Ollama Library · llama
Can I run vicuna?
General use chat model based on Llama and Llama 2 with 2K to 16K context sizes.
Parameter counts and memory belong to each build below. A family can contain several sizes and tasks; fitting in memory is not a quality ranking.
Calculate memory for a build and contextvicuna system requirements
Memory needed at 4K context, counting model weights, KV cache, compute buffers and runtime overhead. Download sizes come from the official Ollama registry.
| Build | Quantization | Download | Memory needed |
|---|---|---|---|
| vicuna:7b-q4_07B parameters (tag)2K documented maximum contextThe displayed memory scenario exceeds this build's supported context.Build sourceCatalog checked 2026-10-13 | Q4_0 | 3.6 GB | ~6.2 GB |
| vicuna:7b7B parameters (tag)4K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_0 | 3.6 GB | ~6.2 GB |
| vicuna:7b-v1.5-16k-q4_07B parameters (tag)16K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_0 | 3.6 GB | ~6.2 GB |
| vicuna:7b-q4_K_M7B parameters (tag)2K documented maximum contextThe displayed memory scenario exceeds this build's supported context.Build sourceCatalog checked 2026-10-13 | Q4_K_M | 3.8 GB | ~6.5 GB |
| vicuna:7b-v1.5-q4_K_M7B parameters (tag)4K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 3.8 GB | ~6.5 GB |
| vicuna:7b-v1.5-16k-q4_K_M7B parameters (tag)16K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 3.8 GB | ~6.5 GB |
| vicuna:7b-q5_K_M7B parameters (tag)2K documented maximum contextThe displayed memory scenario exceeds this build's supported context.Build sourceCatalog checked 2026-10-13 | Q5_K_M | 4.5 GB | ~7.2 GB |
| vicuna:7b-v1.5-q5_K_M7B parameters (tag)4K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q5_K_M | 4.5 GB | ~7.2 GB |
Showing the 8 smallest non-alias builds. Choose any build and change the context. A context above the build's documented maximum is a hypothetical calculation, not a supported setting.
How to run vicuna locally
- 1.Install Ollama for Windows, macOS or Linux.
- 2.Download this exact build in a terminal:
ollama pull vicuna:7b-q4_0 - 3.Check the source for the build's task. For text generation, run
ollama run vicuna:7b-q4_0. Base/text builds may need a prompt template; an instruct build is usually the appropriate starting point for a conversation.
Frequently asked questions about vicuna
- How much VRAM do I need to run vicuna?
- At 4K context, the smallest published build of vicuna (Q4_0) needs roughly 6.2 GB of GPU memory once weights, KV cache, compute buffers and runtime overhead are counted. Bigger quantizations and longer context windows need more. It can also run partly or entirely on the CPU using system RAM, more slowly.
- Can I run vicuna without a dedicated GPU?
- A supported CPU runtime can use system RAM. The smallest listed build needs an estimated 6.2 GB at 4K context, plus headroom for your operating system. Check the build's runtime requirements. We have not measured its speed on your CPU.
- How big is the vicuna download?
- The smallest published build is 3.6 GB. There are 42 builds in total, the largest being 60.6 GB. Leave some extra free disk space beyond the download itself.
- How do I run vicuna locally?
- Install Ollama, then download a specific build with "ollama pull vicuna:7b-q4_0". For a build that supports text generation, use ollama run with the same tag. Check the model source for its task and prompt format.
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Or start from your memory: 8 GB VRAM, 12 GB VRAM, 16 GB VRAM, 24 GB VRAM or a 16 GB Mac.
Model data from the official Ollama registry · verified 13/10/2026