The M6 Mac mini runs local models at 153 to 170 GB/s of memory bandwidth with 16 to 32 GB of unified memory. On Apple Silicon that memory is shared with the GPU, so the whole pool is available for weights: at 32 GB you can hold roughly a 42B dense model at Q4. A base M chip is the narrow end of the memory bus. It runs small models pleasantly and stops hard at the memory ceiling, which is the constraint you will hit first.
Memory bandwidth is faster than 30% of the Apple Silicon chips shipped in a Mac, against a 1228 GB/s peak.
Its memory ceiling is above 15% of them, against a 512 GB peak.
Every option Apple sells with this chip. The model list below recomputes against the one you pick.
Unified memory
Unified memory is the ceiling and it is soldered, so this is the decision you cannot revisit.
5,572 of 6,563 models fit, and 5,054 of them run with headroom rather than as a squeeze.
6,032 of 6,563 models fit, and 5,271 of them run with headroom rather than as a squeeze.
6,082 of 6,563 models fit, and 5,571 of them run with headroom rather than as a squeeze.
Every model in the database against this exact configuration, at 153 GB/s. Ratings and speeds are the same numbers the model pages show.
Showing 6563 of 6563 models
General · openbmb · 2026-05-21
General · Liquid AI · 2026-06-24
General · ktruestory · 2026-05-28
General · petrouil · 2026-07-23
General · Liquid AI · 2026-03-31
Multimodal · Alibaba · 2026-02-28
General · radixark · 2026-07-27
Multimodal · Alibaba · 2026-02-28
General · goekdeniz-guelmez · 2026-07-31
General · cagrigungor · 2026-08-06
Multimodal · Alibaba · 2026-02-28
Multimodal · Alibaba · 2026-02-28
General · Liquid AI · 2026-05-28
General · Liquid AI · 2026-07-28
Multimodal · Liquid AI · 2026-01-05
General · Liquid AI · 2026-01-20
Reasoning · openonerec · 2026-06-09
General · Liquid AI · 2026-01-05
General · Liquid AI · 2026-01-04
Chat · Liquid AI · 2026-01-06
General · Liquid AI · 2025-10-28
General · frontiersmind · 2026-08-03
General · farbodtavakkoli · 2026-06-17
General · hmellor · 2025-07-22
General · ibm-granite · 2025-09-16
Multimodal · Liquid AI · 2025-08-12
General · Liquid AI · 2025-09-30
General · Liquid AI · 2025-08-22
General · Liquid AI · 2025-09-03
General · Liquid AI · 2025-09-03
General · Liquid AI · 2025-08-25
General · Liquid AI · 2025-09-03
General · Liquid AI · 2025-09-03
General · Liquid AI · 2026-01-05
General · weiboai · 2026-06-12
General · ibm-granite · 2025-04-30
General · Liquid AI · 2025-07-10
General · stefanruseti · 2025-06-04
General · ma7ee7 · 2026-07-30
General · bytedance · 2025-10-28
General · ibm-granite · 2025-10-07
Multimodal · Liquid AI · 2025-08-12
General · lgai-exaone · 2025-07-11
General · Liquid AI · 2025-07-10
General · Liquid AI · 2025-07-10
General · Liquid AI · 2025-12-25
General · NCAI · 2025-12-29
Multimodal · ibm-granite · 2026-04-16
General · Alibaba · 2025-09-23
General · openonerec · 2025-12-30
General · amd · 2025-05-17
General · pfnet · 2025-02-05
General · sapientinc · 2026-05-17
Multimodal · Liquid AI · 2025-10-22
General · Liquid AI · 2025-09-22
General · viorikaai-org · 2026-07-05
General · bananamind · 2026-07-17
General · maliosdark · 2026-07-09
General · raidium · 2026-06-15
Multimodal · NCAI · 2025-12-29
Embedding · taide · 2026-06-12
General · Alibaba · 2025-04-27
Multimodal · zai-org
Multimodal · datalab-to
Multimodal · openbmb
General · Alibaba · 2025-04-28
Reasoning · DeepSeek · 2025-01-20
Multimodal · tencent
General · distil-labs
General · farbodtavakkoli
Multimodal · ath-maas
Multimodal · Liquid AI
Multimodal · paddlepaddle
General · Liquid AI
Chat · uzlm · 2025-09-03
General · Liquid AI · 2025-10-07
Multimodal · paddlepaddle
General · ai21labs · 2026-01-06
General · adamlucek
Coding · shahriarferdoush
General · ahczhg
General · onnx-community · 2025-04-28
General · etherll
General · baidu
General · Liquid AI
General · openbmb
General · benjamin
General · lemonelabs
General · openbmb · 2025-06-05
General · farbodtavakkoli
General · novachronoai
Multimodal · paddlepaddle
General · kamilamila
General · arcee-ai
General · Liquid AI
General · saidutta69
General · ordenwills
General · smcleish
General · carsenk
Reasoning · nvidia
General · openbmb
General · ibm-granite
General · pyoakum
General · ibm-granite
General · treadon
General · skis-ai-research
General · thkim0305
Reasoning · jackrong
Coding · rahul7star
General · fableforge-ai · 2026-07-05
General · osaurusai
General · artificialguybr
General · Microsoft
General · Microsoft
General · tencent
General · primeintellect
General · reaperdoesntknow
General · darthcrawl · 2026-05-07
General · appvoid
General · pinkstack
Coding · Liquid AI
General · squ11z1
General · iselabvn
General · meddies
General · mihaipopa-1
General · lazos
General · q1ngmang
General · baidu
Multimodal · Liquid AI
General · TII
General · melikegks
Coding · dalatexcoder
Reasoning · supralabs
General · iselabvn
Multimodal · 8f-ai
General · kylesayrs
General · inference-optimization
General · cooperdk
Multimodal · dingdust
Multimodal · yuandaxia
General · dingdust
General · ermiaazarkhalili
General · thepradip
General · marinarosa
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General · ephemeralyou
General · marinarosa
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General · ewinregirgojr
Reasoning · healshsj
Nobody has submitted a benchmark on the M6 yet, so every speed on this page is the formula estimate rather than a measured run. The estimate is bandwidth-driven and calibrated against chips that do have data, which makes it a good guide and not a promise.
ToolPiper contributes a result anonymously when you run the benchmark, and the leaderboard shows every chip that already has one.
What each step actually changes for local models, rather than which one is newer.
On a PC the model has to fit in GPU VRAM, which is a separate pool from system RAM and usually the smaller of the two. Apple Silicon has one pool. The M6's 170 GB/s bus is shared by CPU, GPU, and Neural Engine, so a 32 GB machine can hand almost all of that to a model with no copy across a bus.
The mini is the cheapest way onto this chip, and it runs headless on a shelf perfectly well. Nothing about local inference needs the display attached. Buy the memory, not the cores: every extra GB raises what you can load, while the core count only moves throughput on models that already fit.
No. A 70B model at Q4_K_M needs about 46 GB, and the largest M6 Mac mini tops out at 32 GB, which leaves about 28 GB for weights. The practical ceiling on this machine is around 42B parameters at Q4.
Memory is the only spec that changes what you can run at all. 16 GB holds about a 20B model at Q4; 32 GB holds about 42B. It is soldered, so this is a one-time decision, and it is the upgrade worth paying for before core count.
Token generation is bandwidth-bound, so M6 throughput scales with its 170 GB/s memory bus. Divide bandwidth by the size of the weights actually read per token to get the ceiling, then expect roughly half of that in practice. A 7B model at Q4 reads about 4 GB per token pass, so the M6 lands in the tens of tokens per second and a 70B model lands in the single digits.
Buy on the memory you need today. Apple raises memory ceilings slowly and bandwidth in steps, and the M6 already holds about a 42B model at Q4. If your target model fits in 32 GB, waiting buys throughput rather than capability.
ToolPiper downloads, manages, and runs local models on Apple Silicon. Free, and nothing leaves the machine.