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.
How good is it, 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. Score source. See the whole table.
- Summarising — good
- Translation — good
- Everyday coding — usable
- Reasoning & maths — usable
- Agentic work — usable
What it 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 2026-09-03). 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.
Machines that run it
| Machine | Price | Speed at 32k | Pay-back | |
|---|---|---|---|---|
| Strix Halo Framework Desktop, 32GB | $1,269 | 9.7 tok/s estimated | Pays back in 163 years | Run the numbers |
| Mac mini M6, 32GB | $1,299 | 6.5 tok/s estimated | Pays back in 134 years | Run the numbers |
| MacBook Pro M5 (14-inch), 32GB | $2,399 | 5.8 tok/s estimated | Pays back in 263 years | Run the numbers |
| Mac Studio M5 Max, 36GB | $2,499 | 18 tok/s estimated | Pays back in 234 years | Run the numbers |
| DGX Spark GB10 Grace Blackwell, 128GB | $4,699 | 10 tok/s estimated | Pays back in 640 years | Run the numbers |
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 specifics
- Parameters
- 24B
- Quantisation
- Q4_K_M
- Weights on disk
- 14 GB
- KV cache
- 5.4 GB at 32k context
- Maximum context
- 128k tokens (128k)
- Licence
- Apache 2.0
- Sources
- source 1, source 2