation says that setting GGML_CUDA_ENABLE_UNIFIED_MEMORY=1 can enable swapping to system RAM instead of crashing when VRAM is exhausted on Linux. Windows has a feature called System Memory Fallback. Verify the current release, platform, build options, and backend support before relying on either behavior. System-memory swapping is a capacity fallback, not evidence that speed will remain unchanged.
Reduce allocations made for the workload
Lower batch size or microbatch size
Batch size affects temporary tensors and prompt-processing memory. If the model loads but fails when work begins, lower the batch or microbatch setting and retry. Accelerate’s troubleshooting guide documents find_executable_batch_size(), a helper that halves the batch size after an out-of-memory error and retries. The guide also says that the model, optimizer, and other memory-consuming objects should be created inside the wrapped function so each retry can construct them for the new batch size.
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For inference, apply the same principle manually if your runtime does not provide an equivalent helper: start with the workload that fails, reduce the batch, and keep the model and context settings otherwise unchanged. A smaller batch can reduce throughput, so measure latency and total tokens per second after you have a stable configuration.
Reduce context length when the failure tracks prompt size
Longer context generally requires more cache and working memory, but there is no model-independent KV-cache formula or universal flag established here. Lower the selected runtime’s context setting as a diagnostic, then consult that runtime’s documentation for the exact option and cache implementation. Test with a prompt long enough to reproduce the error; a short prompt cannot confirm that the cache problem is solved.
Reducing context limits how much conversation or source material the model can see. If you need long documents, consider chunking or retrieval in the application, but validate those changes against your task and runtime rather than assuming they remove all memory pressure.
Reduce concurrency
Multiple simultaneous requests multiply some cache and temporary allocations. If one request succeeds but two or more fail, lower the server’s concurrency or per-request batch setting. Keep a single-request baseline so you can tell whether the limit is total model capacity or aggregate workload demand.
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Run a short prompt with one request, then increase only one variable at a time: prompt length, generation length, batch, or concurrency. This identifies the allocation that crosses the limit. A model that survives loading but fails on the first prompt needs a different fix from one that fails only after a long conversation.
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Read allocator numbers without jumping to the wrong diagnosis
Error messages may distinguish memory currently allocated to tensors from memory reserved by the allocator and memory reported as free by the driver. Reserved memory is not, by itself, proof of a leak, and this evidence does not establish a universal cache-clearing cure. Do not assume that calling a cache function always fixes an out-of-memory error.
Likewise, do not edit allocator environment variables merely because an error mentions reserved memory. Such settings are framework- and version-specific and should be changed only when the exact documentation for your runtime and error pattern supports them. First remove competing workloads, reduce the allocation that triggers the failure, and confirm whether the problem is reproducible in a fresh process.
Compare the available fixes
| Intervention | Documented or expected memory effect | Trade-offs and limits | Best diagnostic use |
|---|---|---|---|
| 8-bit quantization | Hugging Face says quantized model memory usage is halved for 8-bit weights | Whole-process savings vary; supported layers, runtime allocations, quality, and speed are model- and hardware-dependent | Loading fails because weight memory is too high |
| Smaller model | Reduces the number of weights that must be placed | Capabilities and output quality can change; the amount of saved memory depends on the two models | The selected model cannot fit even after supported quantization |
| Lower batch size | Accelerate’s helper halves the batch size and retries after OOM | Lower throughput and potentially higher total processing time | Failure appears during prompt processing or only at higher throughput |
| Lower context or concurrency | Reduces cache and workload allocations in runtimes where those settings control them | Less conversation or fewer simultaneous requests; the exact savings are runtime- and model-dependent | Failure follows prompt length, generated history, or concurrent users |
| CPU or disk offload | Device maps can place weights off the GPU on CPU or disk | Different execution path and potentially higher latency; support varies by runtime and platform | Weights exceed accelerator capacity but the system has usable RAM or storage |
| llama.cpp unified memory | On Linux, the documented setting can swap to system RAM when VRAM is exhausted | Requires a compatible build and may be much slower than fitting entirely in VRAM | llama.cpp reaches the VRAM limit and the platform supports the feature |
| GPU with more VRAM | Adds physical accelerator capacity | Cost and compatibility checks remain; no particular capacity is universally sufficient | Supported software reductions and offload still cannot meet the required workload |
How much VRAM do you need to run an LLM locally?
There is no reliable universal VRAM table without naming the model architecture, weight precision or quantization, context length, batch size, concurrency, runtime, accelerator, and overhead assumptions. A parameter count multiplied by bytes per weight is only a rough weight estimate; it omits CUDA kernels, temporary buffers, KV cache, allocator behavior, and other processes.
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Use a configuration-specific budget instead:
- Measure the weight format and model placement the runtime will actually use.
- Reserve headroom for initialization; Hugging Face documents about 1–2 GB for CUDA kernels in its cited example.
- Add the runtime’s reported cache and prompt-processing allocations for your chosen context and batch.
- Subtract VRAM held by other applications and services.
- Test the longest prompt, largest batch, and highest concurrency you genuinely need.
Do not treat a successful load as proof that the intended workload fits. Generation can allocate additional cache and temporary memory after the weights are resident.
Failure-specific troubleshooting branches
It fails immediately while loading weights
- Close competing GPU workloads and retry from a clean process.
- Use a supported quantized or smaller model.
- Enable a documented device map and offload some weights to CPU or disk.
- Leave explicit headroom for CUDA initialization and runtime buffers.
- If the runtime is llama.cpp on Linux, verify whether the current build supports
GGML_CUDA_ENABLE_UNIFIED_MEMORY=1.
It loads, then fails on the first prompt
- Lower batch or microbatch size.
- Lower context length and test with a prompt that previously failed.
- Check whether prompt-processing buffers are larger than expected for the selected runtime.
- Confirm that no second process started using the accelerator after model loading.
It fails only with long conversations or long generations
- Reduce the context setting or trim the conversation history.
- Lower concurrency so each request has more cache headroom.
- Inspect the runtime’s cache allocation and representation rather than applying a generic token-memory estimate.
- Retest with a fixed prompt and generation length to isolate which limit is being crossed.
It fails only when serving several requests
- Reduce server concurrency or request batch size.
- Establish a one-request baseline, then increase concurrency one step at a time.
- Account for the aggregate cache and temporary allocations, not only the shared model weights.
When new hardware is the right answer
Consider a GPU with more VRAM only after you have tried an appropriate model format, reduced workload allocations, and evaluated supported CPU or disk offload. More VRAM addresses a real capacity limit, but it does not correct an incompatible quantization backend, an unsupported layer, an excessive context setting, or another process consuming the device.
Before choosing hardware, verify the power supply, case clearance, motherboard and bus compatibility, operating-system support, driver, runtime backend, and budget. No specific GPU model or price is established here, and a larger card still needs headroom for kernels and inference-time allocations.
Quick Recap
A repeatable checklist
- Save the full error, runtime version, model and format, accelerator, VRAM, system RAM, context, batch, concurrency, and operating-system details.
- Identify whether failure occurs during loading, prompt processing, or generation.
- Inspect and remove competing accelerator workloads.
- Reduce weight demand with supported quantization or a smaller model.
- Use device mapping and CPU or disk offload when supported, keeping headroom in memory limits.
- Lower batch size; where available, use Accelerate’s retry helper that halves the batch after OOM.
- Lower context length or concurrency when the failure follows prompt size or simultaneous requests.
- Interpret allocated and reserved memory in the context of the exact framework version; do not assume fragmentation or a cache-clearing call is the cause or cure.
- Retest the real workload, including its longest prompt and required concurrency.
- Only then decide whether additional GPU memory is necessary.
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