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How this result is calculated
Matching GPU options
smallest viable pools firstCalculator assumptions
Weights: parameters x bits per weight / 8, or the published file size where a real GGUF exists and we have recorded it. Observed beats computed.
KV cache: layers x KV heads x head dimension x context x K/V tensors x bytes per element. Concurrent sequences multiply it only on Ollama, which allocates memory per request; llama.cpp, LM Studio, vLLM and ExLlamaV2 share one pool across sequences. Architecture-aware: MLA and sliding-window models cache far less. Quantizing the cache with -ctk/-ctv shrinks it further.
Runtime: a fixed floor plus a small percentage, because most of this cost is CUDA context and compute buffers that do not grow with the model. llama.cpp 0.75 GB + 2%, Ollama 0.9 GB + 3%, LM Studio 1 GB + 3%, vLLM 2 GB + 5%, ExLlamaV2 0.6 GB + 2%. Changed 2026-08-12 from a flat 15%, which overstated large models badly and understated small ones. Screenshots taken before that date show the old figures.
Hardware: multi-GPU fits include extra headroom for PCIe and tensor-parallel synchronization.
GGUF files: read entirely in your browser, header only. The file is never uploaded. Weight size is measured from the file itself.
Domain for sale: voxllm.com
Got your number? Now see it the other way round: what your GPU can run →
How much VRAM do I need to run an LLM?
Three things, added together: the model's weights, the KV cache for your context, and what the inference engine keeps for itself. This VRAM calculator adds them for the model, quantization, context length and backend you pick above, and the default view shows how it lands: Qwen3.8 27B at Q4_K_M with 8,192 tokens of context in llama.cpp needs 15.3 GB of weights, 0.6 GB of KV cache and 1.1 GB of overhead, 17.0 GB in total.
- Weights are the model file. Where we have recorded a published GGUF file's size, the calculator uses that measured size instead of an estimate from the parameter count, because the two can differ by a lot at low bit widths. For every other model it multiplies parameters by bits per weight.
- The KV cache is the memory that holds your conversation, and it grows with every token of context. It is usually what decides whether a card that fits the model also fits the chat. On Qwen3.8 27B it is 0.6 GB at 8K and about 16 GB at the full 262K window.
- Overhead depends on the engine: llama.cpp 0.75 GB plus 2%, Ollama 0.9 GB plus 3%, LM Studio 1 GB plus 3%, vLLM 2 GB plus 5%, ExLlamaV2 0.6 GB plus 2%. A single card then keeps 5% spare, because filling one to the last megabyte fails in practice.
Have a GGUF file already? Choose it in the calculator's GGUF field and it reads the model's header in your browser. Nothing uploads. It works as a GGUF VRAM calculator for files that are not in the list yet.
Start from the other side if you know your card and not your model: the GPU checker lists what your graphics card can run, the Mac checker does the same for Apple Silicon, and the speed checker estimates tokens per second once it fits.
FAQ
How do I calculate VRAM for an LLM?
Add the weights (the model file size, or parameters times bits per weight divided by 8), the KV cache for your context length, and the engine's overhead, then keep about 5% spare on a single card. This LLM GPU memory calculator does all three and shows each part separately.
How much VRAM does a 27B model need?
It depends on the model's attention design, so pick yours above. Qwen3.8 27B at Q4_K_M comes to 17.0 GB with 8,192 tokens of context in llama.cpp using unsloth's 16.5 GB file, and about 18 GB with bartowski's 17.4 GB file. Either way that is a 20 GB or 24 GB card, not a 16 GB one. Longer context raises it, because the KV cache grows with every token.
Does context length change how much VRAM I need?
Yes. The weights stay the same size, but the KV cache grows with every token you keep: on Qwen3.8 27B it is 0.6 GB at 8K and about 16 GB at 262K, more than the 15.3 GB of weights. Set the context window to the length you actually use before you trust the total.
Does this VRAM calculator upload my GGUF file?
No. The calculation runs in your browser, and the GGUF reader opens only the file's header on your machine, so the file never leaves it. There is no signup.