The M3 Max MacBook Pro 14" runs local models at 300 to 400 GB/s of memory bandwidth with 36 to 128 GB of unified memory. On Apple Silicon that memory is shared with the GPU, so the whole pool is available for weights: at 128 GB you can hold roughly a 172B dense model at Q4. The Max is where the bus gets wide enough that model size, not bandwidth, becomes the thing you plan around.
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 56% of the Apple Silicon chips shipped in a Mac, against a 819 GB/s peak.
Its memory ceiling is above 67% of them, against a 512 GB peak.
Every option Apple sells with this chip. The model list below recomputes against the one you pick.
GPU cores
Unified memory
Apple couples memory to the core count on this chip, so the options change with the bin above.
Unified memory is the ceiling and it is soldered, so this is the decision you cannot revisit.
3,417 of 3,641 models fit, and 3,252 of them run with headroom rather than as a squeeze.
3,503 of 3,641 models fit, and 3,351 of them run with headroom rather than as a squeeze.
3,541 of 3,641 models fit, and 3,385 of them run with headroom rather than as a squeeze.
3,556 of 3,641 models fit, and 3,503 of them run with headroom rather than as a squeeze.
3,582 of 3,641 models fit, and 3,532 of them run with headroom rather than as a squeeze.
Every model in the database against this exact configuration, at 300 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
Multimodal · Alibaba · 2026-02-28
Multimodal · Alibaba · 2026-02-28
Multimodal · Alibaba · 2026-02-27
Multimodal · Alibaba · 2026-02-27
General · Liquid AI · 2025-11-28
General · 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
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
Reasoning · Liquid AI · 2025-11-28
General · ibm-granite · 2025-09-16
Chat · Liquid AI · 2025-11-28
Chat · Liquid AI · 2025-11-28
Chat · Liquid AI · 2025-11-28
General · NCAI · 2025-12-29
Reasoning · HuggingFace · 2025-07-08
General · ibm-granite · 2025-09-16
General · Liquid AI · 2025-11-28
General · Liquid AI · 2025-11-28
Multimodal · NCAI · 2025-12-29
Multimodal · Google · 2025-07-30
Multimodal · Google · 2025-06-25
Multimodal · Liquid AI · 2025-11-28
Multimodal · Liquid AI · 2025-11-28
Multimodal · Liquid AI · 2025-11-28
Multimodal · Liquid AI · 2025-11-28
Reasoning · jackrong · 2026-03-16
General · lgai-exaone · 2025-03-12
General · LG AI · 2025-07-15
General · raidium · 2026-06-15
General · NCAI · 2025-12-29
Embedding · taide · 2026-06-12
General · Alibaba · 2025-04-27
General · Alibaba · 2025-04-27
Multimodal · zai-org
Multimodal · Alibaba
General · Alibaba
General · Alibaba
Multimodal · openbmb
Reasoning · DeepSeek
Multimodal · rednote-hilab
General · DeepSeek
Multimodal · datalab-to
Multimodal · moonshotai
General · openbmb
Multimodal · rednote-hilab
General · hmellor
General · distil-labs
General · farbodtavakkoli
General · Liquid AI
General · ibm-granite
General · Google
Multimodal · moonshotai
Multimodal · lkhl
General · jinaai
General · Alibaba
Reasoning · typhoon-ai
General · Upstage
General · openai
General · paddlepaddle
General · Liquid AI
General · pfnet
General · openbmb
General · baidu
General · jetbrains
General · amd
General · bytedance-seed
General · arcee-ai
General · adamlucek
Coding · shahriarferdoush
General · ahczhg
General · abaryan
General · etherll
General · jetbrains
General · farbodtavakkoli
Reasoning · khazarai
General · kamilamila
General · getonit
General · paddlepaddle
General · moonshotai
General · jangq-ai
General · agentica-org
General · novaciano
General · dmusingu
General · kgrabko
General · ordenwills
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General · carsenk
General · dealignai
Reasoning · nvidia
General · zero-point-ai
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General · openbmb
General · ibm-granite
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General · ibm-granite
General · treadon
General · tencent
Coding · DeepSeek
General · skis-ai-research
General · menlo
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General · magistrtheone
Reasoning · jackrong
General · huihui-ai
Coding · rahul7star
General · inclusionai
General · TII
Coding · z-lab
General · osaurusai
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General · Microsoft
General · roystar
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General · Microsoft
Reasoning · ai21labs
General · TII
General · tencent
General · primeintellect
Multimodal · Alibaba · 2026-02-27
Multimodal · Alibaba
Multimodal · google · 2026-05
Multimodal · Microsoft
Coding · DeepSeek · 2024-06-14
General · ibm-granite
Multimodal · nanonets
Multimodal · Alibaba · 2026-02-26
Multimodal · ibm-granite
Multimodal · typhoon-ai
General · internlm
Multimodal · goekdeniz-guelmez
General · kristaller486
General · ibm-granite
General · weiboai
General · x-izhang
Apple ties the memory bus to the bin on this chip: 300 GB/s at 30 cores and 400 GB/s at 40. That moves tokens per second. It does not move which models fit, because that is memory, not cores.
| Configuration | Memory bandwidth | Memory options | Models that fit |
|---|---|---|---|
| 14-core CPU, 30-core GPU | 300 GB/s | 36, 96 GB | Identical |
| 16-core CPU, 40-core GPU | 400 GB/s | 48, 64, 128 GB | Identical |
On openbuddy zero 56b v21.2 32k the 30-core generates about 8 tok/s and the 40-core about 10 tok/s, a 33% difference. Both hold the model at the same quantization.
Nobody has submitted a benchmark on the M3 Max 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 M3 Max's 400 GB/s bus is shared by CPU, GPU, and Neural Engine, so a 128 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 14-inch chassis cools well enough to hold its clocks through a long generation run, and it is the smallest machine Apple puts a Max chip in. A used M3 Max at 128 GB still gives you 400 GB/s and a hard 172B ceiling, and neither number degrades with age the way a battery does.
Yes, at 128 GB. A 70B model at Q4_K_M needs about 46 GB including an 8K context, and 128 GB of unified memory leaves about 112 GB for weights once macOS takes its share. At 36 GB it does not fit at any quantization worth running.
Memory is the only spec that changes what you can run at all. 36 GB holds about a 47B model at Q4; 128 GB holds about 172B. 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 M3 Max throughput scales with its 400 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 M3 Max lands in the tens of tokens per second and a 70B model lands in the single digits.
For throughput, yes: Apple ties bandwidth to the bin here, so the 30-core runs at 300 GB/s and the 40-core at 400 GB/s, about 33% more. For fit, no: both bins hold exactly the same models, because that is set by memory rather than by cores.
For inference, the specs that matter do not age: 400 GB/s and up to 128 GB of unified memory are the same numbers today as they were in 2023. A used M3 Max 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.