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First identify whether the problem happens while the model loads, processes your prompt, or generates tokens. Those are different bottlenecks: a model can fit in memory while its context cache or other runtime allocations do not, and a model that loads successfully may still be running partly on the CPU. Check logs and device placement before changing settings or hardware.
Identify the slow or failing phase
Record the model and its parameter size, quantization or precision, runtime and version, CPU and GPU, available system RAM and GPU VRAM, context length, batch size, and concurrency. Then note when the symptom occurs:
- Loading: The model takes a long time to become available or fails as weights are loaded.
- Prompt processing: The model is available, but takes a long time to process the input before replying.
- Token generation: The reply starts, but subsequent tokens arrive slowly.
- Later allocation: The model loads, then runs out of memory during warm-up, cache allocation, or inference.
After each adjustment, use the same prompt and output length so the comparison is meaningful. There is no universal tokens-per-second target without a specified model and hardware configuration.
Fix slow model loading
Separate download time from loading time. vLLM recommends downloading the model first and passing a local model path when investigating load delays. A large checkpoint on shared or network storage may take longer to read, while high system-memory use can trigger disk swapping and slow the whole system. See the vLLM troubleshooting guide.
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- Confirm that the model is already downloaded and that the runtime is opening the intended local files.
- Check whether the model files are on a shared or network filesystem; test from local storage if that is practical.
- Watch system RAM and disk activity during loading. Frequent swapping indicates system-memory pressure, not a shortage of GPU VRAM.
More system RAM may help if measurements show system-memory pressure. It does not increase GPU VRAM, and it will not by itself fix a GPU allocation failure.
Diagnose an out-of-memory error by its failure point
Read the runtime logs to see which allocation failed. An OOM while loading weights differs from one during KV-cache allocation, warm-up, or inference. Model weights are only part of the memory budget: GPU memory may also be needed for the KV cache, activations, communication buffers, CUDA graphs, adapters, and other runtime state. NVIDIA’s NIM performance guide explains the distinction between model-weight memory and additional runtime requirements.
As an illustration of weights alone, NVIDIA gives a figure of 16 GB for Llama 3.1 8B in BF16 at one-way tensor parallelism. This is a model-weight estimate, not a claim that the model will run in 16 GB of VRAM: cache and runtime allocations still need space.
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Choose a change that addresses the failed allocation, and test one change at a time:
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems- Weights do not fit: Try a smaller model or a lower-memory precision or quantization, if acceptable for the task.
- Cache or context allocation fails: Reduce the context length or use a supported lower-memory KV-cache type.
- Batch or concurrent requests cause pressure: Reduce batch size or concurrency.
- Other applications are using the GPU: Close unnecessary GPU workloads and retry.
- The runtime supports CPU/GPU hybrid inference: Place fewer layers on the GPU if the GPU-memory limit is the constraint, understanding that this changes the performance trade-off.
Lower precision and quantization can affect output quality; the impact depends on the model and task. Check the current documentation for your installed runtime and test results on the work you actually need to do.
Check whether the model is using the GPU
A model that loads but responds slowly may be running on the CPU, or partly on it. Verify placement before tuning performance settings.
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Ollama
Run ollama ps and inspect the processor column to see where the loaded model is running. Ollama documents this command in its FAQ.
llama.cpp
Check the GPU-layer setting and device output. The llama.cpp server documentation describes --gpu-layers, which sets the maximum number of model layers placed in VRAM. The appropriate setting depends on the available memory and backend; more GPU layers are not an option if they do not fit.
Tune prompt processing and token generation separately
Prompt processing and token generation do not necessarily benefit from the same settings. The llama.cpp documentation notes that a larger physical batch size may improve prompt processing but consumes more memory. Reduce it when memory is tight; consider increasing it only when prompt processing is the bottleneck and memory headroom is available. The documentation also notes that some systems benefit from using more threads for batch processing than for generation. These are runtime-specific tuning options, not guaranteed speedups. See the llama.cpp server documentation.
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- AI NPU with XDNA 2 ARCHITECTURE - Powered by 16 “Zen 5” CPU cores, 50+ peak AI TOPS XDNA 2 NPU and a truly massive integrated GPU driven by 40 AMD RDNA 3.5 CUs, the Ryzen AI MAX+ 395 is a transformative upgrade and delivers a significant performance boost over the competition. The Ryzen AI Max+ 395 excels in consumer AI workloads like the llama.cpp-powered application: LM Studio. Shaping up to be the must-have app for client LLM workloads, LM Studio allows users to locally run the latest language model without any technical knowledge required and unleash their creativity and productivity.
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- EIGHT CHANNEL LPDDR5X - LPDDR5X is a new ground breaking memory small form factor installed on-board. With blazing speeds up to to 8000MT/s, it runs 1.5x faster than the DDR5 SODIMMs; 90% better performance over DDR5 SODIMMs in video conferencing and photo editing; 30% better performance in productivity apps; 4% better performance in digital content workloads.
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Reduce context or KV-cache memory when appropriate
Use a context window sized for the task rather than choosing a large value by default. Ollama’s FAQ documents 4096 tokens as its default context window and describes OLLAMA_CONTEXT_LENGTH and the num_ctx option for controlling context. Defaults and controls may vary by release, so check the installed version’s behavior.
Ollama documents these KV-cache types in its FAQ:
| Cache type | Documented memory use and trade-off |
|---|---|
f16 |
Default; comparison baseline for the cache types below. |
q8_0 |
Approximately half the memory of f16, with a small precision loss, according to Ollama. |
q4_0 |
Approximately one quarter the memory of f16, with a small-to-medium precision loss that may be more noticeable at higher context, according to Ollama. |
These are Ollama’s published descriptions, not guarantees for every model or task. Test output quality with your workload before adopting a lower-precision cache. The same Ollama FAQ says Flash Attention can significantly reduce memory as context grows and is enabled automatically on supported backends and devices; support is not universal.
Keep a model resident if repeated loading is the delay
Ollama says a model stays loaded for five minutes by default and that its API provides keep_alive controls. Keeping a model resident can avoid reloading it between requests made within that period, but it also keeps using memory. Other runtimes have their own residency behavior. Check the Ollama FAQ for the documented controls.
Choose a fix for the constrained resource
| Observed constraint | Potential response | Important trade-off |
|---|---|---|
| System RAM pressure or swapping during loading | Reduce competing memory use; investigate whether more system RAM or local model storage addresses the measured constraint. | System RAM does not add GPU VRAM. |
| GPU VRAM exhausted by weights | Try a smaller or lower-memory model, or reduce GPU placement where supported. | Model choice, precision, or CPU offload can change quality or speed. |
| GPU VRAM exhausted by cache or runtime allocations | Reduce context, batch size, or concurrency; consider a supported cache type with lower memory use. | Smaller batches can reduce prompt-processing throughput; cache precision may affect output. |
| Slow prompt processing with memory headroom | Test a larger physical batch size or runtime-specific batch-processing thread settings. | Batch size uses more memory, and speedups are workload- and system-dependent. |
| Repeated load delays between requests | Use the runtime’s model-residency controls where available. | A resident model continues to consume memory. |
Runtime flags, defaults, supported devices, and backends can change. Confirm settings in documentation for the installed version of Ollama, llama.cpp, or vLLM rather than assuming that one runtime’s controls apply to another.
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