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A slow or crashing local AI model usually has one of two root causes: the model is not actually running on your GPU, or the workload needs more memory than your hardware can provide. An out-of-memory error while loading weights requires a different fix from an error that appears after a long prompt, when the key-value (KV) cache is allocated. Start by identifying that phase, then verify placement with your runtime’s logs, calculate the model’s memory budget, and change only the setting that is limiting you. The quickest practical remedies are enabling the correct accelerator, using a smaller or quantized model, reducing context length, lowering batch or parallel-request counts, and leaving headroom for runtime overhead.
Why is my local AI model slow or running out of memory?
The failure phase tells you what to change. If the process fails before generation while loading weights, the model’s parameter count, numeric precision, GPU count, or tensor-parallel configuration is too large for the available memory. If it fails after a long prompt or when another request starts, the weights may fit but the KV cache, batch, parallel requests, graph memory, activations, adapters, or fragmentation do not.
Very low tokens per second often means an accidental CPU fallback rather than an inherently slow model. A model can also have a slow first response because its weights are being loaded; later responses may be faster when the runtime keeps it resident. Treat these as separate problems instead of lowering quality immediately.
How can I tell which phase is failing?
Read the runtime log and note exactly when the error appears. NVIDIA’s NIM guidance distinguishes weight-loading failures from later allocation failures, and recommends checking logs before selecting a supported profile.
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| Symptom | First checks | Likely fix | Trade-off |
|---|---|---|---|
| Out of memory before generation | Parameter count, precision, GPU count, and load log | Smaller model, lower-bit quantization, more VRAM, or tensor parallelism | Possible quality loss or hardware cost |
| Out of memory after long prompts | Context length, KV-cache allocation, parallel requests, and batch size | Lower context, batch, or parallel-request count | Less history or lower throughput |
| Very low tokens per second | Accelerator messages and model placement | Enable the CUDA, Metal, or ROCm path and eliminate CPU fallback | Driver or engine configuration work |
| Only the first response is slow | Model-load behavior and keep-alive settings | Preload the model or keep it resident | Memory remains occupied while idle |
| Model barely fits but is unstable | Headroom, graph or activation overhead, and fragmentation | Leave a larger margin or use a smaller model or quantization | Lower capacity or quality |
Do not infer a weight problem from an error that occurs only after increasing the prompt. Conversely, reducing context will not make a model whose weights cannot load fit in memory.
How do I verify that the model is using my GPU?
Check placement before changing prompts, context, or model quality. A CPU fallback can make an otherwise suitable model appear unusably slow.
Check Ollama placement
Run ollama ps while the model is loaded. Ollama reports 100% GPU, 100% CPU, or a CPU/GPU split. A split means some tensors are offloaded to system memory; it can make the model fit, but transfers across the CPU-GPU boundary generally reduce speed.
If the output shows 100% CPU when you expected acceleration, check the installed driver, the runtime build, and whether the model format is supported by the accelerator. Fix that placement issue first. Tuning context or quantization while the model is unintentionally on the CPU can hide the real cause.
Check Docker inference logs
For a containerized engine, confirm that the container can see the GPU and inspect startup logs for CUDA or Metal acceleration messages. Docker’s inference-engine guidance specifically says to ensure GPU acceleration is working and to check logs for those messages. A missing device mapping, incompatible driver, or CPU-only image can silently change the execution path.
Interpret a mixed placement result
Mixed placement is not automatically wrong. It is a deliberate way to run a model that exceeds VRAM, but every transfer between CPU and GPU adds latency. If the model fits entirely on one GPU, keeping it there is normally faster; use offload when fitting is more important than response speed.
How much VRAM does a local model need?
Estimate weight memory with NVIDIA’s rule: weight memory per GPU = total parameters × bytes per parameter ÷ tensor parallelism. This is a starting estimate for the weights only. KV cache, activations, graph workspaces, adapters, batching, and the runtime itself require additional memory.
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|---|---|---|
| BF16 or FP16 | 2 bytes | Full half-precision weight storage |
| FP8 | 1 byte | Lower storage than BF16 or FP16, when the backend supports it |
| INT4 or NVFP4 | 0.5 byte | Very compact weights with a possible quality trade-off |
For example, an 8-billion-parameter BF16 model needs about 16 GB of weight memory before KV cache and other overhead. NVIDIA’s example says that this can fit on one 24 GB GPU, such as an RTX 4090-class card, leaving room for runtime memory and a useful context. That is not a guarantee for every engine: the exact graph, context, batch, and driver allocations still matter.
Hugging Face recommends adding roughly 10% headroom beyond the theoretical minimum because batches and runtime allocations need more than the weights alone. A model that appears to require exactly the available VRAM is therefore an unstable configuration, especially when the operating system or another process is already using memory.
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What tensor parallelism changes
With two equivalent GPUs and a supported tensor-parallel configuration, the weight portion of the estimate is divided by two. Parallelism does not make KV-cache, communication, or workspace costs disappear, and inter-GPU transfers can reduce performance. Use the runtime’s supported profile rather than assuming that adding a second card automatically doubles usable capacity.
What should I do when the model cannot load its weights?
Change the weight budget in the least disruptive order: confirm placement, choose a smaller representation, then add hardware or parallelism if the workload still needs more capacity.
1. Confirm the model and precision
Record the parameter count and whether the file is FP32, BF16, FP16, FP8, or an integer-quantized variant. Moving from float32 to float16 or bfloat16 halves storage, according to Hugging Face’s evaluation guidance. Do not compare a model’s advertised parameter count with VRAM without accounting for precision.
2. Use a smaller model when its capability is sufficient
A smaller model reduces both weight memory and the amount of data moved during generation. This is often more reliable than forcing a barely fitting large model into an unsupported configuration. Check the task requirements first: a compact model may be adequate for short classification or extraction jobs, while complex reasoning or long-context work may justify the larger model.
3. Quantize deliberately
Eight-bit and four-bit quantization reduce memory substantially. Docker recommends Q4 quantization for llama.cpp cases involving out-of-memory errors or poor speed. Quantization is not free: some libraries execute a quantized model more slowly, and NVIDIA warns that overly aggressive quantization can reduce response quality. Compare factual accuracy, formatting, and instruction following on a small set of representative prompts before making a permanent choice.
4. Add VRAM or use more GPUs
If the required quality cannot be met by a smaller or quantized model, use a GPU with more VRAM or a supported multi-GPU profile. Hardware capacity is only part of the decision: verify power, cooling, case clearance, driver support, and the runtime’s supported parallelism mode. More GPUs can add communication overhead even when they solve the memory limit.
5. Use CPU offload only when the speed trade-off is acceptable
Offloading part of the model to system RAM can make an otherwise impossible load succeed. It is normally slower because weights or intermediate data move between CPU and GPU. Reserve it for interactive workloads that tolerate lower throughput, background jobs, or situations where buying more VRAM is not practical.
How do I fix an out-of-memory error after a long prompt?
Reduce the memory used by the KV cache and concurrent work. The weights may already fit; the additional tokens and requests are what exceed the remaining budget.
Lower the context window to the task’s real requirement
Longer context consumes more memory, even when the model has a larger advertised maximum. Ollama documents a default context of 2,048 tokens and exposes the num_ctx setting to change it. Set that value to the longest prompt and answer history you genuinely need rather than the model’s maximum. A coding assistant that needs a few files does not benefit from reserving a huge context for every request.
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Lowering context discards older conversation or limits how much source text can be supplied in one call. If you need the information later, summarize or retrieve the relevant passages instead of keeping every token in the active window.
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Reduce batch size
Batching can improve throughput when the requests fit in memory, but the same extra work can trigger an out-of-memory error. Hugging Face recommends lowering batch size when a model that otherwise fits still fails. Test one request first, then increase the batch gradually while watching peak memory and latency.
Reduce parallel requests
Each concurrent request can allocate its own effective context and KV-cache space. Ollama documents OLLAMA_NUM_PARALLEL, queue limits, and the fact that parallel processing increases memory requirements. Lower the parallel count when sporadic failures occur only under load; this trades simultaneous throughput for predictable latency and capacity.
Retest with a short prompt
After changing context, batch, or concurrency, repeat the exact prompt that failed and then test the longest expected prompt. If the short case works but the long case still fails, the remaining limit is probably KV-cache capacity rather than model weights.
Why is generation slow even though the model fits?
First rule out CPU execution, then remove avoidable load and decide whether startup latency or token-generation latency is the problem.
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Use the placement checks above while a request is active. A 100% CPU result or missing CUDA, Metal, or ROCm messages explains very low tokens per second more often than a need for a more aggressive quantization. Correct the driver, device exposure, runtime build, or backend selection before changing model quality.
Separate startup time from steady-state speed
A slow first response can simply be model loading. If later requests are fast, preload the model or configure a keep-alive period so it remains resident. The cost is that the model continues to consume memory while idle, which may reduce the number of other models or services that can run.
Reduce unnecessary context and concurrency
Every extra token must be processed, and additional active requests compete for memory and compute. Keep the context at the requirement established for the task and lower parallel work if latency spikes under load. Do not increase batch size merely because it can improve aggregate throughput; it may make interactive responses slower or cause paging.
Evaluate quantization with measurements
Quantization often improves fit and can improve speed by reducing memory traffic, but a particular quantization library may be slower. Measure tokens per second after warm-up, first-response time, peak memory, and answer quality on the same prompts. Choose the representation that meets the task’s quality and latency target rather than assuming that the smallest file is fastest.
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How should I tune batch size and concurrency?
Use the smallest configuration that meets your throughput goal, then increase one variable at a time. Memory use rises with active sequences, their context, and intermediate work.
- Start with one request and a conservative context value.
- Confirm the model is on the intended accelerator and record peak memory and tokens per second.
- Increase batch size in small steps until throughput improves or memory approaches your safety margin.
- Test concurrent requests separately by changing the parallel-request setting, such as
OLLAMA_NUM_PARALLEL. - Keep the highest setting that survives the longest realistic prompt without an out-of-memory error.
Batching is primarily a throughput lever. For a single interactive user, a larger batch may add waiting without improving perceived speed. For a service handling many short requests, it can raise total throughput if enough memory remains for the KV caches.
When should I use CPU offload or multiple GPUs?
Use offload when fitting the model is more important than speed; use multiple GPUs when the runtime supports a profile that divides the model efficiently and the workload justifies the added hardware.
CPU offload
CPU offload reduces VRAM pressure by placing some weights or data in system memory. The transfer path is slower than keeping the entire model on one GPU, so it is a fallback rather than a universal optimization. Monitor whether the resulting tokens per second meets your use case before accepting it as a permanent setup.
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Ollama’s guidance is to keep a model on one GPU when it fits there for performance; when it cannot fit on one GPU, it can be spread across available GPUs. Multi-GPU operation still needs enough aggregate memory for weights, KV cache, and runtime overhead, and the engine must support the chosen split. A second card is not a cure for an unsupported backend or an overly large context.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do I fix vLLM or NIM memory-profile errors?
Choose a supported hardware and precision profile before adjusting the memory fraction. NVIDIA recommends matching a supported profile, increasing tensor or pipeline parallelism, or selecting FP8 or NVFP4 when the weights do not fit.
Match the documented profile
Check the engine’s supported GPU model, precision, and parallelism combination. A profile designed for a different architecture can fail even when a simple parameter calculation appears to fit. Runtime logs should identify whether the failure is weight loading, KV-cache allocation, or a graph and workspace allocation.
Change parallelism or precision
If the weights exceed one GPU, use the supported tensor- or pipeline-parallel option. If the backend supports it, FP8 or NVFP4 can reduce the weight budget. Validate output quality after changing precision, especially for tasks that depend on exact numbers, code, or structured formats.
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Interpret gpu_memory_utilization correctly
This setting controls how much of the GPU memory budget the engine may use. Raising or lowering it can balance headroom and capacity, but it cannot repair unsupported hardware, an incompatible backend, or a driver defect. If an allocation fails despite a reasonable budget, return to the profile and log checks rather than repeatedly changing this number.
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What is the safest troubleshooting sequence?
Use this sequence so that each test answers one question and does not hide the original cause.
- Capture the failure point. Save the startup and request logs, noting whether the error occurs during weight loading, prompt processing, or generation.
- Check placement. Use
ollama psfor Ollama or the equivalent engine report, and confirm CUDA, Metal, or ROCm messages in container logs. - Calculate the weight budget. Multiply parameters by bytes per parameter and divide by tensor parallelism. Add about 10% headroom before counting KV cache and runtime allocations.
- Test a short prompt with one request. This isolates weight and basic execution problems from context and concurrency pressure.
- Reduce precision or model size if loading fails. Move from FP32 to BF16 or FP16, then consider 8-bit or 4-bit quantization or a smaller model.
- Reduce context if long prompts fail. Adjust
num_ctxor the corresponding engine setting to the real task requirement. - Reduce batch and parallel requests. Change one setting at a time and retest the longest realistic workload.
- Choose offload or more GPUs deliberately. Accept transfer overhead only when it is preferable to not running the model.
- Measure the result. Record first-response time, steady-state tokens per second, peak memory, error rate, and answer quality.
How should I choose between speed, context, quality, and cost?
No single setting optimizes all four. Use the following decision points to make the trade-off explicit.
- Need the highest answer quality: keep the highest precision that fits with headroom, and reduce context or concurrency before applying aggressive quantization.
- Need the lowest memory use: choose a smaller model or 4-bit representation, then keep context and parallelism conservative.
- Need interactive speed: keep the entire model on one supported GPU when possible, avoid CPU offload, preload it, and prevent accidental CPU fallback.
- Need service throughput: increase batch or parallel requests only while peak memory remains below the safety margin; throughput gains disappear when the engine starts failing or transferring data.
- Need long documents: reserve memory for the KV cache first, use the shortest useful context, and consider retrieval or summarization rather than permanently allocating the maximum window.
- Need the lowest hardware cost: quantize or offload, but verify that the slower response and any quality change are acceptable.
Compare model quality, tokens per second, context capacity, stability, and total hardware cost together. A model that technically loads but spends most of its time swapping or transferring data is not a practical success.
What should I check before buying more hardware?
Confirm that memory capacity, rather than a configuration error, is the limiting factor. A 24 GB GPU is a useful reference point because NVIDIA’s example places an 8-billion-parameter BF16 model at about 16 GB of weight memory, leaving room for KV cache and runtime overhead, but the same card may not support a much larger model or a high-concurrency workload.
- Measure the current model’s actual placement and peak allocation.
- Calculate the weight requirement at the precision you intend to run.
- Add headroom for context, batch, parallel requests, and runtime workspaces.
- Check whether your engine supports the new GPU architecture and desired precision.
- Verify power supply, cooling, case clearance, driver support, and the number of compatible GPU slots.
- Estimate whether a smaller model or quantized format would meet the task before paying for more VRAM.
More VRAM solves capacity only when the backend can use it. It does not automatically fix a CPU-only installation, an unsupported profile, or a context setting that allocates more KV cache than the workload needs.
Frequently Asked Questions
Can system RAM replace VRAM for a local AI model?
System RAM can hold offloaded weights, allowing a model to run when it does not fit entirely in VRAM. The transfer between CPU memory and the GPU is slower, so this approach is best when capacity matters more than interactive speed; it is not equivalent to having the same amount of VRAM.
How can I tell whether quantization hurt answer quality?
Create a small evaluation set from your real prompts and compare the original and quantized models for factual accuracy, instruction following, code correctness, and required output format. Keep the quantized version only if its memory and speed benefits outweigh any errors that matter for your task.
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It does not change the model’s learned weights, but it limits how much prompt and conversation history the model can see at once. If required information falls outside the shorter window, answers can become incomplete; summarization or retrieval can preserve the needed facts without allocating the maximum context every time.
Why does the first token take much longer than the rest?
The runtime may be loading model weights, compiling a graph, or allocating the initial KV cache before it emits a token. Compare a cold request with a second request while the model remains resident; if only the cold request is slow, preload or keep-alive settings can help at the cost of holding memory while idle.
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