Mistral Large 4: 1T Parameters You Might Actually Run

Home AI lab with a stacked GPU server rack glowing in a dark room, a monitor showing a neural network diagram

Mistral announced Mistral Large 4 on October 6, nicknaming it "Le Chonk", and the headline number is hard to miss: 1.05 trillion total parameters. Before you close the tab, the number that matters for local runners is the second one: 49 billion active parameters per token.

That split is the whole point of a mixture-of-experts (MoE) design. The model holds a trillion parameters of capacity, but each token only travels through a small set of expert sub-networks. Inference cost tracks the 49B active, not the 1T total. It also ships with a 1 million-token context window and multimodal input. Mistral calls it the best open-weights model from the US or Europe, and it is pitching the release at cybersecurity and sovereign deployments that cannot send data to a cloud API.

The catch: this is a public preview, not a download page. Mistral says the open weights follow later this month, under a license it has not published yet. Treat the benchmarks as company-reported until independent runners get their hands on the weights.

So could you run it at home? A 1T-parameter MoE still stores all trillion parameters somewhere, so you are looking at serious memory even with 4-bit quantization (Q4). On a single 24GB gaming GPU, no. On a stacked home server with a few hundred gigabytes of memory, plausibly, once the community works out the serving setup.

Before the weights land, run the math for your own rig with the VRAM calculator and see what fits.

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