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There is no universal VRAM-per-token figure for GGUF models. A longer context generally needs more runtime memory because the model must hold more prompt and generation state, including its key-value (KV) cache. The actual requirement depends on the model, quantization, runtime, cache settings, GPU placement and—when serving multiple requests—the number of parallel slots.
What does context size mean?
Context size is the runtime’s limit for the tokens it can handle in a prompt and generation. Prompt tokens and generated tokens both use that available context, so a long input leaves less room for the response. The model must also support the context length you want: increasing a runtime setting by itself does not prove that the model can use the longer context correctly.
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In llama.cpp’s documented completion tool, -c N or --ctx-size N sets the prompt context size. Its documentation lists 4096 as the default for that tool and 0 as a request to load the model’s value. These are tool-specific documented values, not universal defaults for every launcher. The same documentation explains that a model built for a longer context can use a larger setting; its example of scaling from 4096 to 32768 with a factor of 8 applies to that illustrative case, not automatically to other models. See the llama.cpp completion documentation.
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The GGUF file describes model weights, but its on-disk size is not a complete runtime memory budget. How many model layers are placed on the GPU is configurable, and the runtime also allocates memory for cache and other buffers. As context grows, KV-cache requirements are one reason runtime memory can increase. The final allocation depends on the exact model and runtime configuration, so the filename or file size alone cannot establish whether a setup will fit.
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Which settings change memory use?
- Model and quantization: Identify the exact model and weight quantization; these determine the model’s weight footprint and supported context.
- Context target: Choose a realistic length that covers both the prompt and the expected generation, while staying within the model’s supported context.
- K and V cache types: llama.cpp exposes separate data-type settings for the key and value caches. Its server README documents
--cache-type-kand--cache-type-v, with f16 shown as the default there. Other cache representations are available, but the documentation cited here does not quantify their memory savings or quality trade-offs. - GPU placement and splitting:
--gpu-layerssets the maximum number of layers placed in VRAM in the documented llama.cpp server. Multi-GPU split modes affect where layers and, depending on the mode, KV data are placed. - Concurrency: A server’s parallel-slot configuration adds a sizing consideration. A memory estimate for one request should not be treated as a guarantee for concurrent requests.
- Runtime buffers and backend: Allocations depend on the selected software build and backend as well as the model and settings.
The llama.cpp server README documents these server options, including --fit, which adjusts unset arguments to fit device memory. Defaults and supported modes can change; check the --help output for the version you actually run.
How should you estimate memory for your setup?
- Confirm the model and quantization. Use the exact GGUF you plan to load, not a different quantization or a similarly named model.
- Check the model’s supported context. Confirm that the target length is supported by that model. Do not assume that raising
--ctx-sizeextends a model’s capability. - Set a practical context target. Include both incoming prompt tokens and the generation you expect. Avoid selecting a maximum length you do not need.
- Choose cache types and placement. Decide how much of the model to place on the GPU, which K/V cache types to use, and—if using more than one GPU—how to split work.
- Account for server slots. If the server will handle concurrent requests, size and test for the intended slot configuration rather than extrapolating from a single request.
- Run the exact build and inspect its allocation output. Use the matching version’s help text and startup or allocation logs. Those provide more useful evidence for your configuration than a generic estimate based on GGUF file size.
What can you change if the configuration does not fit?
- Reduce the context target, provided it still meets the task’s needs.
- Use a smaller model or a different weight quantization.
- Try different K or V cache types, then verify actual allocation and output behavior for the exact model and runtime.
- Place fewer layers on the GPU, accepting that changing placement can affect execution performance.
- For multi-GPU setups, review the supported split modes and available memory on each device.
- Reduce parallel slots if serving multiple requests and concurrency is contributing to the allocation.
These are configuration choices, not guarantees of a particular speed, memory saving or output quality. Check the result with the exact model, software version and backend you intend to use.
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Can you compare context options with one universal VRAM table?
No. The available official documentation describes relevant settings but does not provide a standard benchmark table that maps context length to VRAM across models and runtimes. Exact budgets vary with architecture, weight quantization, cache types, runtime buffers, GPU placement, split mode and concurrency. A numeric estimate is meaningful only when it identifies the model and quantization, context, cache configuration, runtime build and placement conditions.
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