Sunk Cost sunkcost.ai Data checked 2026-09-03

What hardware do you need to run Ministral 3 14B?

Ministral 3 14B at Q4_K_M is 8.2 GB of weights, with a context ceiling of 256k tokens. Matches the older Mistral Small 24B on the index at a third of the size. On the cheapest machine that runs it, a Mac mini M6, 24GB, it generates 9.4 tok/s at 32k of context, worked out from memory bandwidth.

Cheapest machine that runs itMac mini M6, 24GB at $1,099
Cheaper at a shorter windowMac mini M6, 16GB at $899, which holds it at 8k
Fastest machine that runs itNVIDIA RTX PRO 6000 Blackwell, 96GB at $18,000card only — 107 tok/s estimated at 32k context
Honest answer on costPays back in 34 years at 500k tokens a day.

How good is Ministral 3 14B, really?

On the Artificial Analysis Intelligence Index v4.3 it scores 6, 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.

Head to head with its neighbours on the leaderboard: vs Llama 4 Scout 17B-16E above it, vs Qwen3 8B below. Two more models need much the same memory at 32k of context: vs gpt-oss-20b, vs Qwen3 14B.

What Ministral 3 14B costs either way

Renting the same model costs $0.2 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 Ministral 3 14B

Every machine here runs it, but not to the same length. The Mac mini M6, 24GB stops at 32k; the DGX Spark GB10 Grace Blackwell, 128GB takes it to 256k, which is this model's own ceiling and the longest the calculator offers. 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.

MachinePriceSpeed at 32kLongest contextPay-back
Mac mini M6, 24GB $1,099 9.4 tok/s estimated 32k Pays back in 34 years Run the numbers
Strix Halo Framework Desktop, 32GB $1,269 14 tok/s estimated 64k Pays back in 40 years Run the numbers
AMD Radeon AI PRO R9700, 32GB $1,299card only 41 tok/s estimated 128k Pays back in 40 years Run the numbers
NVIDIA GeForce RTX 5090, 32GB $1,999card only 91 tok/s estimated 128k Pays back in 60 years Run the numbers
MacBook Pro M5 (14-inch), 32GB $2,399 8.4 tok/s estimated 64k Pays back in 74 years Run the numbers
Mac Studio M5 Max, 36GB $2,499 25 tok/s estimated 64k Pays back in 75 years Run the numbers
DGX Spark GB10 Grace Blackwell, 128GB $4,699 15 tok/s estimated 256k Pays back in 151 years Run the numbers

The table is the cheapest machine in each family, not the only one that runs it: 50 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 16GB or more, every Mac mini and MacBook Pro with 24GB 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 256k 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. 1 of the 7 above reaches it, and the slowest is 8.4 tok/s.

The cheapest machine that runs it is sold with 24 GB. There is a page on what 24 GB runs, machine by machine: it names the 7 machines here sold at that size, and the 28 current models the roomiest of them holds at 32k, this one among them.

It fits in less memory than that: the smallest size here that runs it at 32k is 16 GB, on the GeForce RTX 4080, 16GB at $1,199, card only, which hands a model 15 GB and is no longer sold.

Run Ministral 3 14B at 256k on the DGX Spark GB10 Grace Blackwell, 128GB

How much memory does Ministral 3 14B need at 256k?

Every figure above is priced at 32k of context. At 256k, which is this model's own ceiling and the longest the calculator offers, Ministral 3 14B needs 51.2 GB rather than 13.6 GB. The weights are the same either way; the key-value cache goes from 5.4 GB to 42.9 GB.

33 of the 37 machines here still sold at a published price hold Ministral 3 14B at 32k and 13 hold it at 256k. The cheapest still sold goes from the Mac mini M6, 24GB at $1,099 to the Framework Desktop, 128GB at $3,449.

On the Framework Desktop, 128GB it runs 14 tok/s estimated at 32k and 3.8 tok/s estimated at 256k. Every token generated reads the whole cache back, so a longer window is slower as well as bigger.

Three more machines, at a shorter window

The table above lists the machines that run it at 32k, the context the calculator starts on, where Ministral 3 14B needs 14 GB. Three 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, 16GB at $899. The table above starts at $1,099.

MachinePriceLongest window it fitsNeeds there
Mac mini M6, 16GB $899 8k 9.6 GB
MacBook Air M5 (13-inch), 16GB $1,299 8k 9.6 GB
MacBook Pro M5 (14-inch), 16GB $1,999 8k 9.6 GB

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
14B
Quantisation
Q4_K_M
Weights on disk
8.2 GB
KV cache
5.4 GB at 32k context — All 40 layers full attention; no sliding window. How weights and cache add up.
Maximum context
256k tokens
Licence
Apache 2.0
Sources
mistralai/Ministral-3-14B-Instruct-2512