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Most local-model failures have a predictable cause: the GPU must hold more than the model’s weight file. Weights, the key-value (KV) cache, activations, communication buffers, CUDA graphs, adapters and multimodal state all compete for the same VRAM. Classify the stage at which the run fails, verify that the runtime is actually using the GPU, then reduce the specific allocation that exceeds your headroom.
A model that loads and then crashes is usually running out of KV-cache or warm-up memory, not rejecting a defective model file. A model that generates slowly is often spilling layers to system RAM or oversubscribing CPU threads. The fixes below apply to llama.cpp, Ollama, LocalAI and LM Studio, with settings named for each where they differ.
What the error stage tells you
Read the log around the failure and identify when it happens. The stage narrows the cause before you change settings.
| Failure stage | Likely cause | First change to try |
|---|---|---|
| Before or during weight loading | Too many parameters, high-precision weights, insufficient VRAM, or inadequate tensor/pipeline parallelism | Use a smaller model or lower-memory quantization; distribute layers across more GPUs |
| After weights load, during KV-cache or block allocation | Context window is too large, KV cache is placed on the GPU, or memory is fragmented | Lower context length and batch size; review KV-cache placement |
| CUDA graph capture or warm-up | Only a small amount of VRAM remains for graph work and temporary buffers | Lower the runtime memory budget or disable CUDA graphs for diagnosis |
| Generation starts but is slow | CPU fallback, partial GPU offload, excessive threads, large context or batch settings | Confirm GPU layers in logs, then tune offload, threads, context and batch size |
Do not infer capacity from the model-file size alone. A quantized file may fit while its runtime allocations do not.
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How much VRAM do you need?
Use NVIDIA’s planning heuristic as a lower bound for the weights:
weight_memory_per_gpu = total_parameters × bytes_per_parameter ÷ tensor_parallelism
Reserve additional memory for the KV cache, activations, communication buffers, CUDA graphs, adapters and any image or audio state. The usable budget is lower than the advertised VRAM capacity because the operating system, driver and other processes also need memory.
Worked sizing example
NVIDIA estimates that an 8-billion-parameter Llama 3.1 model in BF16 needs approximately 8 billion × 2 bytes = 16 GB for weights. Its documentation says that this can fit on one 24 GB GPU, such as an A10G or RTX 4090, with room left for KV cache and runtime overhead. That is a workload-dependent example, not a guarantee for every context length, batch size or backend.
Long contexts can consume the remaining headroom surprisingly quickly. A model’s native context setting, often read from max_position_embeddings in config.json, may demand more KV-cache memory than remains after weights, activations and overhead are subtracted.
Step 1: Confirm that acceleration is active
Before tuning performance, prove that the model is on the GPU. A CPU-only run can look like a memory problem while actually being a device-discovery or driver problem.
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llama.cpp
Start the model with GPU offload requested, for example:
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(The short form is -ngl 60.) In startup output, look for lines like llama_model_load_internal: [cublas] offloading 60 layers to GPU and llama_model_load_internal: [cublas] total VRAM used: 17223 MB. These diagnostics indicate that CUDA offload is active and show the allocation reported by the example run. If no layers are offloaded, check that you installed a CUDA-capable build and that the driver can see the GPU.
LocalAI
Use nvidia-smi while the model starts and verify that the process appears with GPU memory allocated. Use a CUDA-enabled container image, select the backend that matches the model format, and inspect logs for ggml_init_cublas: found X CUDA devices. A GGUF model should normally be paired with the llama-cpp backend. If the container cannot see the device, fix passthrough and driver configuration before changing quantization.
Ollama
For an Ollama installation in a container, verify NVIDIA device passthrough, the NVIDIA driver and Unified Virtual Memory (UVM). Enable diagnostic output with OLLAMA_DEBUG=1, then inspect the service log and kernel messages for device or driver errors. A missing GPU in these checks means the model is likely running on CPU regardless of its size.
LM Studio
Watch the load information for GPU offload and observe VRAM use in the operating system’s GPU monitor. The relevant controls are context_length, eval_batch_size, flash_attention and offload_kv_cache_to_gpu. Their effect depends on the backend and model, so change one at a time and monitor both memory and tokens per second.
Fix models that do not fit during weight loading
Choose a smaller quantization
Quantization stores weights with fewer bits, reducing memory at the cost of some numerical precision. If Q8_0 or Q6_K will not load, LocalAI specifically recommends trying Q4_K_S or Q2_K. Q4_K_S generally preserves more quality than Q2_K but uses more memory; Q2_K is a more aggressive capacity-first option. Compare variants of the same model at the same context and workload rather than assuming the highest-bit file is always best.
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Choose fewer parameters
A smaller model can be faster and more reliable than a larger model that forces extensive CPU offload. Parameter count and quantization interact: reducing both may free enough VRAM for the KV cache and leave room for stable generation.
Distribute the model
When several GPUs are available, use tensor or pipeline parallelism so that each device holds only part of the weights. NVIDIA recommends distributing weights across more GPUs when the selected precision cannot fit on one device. Confirm from logs that the intended number of devices and layers is actually being used.
Do not treat system RAM as equivalent VRAM
Allowing layers to spill into system memory can make a run technically possible but dramatically slower because generation must cross the PCIe or interconnect boundary. If you must use partial offload, keep the GPU-resident layers that the runtime prioritizes and reduce context and batch allocations so the offload is deliberate rather than an emergency fallback.
Fix an out-of-memory error after the model loads
Lower the context length first
The KV cache grows with the number of tokens retained for attention. A model can finish loading and then fail when the first request allocates its cache. Set a smaller maximum context: use context_size in LocalAI, --max-model-len in runtimes that expose that option, or the equivalent application setting. NVIDIA’s example reduces --max-model-len to 4096 to fit a constrained GPU.
In LM Studio, context_length is the maximum number of tokens considered. Set it to the longest prompt and conversation you genuinely need, not automatically to the model’s advertised native maximum. If a 32K context is unnecessary, starting at 4K or 8K leaves substantially more room for generation and other allocations.
Reduce input and evaluation batch size
Batching processes more input tokens simultaneously and therefore needs more temporary memory. In LM Studio, lower eval_batch_size when prompt evaluation causes an OOM or a large transient spike. Other runtimes expose a similarly named batch or prompt-batch setting. A smaller batch may reduce prompt throughput while allowing the model to run consistently.
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Decide where the KV cache belongs
Keeping the KV cache on the GPU usually improves latency but consumes VRAM. If the weights fit yet the cache does not, move the cache to system memory when your backend supports it. LM Studio exposes offload_kv_cache_to_gpu; disabling it can avoid an OOM at the cost of slower generation. Conversely, if the cache is currently on the CPU and you have spare VRAM, enabling GPU placement can improve speed.
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Use Flash Attention where supported
LM Studio’s flash_attention option can reduce memory use and improve speed on compatible backends and hardware. Enable it, retest the same prompt and context, and keep it disabled if the backend or driver does not support it correctly.
Handle fragmentation and CUDA-graph warm-up failures
Recognize fragmentation
A process can report substantial reserved but unallocated PyTorch memory and still fail a large allocation because free blocks are fragmented. NVIDIA documents this mitigation:
PYTORCH_ALLOC_CONF=expandable_segments:True
Set the variable before launching the service, then restart it. This changes allocator behavior; it does not create additional VRAM and cannot make an oversized model fit.
Give graph capture more headroom
Some runtimes allocate extra memory while capturing CUDA graphs or warming up. If the failure appears at that point, lower the runtime’s memory-utilization budget so temporary allocations have room, or disable CUDA graphs for diagnosis. Disabling graphs can reduce throughput, so use it to confirm the cause and then re-enable them if a lower budget resolves the issue.
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- Stop other GPU applications, browsers using hardware acceleration and stale model servers.
- Unload inactive models; a service that keeps several models resident can consume the headroom needed by the next request.
- Restart the process after changing context, cache or allocator settings so old reservations are released.
Fix slow generation once the model runs
Check for partial CPU offload
Inspect the startup log for the number of GPU layers and total VRAM. If only part of the model is on the GPU, generation speed may be limited by repeated transfers to system RAM. Lower the model size or quantization, reduce context and batch allocations, or add GPU capacity so more layers remain resident.
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Tune CPU threads instead of maxing them out
More threads are not automatically faster. llama.cpp recommends starting with one thread, doubling the count until a bottleneck appears, then setting the production value around the physical-core count. An excessively large -t or --threads value can oversubscribe the CPU, increase contention and reduce tokens per second. Benchmark with a repeatable prompt after each change.
Reduce context and batch overhead
Every additional token in the active context increases attention work, and large prompt batches can make the initial evaluation phase dominate perceived latency. Trim old conversation turns, lower the maximum context, or reduce evaluation batch size when prompt processing—not token generation—is the slow phase.
Separate prompt speed from generation speed
Measure time to first token and steady-state tokens per second separately. A large prompt, image input or cold CUDA graph can make first-token latency high even when subsequent generation is fast. If both are slow, investigate device use and CPU offload first; if only the first token is slow, adjust context, batch and warm-up settings.
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Tool-specific settings checklist
| Runtime | Settings and checks | Typical use |
|---|---|---|
| llama.cpp | -ngl/--n-gpu-layers; startup VRAM and offload lines; -t/--threads |
Request GPU layers, verify CUDA, then tune physical-core-aligned threading |
| LocalAI | Matching backend and model format; context_size; lower quantization; low_vram; model unload limits |
Fit GGUF models, constrain cache use and prevent multiple resident models |
| LM Studio | context_length, eval_batch_size, flash_attention, offload_kv_cache_to_gpu |
Balance cache placement, prompt throughput and VRAM |
| Ollama | Container passthrough, current NVIDIA driver and UVM, OLLAMA_DEBUG=1, service and kernel logs |
Resolve GPU discovery and driver failures before performance tuning |
A repeatable troubleshooting procedure
- Record the exact failure point. Note whether it occurs during weight load, cache allocation, graph capture, warm-up or generation.
- Observe the device. Run
nvidia-smiduring startup and save the runtime’s offload and VRAM lines. - Make a conservative baseline. Use a short context such as 4096 tokens, a small evaluation batch, one CPU thread initially, and no other resident models.
- Fix capacity in order. If weights fail, lower parameter count or quantization, then add parallelism. If the cache fails, lower context and adjust KV placement. If warm-up fails, address fragmentation or graph headroom.
- Change one variable. Retest the same prompt and record memory, time to first token and steady-state tokens per second.
- Increase limits gradually. Raise context, batch size and thread count independently until the first regression, then keep the previous stable value.
- Persist only verified settings. Put the working values in the service configuration and document the model, quantization, driver and backend so an update can be diagnosed against a known baseline.
Common symptoms and targeted fixes
- “Loads to 100%, then OOMs on the first prompt”: lower context length, reduce evaluation batch size and check KV-cache placement.
- “Works at 2K context but fails at 16K”: the KV cache, not the weights, is exceeding the remaining VRAM; use a smaller context or move the cache off GPU.
- “GPU utilization is near zero and CPU is busy”: inspect CUDA build, device passthrough, drivers and offload log lines.
- “It runs, but tokens per second fell after increasing threads”: reduce
--threadstoward the physical-core count. - “Only graph capture fails”: lower the memory budget or disable CUDA graphs temporarily, and investigate fragmentation.
- “A smaller quantization fixed OOM but quality changed”: move up one quantization level if headroom permits, or use a smaller parameter-count model at a higher precision.
Capacity planning that remains reliable
Plan for the complete runtime footprint rather than the model filename: weight memory plus KV cache, activations, communication buffers, graph allocations, adapters and multimodal state. Leave headroom for the driver and other processes. A 24 GB card can accommodate the documented 16 GB BF16 weight example with room for runtime allocations, but a longer context, larger batch, image input or multiple resident models can consume that margin.
For a stable deployment, record the model’s parameter count and quantization, GPU VRAM, context limit, KV-cache location, batch size, thread count, backend, driver version and whether parallelism is enabled. Those details explain why an identical model may succeed on one machine and fail or crawl on another.
Frequently Asked Questions
Why does a local model run out of memory after it has already loaded?
The KV cache and other runtime allocations are created after weight loading. Lower the context length first, then reduce batch size or move the KV cache off the GPU if your runtime supports it.
How can I tell whether Ollama or llama.cpp is using my GPU?
Watch nvidia-smi during startup and inspect logs. llama.cpp should report GPU-layer offload and total VRAM; Ollama and container deployments should show successful device passthrough and no UVM or driver errors.
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It is the direct fix for weight-capacity failures, but it trades precision for memory. Try the highest quantization that leaves room for the required context and runtime overhead; LocalAI lists Q4_K_S and Q2_K as lower-memory alternatives to Q8_0 and Q6_K.
Why is CPU offload so slow?
Each generation step may transfer data between GPU and system memory. Reduce model size or quantization, lower context and batch allocations, or add GPU capacity so more layers remain resident.
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