Mistral Large 4: Medium 3.5 comparison no longer gives an unsourced RAM figure; it points to that checker instead

• Page updated

• Sources checked

• Specs from Mistral's docs and announcement

• Estimates labeled until the model card ships

• Jump to the checker ↓ Mistral Large 4 ("le Chonk", Mistral's own nickname for it) is a 1.05-trillion-parameter mixture-of-experts model with 52B parameters active per token (49B not counting embeddings and output layers). Mistral released it as a public preview on October 6, 2026; the open weights are announced for the end of October, with October 31 as the current ETA on Mistral's Hugging Face page. Here is how much memory it takes to run it yourself.

Short answer: the weights alone take about 2.1 TB at BF16, 1.05 TB at FP8, ~0.56–0.63 TB at 4-bit and ~0.28–0.51 TB at 2–3-bit GGUF quants. Only 52B parameters are active per token (49B not counting embeddings and output layers), but all 1.05T must be held in memory. By capacity alone that is an 8-GPU server at FP8, or a 512 GB Mac Studio for the smallest quants; it will not fit an RTX 4090/5090. These are memory estimates; runtime support is unknown until the weights ship.

Weights: announced for the end of October 2026. Every figure below is an estimate from the published parameter count until an official model card lists file sizes.

How much VRAM does Mistral Large 4 need? #

Weights = parameters × bits per weight ÷ 8. "Block" is the storage rate of the format itself; GGUF "_K"/"_M" files keep some tensors at higher precision, so we assume a slightly higher whole-model average (the number used for the size). Device counts add 10% for KV cache and buffers and assume a usable budget per device (H200 131 GB, B200 168 GB, H100 74 GB, 512 GB Mac 460 GB). They are theoretical capacity minimums, not validated deployments.

Block bits: llama.cpp ggml-common.h; NVFP4 stores an 8-bit scale per 16 values, MXFP4 per 32. Whole-model averages for this architecture are unknown until real files exist; set your own in the calculator. Below ~3 bits, quality on a model this size is untested.

Mistral Large 4 VRAM & memory calculator #

[Open in a full page ↗](https://tools.localrigcheck.com/mistral-large-4/)

 private checker…

• Developer

• Mistral AI Official

• Total parameters

• 1.05 trillion (Mistral docs) Official

• Active parameters

• 52B per token Official

• Context

• 1M tokens (Mistral docs, API model page) Official

• Weights

• open weights announced for the end of October 2026 Official

• API

• mistral-large-4-0 Official

• Ollama

• only the cloud tag mistral-large-4:cloud Official

Can you run Mistral Large 4 on a consumer GPU? #

Not on GPUs alone: even IQ1_S (~210 GB) is more than seven RTX 5090s. If runtimes add support once the weights ship, the home-scale options by memory capacity are:

• One 512 GB Mac Studio (M3 Ultra) : room for Q2_K, IQ2_XXS or IQ1_S under our assumptions. Q3_K_M (~512 GB + overhead) already needs two machines. Requires MLX or llama.cpp support for the model.
• Two clustered 512 GB Mac Studios : room for 4-bit (~560–630 GB), via clustering software such as exo or llama.cpp RPC.
• A server with 512 GB–1 TB of RAM and one large GPU : llama.cpp-style MoE offload keeps shared layers on the GPU and experts in RAM. The idealized batch-one estimate on 8-channel DDR5 (~300 GB/s) is ~10 tokens/s at Q4_K_M and ~15 at Q2_K under those assumptions; GPU-resident layers, batching or speculative decoding change it.

Mixture-of-experts helps speed, not memory: only 52B parameters work per token, but the router can pick any expert, so all of them must be loaded.

Self-host vs. the Mistral API #

The comparison is inside the private calculator. Open the cost calculator →.

Mistral Large 4 specs #

FAQ #

How much VRAM does Mistral Large 4 need? #

By our estimate about 2.1 TB at BF16, 1.05 TB at FP8 and roughly 560–630 GB at 4-bit for the weights alone, plus memory for the KV cache. At FP8 that is the capacity of an 8-GPU server such as 8× B200 or 8× MI300X. Official file sizes are not published yet.

Can Mistral Large 4 run on an RTX 4090 or RTX 5090? #

No. Even a ~1.6-bit quant is about 210 GB, far beyond one 24–32 GB card. A server with enough system RAM could offload the experts to the CPU if a runtime supports the model, with speed limited by RAM bandwidth.

Can I run Mistral Large 4 with a 24 GB GPU and 128 GB of RAM? #

Not by our capacity estimate. 24 GB of VRAM plus 128 GB of RAM is 152 GB, and even the smallest quant in the table above, IQ1_S, is about 210 GB before the KV cache. Off the experts to system RAM does not shrink them: the router can pick any expert, so they all have to fit somewhere. The weights are not downloadable yet, so local runtime support and setups that stream experts from an SSD are untested; we know of no measurement. On such a PC, Mistral's API or Ollama's cloud model is the way to use it. A dense model that does fit there is Mistral Medium 3.5 (128B): by our estimate its 4-bit GGUF runs on a 24 GB GPU with most of its layers in system RAM, slowly (the Medium 3.5 checker shows the split for your RAM). See the Mistral Medium 3.5 checker.

Can I run Mistral Large 4 on a Mac Studio? #

By memory capacity, a 512 GB M3 Ultra can hold Q2_K, IQ2_XXS or IQ1_S quants; Q3_K_M and 4-bit need two clustered machines. That also requires MLX or llama.cpp support for the model, which is not confirmed yet.

Why does a 52B-active model need memory for 1.05T parameters? #

In a mixture-of-experts model a router picks different experts for every token, so all experts must be loaded. Active parameters decide speed (bytes read per token); total parameters decide memory.

What is Le Chonk (Mistral Chonk)? #

Le Chonk is Mistral AI's own nickname for Mistral Large 4: the announcement says "Unofficially ML4, very officially: le Chonk." It is the same 1.05T-parameter model with 52B active parameters, so every memory figure on this page applies to it.

When will Mistral Large 4 weights be released? #

Mistral's announcement says it will release the weights "by the end of the month" (October 2026), and its Hugging Face page for the model, mistralai/Mistral-Large-4.0-1T05-A52B, lists October 31, 2026 as the current ETA (checked October 8, 2026). Community GGUF, MLX or local Ollama builds need both the weights and runtime support; no date for those is confirmed.

Is Mistral Large 4 on Ollama? #

Only as a cloud model so far. Ollama's library lists mistral-large-4:cloud (checked October 8, 2026), which runs on Ollama's servers, not on your hardware, so none of the memory figures on this page apply to it. Ollama lists no local tags, and no downloadable weights are out yet (both checked October 8, 2026); whether Ollama or llama.cpp will support the architecture locally is not known until they ship. Mistral's own API is the other way to use it today.

Sources #

- [Mistral docs: Mistral Large 4 model page](https://docs.mistral.ai/models/mistral-large-4) (1.05T total and 52B active parameters, vision encoder, context, API price)
- [Mistral AI: Mistral Large 4 announcement](https://mistral.ai/news/mistral-large-4) (parameters, pricing, release plan, the "le Chonk" nickname)

• Hugging Face: mistralai/Mistral-Large-4.0-1T05-A52B (placeholder without files; 49B per token, 52B including embeddings and output layers; October 31 ETA; checked October 8, 2026)

- [Ollama library: mistral-large-4 tags](https://ollama.com/library/mistral-large-4/tags) (only the cloud tag`mistral-large-4:cloud` ; checked October 8, 2026)
- [llama.cpp ggml-common.h](https://github.com/ggml-org/llama.cpp/blob/master/ggml/src/ggml-common.h) (GGUF block formats)
- [MLX LM README](https://github.com/ml-explore/mlx-lm/blob/main/README.md) (raising the macOS GPU memory limit)
- [Apple: Mac Studio (2025) tech specs](https://support.apple.com/en-au/122211) (819 GB/s, 512 GB)

• Device budgets, overhead and whole-model averages are RigCheck assumptions, stated above.