Apple M1 Pro

Apple M1 Pro (32GB): the double-memory configuration of Apple's first Pro-tier Apple Silicon chip, launched October 2021 as a $400 build-to-order upgrade over the 16GB base on either the $1,999 14-inch or $2,499 16-inch MacBook Pro. Same 200 GB/s LPDDR5 bandwidth and CPU options (8-core or 10-core) as the 16GB sibling — only the memory pool triples the sibling's usable headroom.

With the standard 8 GB OS reservation, this configuration has 24 GB of real headroom — three times the 16GB sibling's 8 GB, which changes what's realistic to run rather than just how comfortably it fits. This site's calculator lets Qwen3 32B run natively at Q4_K_M (23.9 GB, ~7.5 tok/s) and Mixtral 8x7B fit at Q2_K (~9.2 tok/s), both firmly out of reach on the 16GB config. Mid-size models get headroom too: Llama 3.1 8B and Qwen2.5 7B now fit at full BF16 precision instead of being squeezed into Q5/Q6, at roughly 9-10 tok/s. 46 of the 84 tracked models fit natively, up from 26 on the 16GB sibling — the extra 16 GB unlocks a full tier of 32-35B models, not just more headroom on the same 7-14B models.

Full MLX and llama.cpp Metal support, same as the 16GB sibling. Because the OS-and-background reservation this site models — and macOS's own Metal working-set default — stays roughly fixed regardless of installed memory, it eats a much smaller fraction of this 32 GB pool than the 16 GB config's, leaving comfortable room to raise the Metal working-set ceiling further if needed.

Apple M1 Pro (16GB): launched October 2021 as the base memory tier of Apple's first Pro-tier Apple Silicon chip — standard on the $1,999 14-inch MacBook Pro (8-core CPU, 6P+2E) and, at $2,499, the 16-inch base model too (10-core CPU, 8P+2E). Both CPU binnings share identical 200 GB/s LPDDR5 bandwidth; Apple never cut memory speed to hit a cheaper CPU tier on this chip, unlike the 96GB M3 Max a few generations later. The 32GB configuration tracked separately on this site (m1-pro-32) is the same chip family with double the unified memory pool — bandwidth doesn't change between them.

This site's calculator reserves 8 GB of the 16 GB pool for macOS and background apps — the same baseline used for every unified-memory GPU here — leaving 8 GB of real headroom for a model's weights, KV cache, and overhead. That's half the nameplate capacity, and a third of the 24 GB the 32GB sibling gets from the identical reservation. In practice that caps sensible model choices around 7-9B parameters at real quality: Llama 3.1 8B fits at Q5_K_M (7.58 GB, ~23.6 tok/s) and Qwen2.5 7B fits at Q6_K (7.51 GB, ~23.8 tok/s), each with only a few hundred MB of headroom left over. Reach for 12-14B models and the pool only has room at the bottom of the quant ladder — Qwen3 14B needs Q2_K (7.82 GB) and Gemma 3 12B needs Q3_K_M (7.76 GB), both well below the precision those models are capable of at higher bit depths. Context adds up fast against that same 8 GB ceiling too: Llama 3.1 8B still fits at 16K context (7.86 GB at Q4_K_M) but drops out entirely by 32K as its KV cache grows into the same shrunken pool. The one model that fits above 15B parameters at all is Bonsai 27B's proprietary 1-bit build (6.06 GB) — an extreme fixed-format requantization, not a sign this configuration handles 27B-class quality on the standard quant ladder.

macOS's default Metal working-set limit (recommendedMaxWorkingSetSize, the figure llama.cpp and MLX both read on startup) is roughly 75% of unified memory — about 12 GB on this configuration. Community guides for raising it further with sudo sysctl iogpu.wired_limit_mb=... generally recommend leaving 8-16 GB in reserve for macOS itself; on a 16 GB machine that reserve is most or all of the pool, so there's little safe room to push past what this page already assumes. That constraint eases quickly further up the Apple Silicon range, where the same 8-16 GB reserve is a small fraction of the pool rather than most of it.