The M5 Pro MacBook Pro 16" runs local models at 307 GB/s of memory bandwidth with 24 to 64 GB of unified memory. On Apple Silicon that memory is shared with the GPU, so the whole pool is available for weights: at 64 GB you can hold roughly a 85B dense model at Q4. The Pro roughly doubles the base chip's memory bus. That moves mid-size models from usable to comfortable without moving you into desktop money.
Memory bandwidth is faster than 50% of the Apple Silicon chips shipped in a Mac, against a 819 GB/s peak.
Its memory ceiling is above 44% 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.
3,352 of 3,641 models fit, and 3,007 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.
Every model in the database against this exact configuration, at 307 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
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
Multimodal · datalab-to
General · openbmb
Multimodal · rednote-hilab
General · hmellor
General · distil-labs
General · farbodtavakkoli
General · Liquid AI
General · ibm-granite
General · Google
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 · amd
General · bytedance-seed
General · arcee-ai
General · adamlucek
Coding · shahriarferdoush
General · ahczhg
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General · etherll
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Reasoning · khazarai
General · kamilamila
General · getonit
General · paddlepaddle
General · agentica-org
General · novaciano
General · dmusingu
General · kgrabko
General · ordenwills
General · smcleish
General · carsenk
Reasoning · nvidia
General · zero-point-ai
General · ibm-granite
General · openbmb
General · ibm-granite
General · osaurusai
General · pyoakum
General · ibm-granite
General · treadon
General · tencent
General · skis-ai-research
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Reasoning · jackrong
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Coding · rahul7star
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Coding · z-lab
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General · Microsoft
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Reasoning · ai21labs
General · TII
General · tencent
General · primeintellect
General · Alibaba · 2025-04-27
Multimodal · Alibaba · 2026-02-27
Multimodal · Alibaba
Multimodal · datalab-to
Multimodal · Microsoft
General · ibm-granite
Multimodal · nanonets
Multimodal · Alibaba · 2026-02-26
General · nanonets
Multimodal · ibm-granite
Multimodal · typhoon-ai
General · internlm
Multimodal · goekdeniz-guelmez
General · kristaller486
General · zstanjj
General · ibm-granite
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General · bytedance
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General · radheneev
Nobody has submitted a benchmark on the M5 Pro 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 M5 Pro's 307 GB/s bus is shared by CPU, GPU, and Neural Engine, so a 64 GB machine can hand almost all of that to a model with no copy across a bus.
The 16-inch chassis has the most thermal headroom Apple ships in a laptop, so sustained token throughput stays close to the burst figure. 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.
Yes, at 64 GB. A 70B model at Q4_K_M needs about 46 GB including an 8K context, and 64 GB of unified memory leaves about 56 GB for weights once macOS takes its share. At 24 GB it does not fit at any quantization worth running.
Memory is the only spec that changes what you can run at all. 24 GB holds about a 31B model at Q4; 64 GB holds about 85B. 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 M5 Pro throughput scales with its 307 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 M5 Pro 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 M5 Pro already holds about a 85B model at Q4. If your target model fits in 64 GB, waiting buys throughput rather than capability.
ToolPiper downloads, manages, and runs local models on Apple Silicon. Free, and nothing leaves the machine.