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Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →To reduce GPU memory use during AI model inference, first identify whether memory is going to model weights, the key/value (KV) cache, or temporary runtime allocations. Then choose a matching fix: quantize weights, limit context length or concurrent requests, use a supported memory-efficient attention backend, or offload some model state to CPU memory. These measures affect different parts of the workload, so no single setting is a universal VRAM fix.
What is using GPU memory?
Inference—loading a model and generating outputs—has different memory demands from training. During inference, GPU memory is typically needed for three broad categories:
- Model weights: the stored parameters. Their memory use depends heavily on the model and the precision or quantization used to represent its weights.
- KV cache: data retained for the input and generated tokens. It grows as sequences get longer, and serving more sequences increases the active cache workload.
- Temporary allocations: runtime and attention operations can require additional memory beyond the weights and cache.
Record the GPU model and VRAM, checkpoint and parameter count, runtime, weight dtype or quantization, prompt length, generation limit, and number of concurrent sequences. If your runtime exposes peak memory, observe it separately during model loading and generation; a model that loads may still run out of memory once generation begins.
Reduce memory used by model weights
Use lower-precision or quantized weights
Quantization represents weights with fewer bits, which can reduce their memory footprint. Hugging Face’s current inference documentation illustrates the difference with a 70-billion-parameter Llama 2 model: it gives 256 GB for full-precision weights and 128 GB for half-precision weights. These are the guide’s illustrative figures, not a universal VRAM calculator or a guarantee that a model will fit on a particular GPU. Hugging Face: Optimizing inference
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Check that the chosen checkpoint and your model, GPU, and runtime support the precision or quantization format. Lower precision can affect output quality and speed; in some configurations, quantization can add latency. Compare representative outputs and latency as well as whether the model loads. vLLM likewise describes quantized models as using less memory at the cost of lower precision. vLLM: Conserving Memory
Limit memory used by context and concurrent requests
If GPU use rises with longer prompts, longer generations, or more active requests, the KV cache may be a significant part of the load. Reduce the maximum context or generation length to match the task, and limit how many sequences run at once. This can reduce memory demand, but it also restricts how much text the model can handle or how much work the server can process concurrently.
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For vLLM, its documentation identifies max_model_len and max_num_seqs as controls to consider when conserving memory. Check the documentation for your installed vLLM version before changing configuration, because syntax and behavior can vary. vLLM: Conserving Memory
Use a memory-efficient attention backend when supported
Attention implementations can differ in the size of the intermediate allocations they require. Hugging Face recommends considering FlashAttention 2 or PyTorch scaled dot product attention (SDPA) for memory-efficient attention when the model, GPU, and software stack support them. Confirm compatibility in your runtime’s documentation rather than forcing a backend that the hardware or model does not support. Hugging Face: Optimizing inference
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Consider CPU offload or a serving engine
Offload some model state
Device mapping or CPU offload can put some model state in system memory instead of GPU memory. This can help when the weights do not fit in VRAM, but shifting work or data to the CPU can affect performance. Support depends on the runtime and configuration, so check its current documentation and measure the result.
Use serving controls for multi-request workloads
For workloads serving multiple requests, a serving engine’s cache management can matter as well as the model’s raw weight size. The PagedAttention paper describes fragmentation and redundant KV-cache duplication as sources of wasted memory in serving. vLLM documents memory controls and cache behavior; these mechanisms are especially relevant to multi-request serving and are not automatically the best solution for a single local generation. PagedAttention paper (2023) · vLLM: Conserving Memory
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Apply changes in a controlled order
- Measure the workload: note the GPU, model, runtime, precision, prompt and generation lengths, and concurrency. Check memory peaks during loading and generation where possible.
- Match the fix to the bottleneck: if weights dominate, test a supported lower-precision or quantized checkpoint; if memory rises with sequence length or request count, cap context or concurrency.
- Check attention support: use FlashAttention 2 or SDPA only when supported by the exact model, GPU, and software stack.
- Try offload or serving-specific controls if needed: confirm current runtime documentation and account for possible speed costs.
- Re-measure after each change: leave headroom for runtime allocations and the intended context and concurrency. Check output quality and latency, not just whether the model starts.
Compare the trade-offs, not just the VRAM number
| Approach | Memory target | Trade-offs to check |
|---|---|---|
| Lower-precision or quantized weights | Model weights | Output quality, latency, and compatibility with the model, GPU, and runtime |
| Shorter context or fewer concurrent sequences | Active KV cache | Available context length and serving throughput |
| FlashAttention 2 or SDPA | Some temporary attention allocations | Hardware, model, and software compatibility |
| CPU offload or device mapping | GPU-resident model state | Runtime support and possible performance impact |
| Serving-engine cache controls | Cache management in serving workloads | Runtime-specific configuration and workload fit |
These options are not interchangeable: quantization targets weights, context and concurrency limits target active cache demand, attention backends can reduce some intermediate allocations, and offload shifts state to other memory. Some speed-oriented optimizations can use more memory, so assess peak allocated and reserved VRAM alongside quality, latency, and throughput rather than assuming every optimization saves memory. Hugging Face: Optimizing inference
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