About Mistral Large 3
Mistral Large 3 is Mistral's 675B-parameter model released in December 2025, with a 256K-token context window. It is a mixture-of-experts model: all 675B parameters must sit in memory, but only ~41B are active per token, which is what makes it fast for its size. It uses a latent-attention (MLA) design that compresses its KV cache, keeping even very long contexts cheap in memory: its KV cache is about 0.6 GB at an 8K context, 9.2 GB at 128K, and 18.4 GB at the full 256K window (FP16 cache).
Mistral ships FP8-quantised weights as the primary release; community GGUF quants (Q4–Q6) are what most people run locally. Totals above include the KV cache and a realistic framework overhead, so they are what you should expect to see in practice rather than just the download size. Weight sizes are calibrated against real GGUF files — see themethodology.
Frequently asked questions
How much VRAM does Mistral Large 3 need?
At Q4_K_M with an 8K context, Mistral Large 3 needs about 455 GB (weights 412 GB + KV cache + overhead). No single consumer or workstation device fits it — it needs multi-GPU or datacenter hardware.
Can an RTX 4090 (24GB) run Mistral Large 3?
Not fully in VRAM. Mistral Large 3 needs about 455 GB even at Q4_K_M, so a 24 GB card would have to offload layers to system RAM at a large speed penalty.
Can a Mac run Mistral Large 3?
Not on any current configuration — even a 512 GB Mac Studio's ~384 GB GPU allowance is below the ~455 GB this model needs.
Related
VRAM calculator for any model ·Token counter · Mistral Large 3 API pricing
Last updated 2026-08-03. Architecture figures from the model's published config.json; see themethodology.