What hardware do you need to run Mistral Small 3.2 24B Instruct?

Mistral Small 3.2 24B Instruct at Q4_K_M is 14 GB of weights, with a context ceiling of 128k tokens. Well-rounded, follows instructions tightly, decent function calling. Nothing exceptional. On the cheapest machine that runs it, a Framework Desktop, 32GB, it generates 9.7 tok/s at 32k of context, worked out from memory bandwidth.

How good is Mistral Small 3.2 24B Instruct, really?

On the Artificial Analysis Intelligence Index v4.3 it scores 7, which puts it in the Below every hosted tier band. Fine for simple, well-specified tasks. Noticeably less capable than anything the big labs sell today. See the whole table.

• Summarising — good
• Translation — good
• Everyday coding — usable
• Reasoning & maths — usable
• Agentic work — usable

Head to head with its neighbours on the leaderboard: vs Qwen3 32B above it, vs Qwen3 14B below. Also head to head: vs Devstral Small 2 24B, the current Mistral nearest it in size. One more model needs much the same memory at 32k of context: vs Gemma 3 27B it.

Is Mistral Small 3.2 24B Instruct still worth running?

Not on the two figures this site can compare. The intelligence index puts 14 of the current models here above Mistral Small 3.2 24B Instruct, and not one of them asks more of a machine: at 32k of context it needs 19.7 GB and scores 7, where the strongest of the 14, Qwen3.8 27B, needs 18.6 GB and scores 34 (xhigh reasoning effort; 22 without reasoning).

The cheapest machine still sold that holds Mistral Small 3.2 24B Instruct at 32k is the Framework Desktop, 32GB at $1,269, which holds Qwen3.8 27B as well. This site quotes Qwen3.8 27B at 10 tok/s estimated there and Mistral Small 3.2 24B Instruct at 9.7 tok/s estimated.

What Mistral Small 3.2 24B Instruct costs either way

Renting the same model costs $0.075 per million input tokens and $0.2 per million output (OpenRouter, cheapest active endpoint, checked 2026-10-29). Buying a machine only beats that if you use it hard enough, for long enough, that the hardware price divides down below the rental bill. What a million tokens costs each way puts the two prices side by side.

Machines that run Mistral Small 3.2 24B Instruct

Every machine here runs it, but not to the same length. The Strix Halo Framework Desktop, 32GB stops at 32k; the DGX Spark GB10 Grace Blackwell, 128GB takes it to 128k, this model's own ceiling. The weights are the same size on every machine; what differs is the memory left for the key-value cache, which grows with every token you keep.

The table is the cheapest machine in each family, not the only one that runs it: 44 of the 56 machines on this site hold it at 32k. Within a family only memory decides, so that is every Mac Studio and Strix Halo box at any size, every NVIDIA card with 24GB or more, every Mac mini and MacBook Pro with 32GB or more, the Radeon AI PRO R9700, 32GB and the DGX Spark, 128GB. One table sets every machine against the others: price, the memory its GPU can use, what it holds and how long it takes to pay for itself.

One machine per family, cheapest first. Speeds are measured where a public benchmark exists and estimated from memory bandwidth otherwise; the calculator says which for any configuration. The longest context is the longest setting the calculator offers that the machine still holds this model at, cache included, and no machine is shown taking it past its own 128k limit. Each one opens the calculator on that machine at that length.

For scale, the calculator starts from 80 tok/s for a hosted API and times a local machine against it. Nothing above reaches it, and the quickest is 63 tok/s.

The cheapest machine that runs it is sold with 32 GB. There is a page on what 32 GB runs, machine by machine: it names the 6 machines here sold at that size, and the 32 current models the roomiest of them holds at 32k.

It fits in less memory than that: the smallest size here that runs it at 32k is 24 GB, on the GeForce RTX 3090, 24GB at $1,499, card only, which hands a model 23 GB and is no longer sold.

Run Mistral Small 3.2 24B Instruct at 128k on the DGX Spark GB10 Grace Blackwell, 128GB

How much memory does Mistral Small 3.2 24B Instruct need at 128k?

Every figure above is priced at 32k of context. At 128k, this model's own ceiling, Mistral Small 3.2 24B Instruct needs 35.8 GB rather than 19.7 GB. The weights are the same either way; the key-value cache goes from 5.4 GB to 21.5 GB.

30 of the 37 machines here still sold at a published price hold Mistral Small 3.2 24B Instruct at 32k and 24 hold it at 128k. The cheapest still sold goes from the Framework Desktop, 32GB at $1,269 to the Corsair AI Workstation 300, 64GB at $1,700.

On the Corsair AI Workstation 300, 64GB it runs 9.7 tok/s estimated at 32k and 5.4 tok/s estimated at 128k. Every token generated reads the whole cache back, so a longer window is slower as well as bigger.

What has been measured running Mistral Small 3.2 24B Instruct

Every speed in the table above is worked out from memory bandwidth, and that is not because nobody has run Mistral Small 3.2 24B Instruct. Two machines here have a published speed for it, and the table above has none of them: every one is still sold but not the cheapest of its family. They are below, as published and again at 32k of context, which is what every other speed on this page is taken at.

A published figure is what its source reports, at the window the model was run at. The 32k figure is that measurement read at 32k of context: every token generated reads the weights and the whole key-value cache, so the longer the window the slower it runs. Where two memory tiers of one machine carry the same figure, the speed is set by the bandwidth of the chip they share rather than by the amount of memory. Each published figure links the source that took it.

Two more machines, at a shorter window

The table above lists the machines that run it at 32k, the context the calculator starts on, where Mistral Small 3.2 24B Instruct needs 20 GB. Two more machines hold it at a shorter window, because the weights are the same size either way and the key-value cache is not. The cheapest of those is the Mac mini M6, 24GB at $1,099. The table above starts at $1,269.

One machine per family, cheapest first, from the same list as the table above. Each window is the longest setting the calculator offers that the machine still holds this model at, and it opens the calculator on that machine at that length. What it needs there is the weights plus the key-value cache at that window, measured against the memory each machine's GPU can address, which that machine's own page gives.

The specifics

Parameters

24B

Quantisation

Q4_K_M

Weights on disk

14 GB

KV cache

5.4 GB at 32k context. How weights and cache add up.

Maximum context

128k tokens

Licence

Apache 2.0

Sources

unsloth/Mistral-Small-3.2-24B-Instruct-2506-GGUF, mistralai/Mistral-Small-3.2-24B-Instruct-2506