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

Can a Mac mini M6, 32GB run local LLMs?

Yes — 22 of the 48 current models on this site fit in its 21 GB of usable memory, and four more if you keep the window shorter than 32k. The strongest it holds, Qwen3.8 27B, runs at 6.9 tok/s with a 32k window, worked out from memory bandwidth. Whether that saves you money is a different question: at $1,299 and 500k tokens a day, it pays back in 82 years.

Price$1,299
Memory32 GB, about 21 GB of it addressable by the GPU at 170 GB/s
Best model it runsQwen3.8 27B — Sonnet-class, 6.9 tok/s estimated
Pay-back against the APIPays back in 82 years at 500k tokens a day

Run the numbers on this machine

What the Mac mini M6, 32GB runs

They do not all hold the same window. The weights are a fixed size, but the key-value cache grows with every token you keep, so what is left of 21 GB after the weights is how far the context goes. On 5 of the 12 below, this machine's memory is what runs out first: Qwen3.6 27B stops soonest, at 32k. The rest reach 256k, where the calculator's list ends, or the longest setting their own context limit allows.

ModelSpeedClassMemoryLongest context
Qwen3.8 27BQ4_K_M 6.9 tok/s estimated Sonnet-class 19 GB 64kmemory
Qwen3.6 27BQ4_K_M 6.7 tok/s estimated Haiku-class 19 GB 32kmemory
Muse Glimmer 30BQ4_K_M 7.2 tok/s estimated Haiku-class 18 GB 128k
Gemma 4 26B-A4BQ4_K_M 15 tok/s estimated Haiku-class 18 GB 128kmemory
GLM-4.7-FlashQ4_K_M 14 tok/s estimated Haiku-class 20 GB 32kmemory
Gemma 4 12BQ4_K_M 16 tok/s estimated Below every hosted tier 8.0 GB 256k
Qwen3.5 9BQ4_K_M 19 tok/s estimated Below every hosted tier 6.8 GB 256k
MiniCPM5 2BQ4_K_M 43 tok/s estimated Below every hosted tier 3.0 GB 128k
Qwen3.5 4BQ4_K_M 33 tok/s estimated Below every hosted tier 3.8 GB 256k
Ling 3.0 tinyQ4_K_M 50 tok/s estimated Below every hosted tier 5.0 GB 128k
Granite 4.2 8BQ4_K_M 12 tok/s estimated Below every hosted tier 11 GB 64kmemory
gpt-oss-20bMXFP4 18 tok/s estimated Below every hosted tier 13 GB 128k

Speed and memory are at 32k context, the setting the calculator starts on; the memory column is the weights plus the cache for that much of it. Each speed says how it was arrived at, and every one here is estimated from this machine's 170 GB/s of memory bandwidth rather than taken from a published benchmark. There is a page on how that sum works, and what each size needs, and one on what 32 GB runs, machine by machine. The longest context is the longest setting the calculator offers that this machine still holds the model at, cache included, and each one opens the calculator on that model at that length. A figure tagged memory is one this machine ran out of room for, and the rest are stopped by the model's own limit or by the end of the list.

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 50 tok/s.

10 more fit. Ranked below the twelve above: Gemma 4 E4B, LFM2.5 2.6B, Devstral Small 2 24B, Ministral 3 14B, Ministral 3 8B. The other 5 have no intelligence-index score, so the ranking cannot place them: LFM3 1.7B, Ornith 1.5 9B, Spark-X2.5 4B, Spark-X2.6 4B, LFM2.5 24B-A2B. Their pages show what each one needs and what runs it.

The 48 counted above are the current models. This site prices 16 superseded ones as well, because people still run them, and this machine holds seven of those at 32k, strongest first: Llama 3.1 8B Instruct at Q4_K_M, Llama 3.1 8B Instruct at Q8_0, Mistral Small 3.2 24B Instruct, Qwen3 14B, Qwen3 8B, Gemma 3 27B it, Gemma 3 12B it. Each of those pages asks whether the model is still worth running.

Four more, at a shorter window

The table above counts what fits at 32k, the context the calculator starts on. Four more models fit this machine at a shorter one. Same weights, smaller cache.

ModelLongest window it fitsNeeds thereNeeds at 32k
Qwen3-Coder 30B-A3BQ4_K_M 16k 20 GB 22 GB
Laguna XS 2.1Q4_K_M 8k 21 GB 22 GB
Granite 4.2 30BQ4_K_M 8k 20 GB 26 GB
Gemma 4 31B itQ4_K_M 4k 21 GB 23 GB

Each window is the longest setting the calculator offers that this machine still holds the model at, and it opens the calculator on that model at that length. The memory figures are the weights plus the key-value cache at that window, against the 21 GB this machine's GPU can use.

Other machines to weigh against the Mac mini M6, 32GB

MachinePriceMemoryModels that fitPay-back
The rest of the Mac mini range
Mac mini M6, 16GB $899 16 GB 12 Pays back in 71 years
Mac mini M6, 24GB $1,099 24 GB 16 Pays back in 85 years
Mac mini M5 Pro, 24GB $1,699 24 GB 16 Pays back in 135 years
Mac mini M5 Pro, 48GB $2,299 48 GB 32 Pays back in 155 years
Mac mini M5 Pro, 64GB $2,699 64 GB 32 Pays back in 182 years
Mac mini M4, 16GBdiscontinued $599 16 GB 12 Pays back in 50 years
Mac mini M4, 24GBdiscontinued $799 24 GB 16 Pays back in 66 years
Mac mini M4, 32GBdiscontinued $999 32 GB 22 Pays back in 73 years
Mac mini M4 Pro, 24GBdiscontinued $1,399 24 GB 16 Pays back in 114 years
Mac mini M4 Pro, 48GBdiscontinued $1,799 48 GB 32 Pays back in 128 years
Mac mini M4 Pro, 64GBdiscontinued $1,999 64 GB 32 Pays back in 142 years
Nearest in price elsewhere on the list
Strix Halo Framework Desktop, 32GB $1,269 32 GB 29 Pays back in 91 years
MacBook Air M5 (13-inch), 16GB $1,299 16 GB 12 Pays back in 102 years
AMD Radeon AI PRO R9700, 32GB $1,299card only 32 GB 32 Pays back in 84 years
MacBook Pro M5 (14-inch), 16GB $1,999 16 GB 12 Pays back in 157 years
NVIDIA GeForce RTX 5090, 32GB $1,999card only 32 GB 32 Pays back in 123 years

Every row uses the same defaults as the figures above: 500k tokens a day at 15:1 input to output, 32k context, and each machine's strongest model that fits, counted against the same 48 current models. Graphics cards are priced as the card alone, so add the PC around one before comparing it with a complete computer. All seven cards here are ranked by what each one holds.

Head to head with another computer: MacBook Air M5 (13-inch), 16GB · Framework Desktop, 32GB. With a graphics card: Radeon AI PRO R9700, 32GB. With the same machine at another memory size: 16GB · 24GB. With the machine it replaced: Mac mini M4, 32GB. There are 111 machine match-ups on the site.

The specifics

Chip
M6 — 12-core CPU / 12-core GPU
Memory bandwidth
170 GB/s
Usable by the GPU
21 GB — macOS lets the GPU wire roughly two-thirds of unified memory by default (llama.cpp discussion #2182: 66.7% at 32 GiB or less, 75% above). Raise it with `sudo sysctl iogpu.wired_limit_mb`. Memory is treated as GB throughout, which is slightly conservative.
Power under load
65 W (stand-in) — Apple has not published power figures for the 2026 machines yet (support articles 103253 / 102027 stop at the 2024/2025 models). Stand-in: Apple's published maximum for the previous chip (M4 Mac mini, 65 W), which is a CPU+GPU stress figure, so it likely overstates inference draw.
Availability
Pre-order; ships 2026-09-22.
Sources
Apple (specs), Apple (newsroom), Daring Fireball