The M1 Mac mini runs local models at 68.25 GB/s of memory bandwidth with 8 to 16 GB of unified memory. On Apple Silicon that memory is shared with the GPU, so the whole pool is available for weights: at 16 GB you can hold roughly a 20B 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.
Apple no longer sells this configuration new. It stays fully evaluated here because the used market is where most of its local AI value now sits.
Memory bandwidth is faster than 0% of the Apple Silicon chips shipped in a Mac, against a 819 GB/s peak.
Its memory ceiling is above 0% 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.
2,863 of 3,641 models fit, and 1,869 of them run with headroom rather than as a squeeze.
3,128 of 3,641 models fit, and 2,907 of them run with headroom rather than as a squeeze.
Every model in the database against this exact configuration, at 68.25 GB/s. Ratings and speeds are the same numbers the model pages show.
Showing 3641 of 3641 models
Multimodal · Alibaba · 2026-02-28
Multimodal · Alibaba · 2026-02-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
Reasoning · Liquid AI · 2025-11-28
Reasoning · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
Multimodal · Liquid AI · 2025-11-28
Chat · Liquid AI · 2025-11-28
Chat · Liquid AI · 2025-11-28
General · raidium · 2026-06-15
Embedding · taide · 2026-06-12
General · Alibaba
Multimodal · datalab-to
General · distil-labs
General · Google
General · openai
General · Liquid AI
General · baidu
General · farbodtavakkoli
General · kamilamila
General · ordenwills
General · ibm-granite
General · Microsoft
General · Microsoft
General · tencent
General · primeintellect
General · Alibaba · 2025-04-27
Multimodal · Alibaba · 2026-02-28
Multimodal · Alibaba · 2026-02-28
General · etherll
Reasoning · jackrong
Coding · rahul7star
Coding · z-lab
General · Alibaba
General · Alibaba
General · Microsoft
General · openbmb
General · voyageai
General · Microsoft
General · farbodtavakkoli
General · farbodtavakkoli
General · jakobhuss
General · llava-hf
General · paddlepaddle
General · openbmb
Coding · shahriarferdoush
General · openbmb
General · LG AI · 2025-07-15
General · ibm-granite
General · numind
General · toxicityprompts
General · ibm-granite
General · ysmao
General · paddlepaddle
General · lmms-lab
General · tabularisai
General · numind
General · isotonic
General · manycore-research
General · bezzam
General · ibm-granite
General · huihui-ai
General · skis-ai-research
General · thkim0305
General · lunahr
General · ibm-granite
General · lunahr
Multimodal · zai-org
Multimodal · openbmb
General · Google
Multimodal · opengvlab
General · hmellor
General · farbodtavakkoli
General · Alibaba
Multimodal · stepfun-ai
General · TII
General · Google
Multimodal · Liquid AI · 2025-11-28
Coding · Alibaba
General · adamlucek
General · ahczhg
General · abaryan
General · mrs83
Coding · codefuse-ai
Coding · DeepSeek
Coding · codefuse-ai
Multimodal · Liquid AI · 2025-11-28
General · carsenk
Coding · candan77
Coding · tommymir4444
Reasoning · nvidia
Coding · holten51
General · dllm-hub
General · amd
Coding · baebii
Coding · bedeviler
Reasoning · kkhya
Reasoning · kkhya
Reasoning · kkhya
General · artificialguybr
General · dllm-hub
General · huggingfacetb
Reasoning · HuggingFace · 2025-07-08
Reasoning · DeepSeek
Multimodal · opendatalab
General · ibm-granite
Multimodal · tencent
General · iamjb
General · nlpai-lab
General · farbodtavakkoli
Embedding · cnmoro
General · pfnet
General · freakingpotato
General · simonlee711
General · turing-motors
General · qnguyen3
General · lsx-uniwue
General · jdchang
General · opengvlab
General · colinglab
General · thisisiron
General · nvidia
General · mlnomad
General · mlnomad
General · mlnomad
General · mlnomad
General · openbmb
Chat · alignmentresearch
General · mlnomad
General · treadon
General · huggingfacetb
Chat · TII
General · ba2han
General · pjmixers-dev
Nobody has submitted a benchmark on the M1 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 M1's 68.25 GB/s bus is shared by CPU, GPU, and Neural Engine, so a 16 GB machine can hand almost all of that to a model with no copy across a bus.
Apple stopped selling this one, which is exactly why it is interesting. 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. A used M1 at 16 GB still gives you 68.25 GB/s and a hard 20B ceiling, and neither number degrades with age the way a battery does.
No. A 70B model at Q4_K_M needs about 46 GB, and the largest M1 Mac mini tops out at 16 GB, which leaves about 14 GB for weights. The practical ceiling on this machine is around 20B parameters at Q4.
Memory is the only spec that changes what you can run at all. 8 GB holds about a 9B model at Q4; 16 GB holds about 20B. 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 M1 throughput scales with its 68.25 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 M1 lands in the tens of tokens per second and a 70B model lands in the single digits.
For inference, the specs that matter do not age: 68.25 GB/s and up to 16 GB of unified memory are the same numbers today as they were in 2020. A used M1 at the top memory option usually beats a new base-tier machine at the same price on both. Check the battery and the display, not the silicon.
ToolPiper downloads, manages, and runs local models on Apple Silicon. Free, and nothing leaves the machine.