Mistral Medium 3.5 — local AI model by Mistral AI
Written by Jakub Rusinowski · Last updated
Mistral's first merged flagship: one dense 128B model that folds the reasoning (Magistral) and coding (Devstral 2) lines into a single checkpoint, with image input and a 256K context. Dense means every parameter is read for every token, so it is slower per token than a mixture-of-experts model of the same size, but at about 78 GB in Q4_K_M it fits a 128 GB Mac or a pair of 48 GB cards. Its "Modified MIT" licence withholds every right from companies above $20M monthly revenue.
Variants
The smallest Mistral Medium 3.5 variant needs about 78 GB of VRAM at Q4_K_M — quantized weights plus framework overhead, before any KV cache.
| Model | VRAM at Q4 | VRAM | Context | Run it |
|---|---|---|---|---|
| Mistral Medium 3.5 128B → 128B | ~77.9 GB | 262,144 | ollama run mistral-medium-3.5:128b |
Memory is quantized weights plus overhead at Q4_K_M, from the same engine as the GPU & VRAM checker.
How to run Mistral Medium 3.5 locally
Install Ollama, then pull the tag.
ollama run mistral-medium-3.5:128bPick a size above for its own VRAM figure, speed estimate and install command.
Licence
Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.
Applies to: Mistral Medium 3.5 128BRecommended GPU
The cheapest catalogued GPU that runs Mistral Medium 3.5 locally (min 78 GB VRAM) is the NVIDIA DGX Spark (128 GB).
Mistral Medium 3.5 — frequently asked questions
How much VRAM does Mistral Medium 3.5 need?
Mistral Medium 3.5 needs about 78 GB VRAM at Q4_K_M quantization for its smallest variant. Variants: Mistral Medium 3.5 128B (78 GB, Q4_K_M). On Apple Silicon, unified memory counts toward this requirement.
Can I run Mistral Medium 3.5 on an RTX 4090 (24 GB)?
Mistral Medium 3.5's smallest variant needs about 78 GB, which exceeds a single RTX 4090 (24 GB). Use multiple GPUs, a higher-VRAM card, or Apple Silicon with large unified memory.
What quantization should I use for Mistral Medium 3.5?
Q4_K_M is the best balance of quality and VRAM for Mistral Medium 3.5 in most cases. Choose Q8_0 for near-lossless quality if you have spare VRAM, or smaller quants (Q3/Q2) only when memory is tight.
How do I run Mistral Medium 3.5 with Ollama?
Install Ollama, then run: ollama run mistral-medium-3.5:128b. This downloads Mistral Medium 3.5 and starts a local, OpenAI-compatible endpoint — no internet connection is needed after the initial download.