Mistral AI · llama
Can I run mistral-large?
Mistral Large 2 is Mistral's new flagship model that is significantly more capable in code generation, mathematics, and reasoning with 128k context window and support for dozens of languages.
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 contextmistral-large 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 |
|---|---|---|---|
| mistral-large:123b-instruct-2407-q4_0123B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_0 | 64.3 GB | ~71.2 GB |
| mistral-large:123b-instruct-2411-q4_0123B parameters (tag)128K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_0 | 64.3 GB | ~71.2 GB |
| mistral-large:123b-instruct-2407-q4_K_M123B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 68.2 GB | ~75.4 GB |
| mistral-large:123b123B parameters (tag)128K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q4_K_M | 68.2 GB | ~75.4 GB |
| mistral-large:123b-instruct-2407-q5_K_M123B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q5_K_M | 80.5 GB | ~88.8 GB |
| mistral-large:123b-instruct-2411-q5_K_M123B parameters (tag)128K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q5_K_M | 80.5 GB | ~88.8 GB |
| mistral-large:123b-instruct-2407-q6_K123B parameters (tag)32K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q6_K | 93.7 GB | ~102.9 GB |
| mistral-large:123b-instruct-2411-q6_K123B parameters (tag)128K documented maximum contextBuild sourceCatalog checked 2026-10-13 | Q6_K | 93.7 GB | ~102.9 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 mistral-large locally
- 1.Install Ollama for Windows, macOS or Linux.
- 2.Download this exact build in a terminal:
ollama pull mistral-large:123b-instruct-2407-q4_0 - 3.Check the source for the build's task. For text generation, run
ollama run mistral-large:123b-instruct-2407-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 mistral-large
- How much VRAM do I need to run mistral-large?
- At 4K context, the smallest published build of mistral-large (Q4_0) needs roughly 71.2 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 mistral-large without a dedicated GPU?
- A supported CPU runtime can use system RAM. The smallest listed build needs an estimated 71.2 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 mistral-large download?
- The smallest published build is 64.3 GB. There are 12 builds in total, the largest being 228.4 GB. Leave some extra free disk space beyond the download itself.
- How do I run mistral-large locally?
- Install Ollama, then download a specific build with "ollama pull mistral-large:123b-instruct-2407-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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Model data from the official Ollama registry · verified 13/10/2026