Apple · apple

Apple M5 Pro

Apple M5 Pro has 48 GB of unified memory at 307 GB/s — about 33.48 GiB usable after driver and compositor overhead. 2030 of 2118 indexed models fit at 8K context with f16 KV. Note only 36 GB of its 48 GB is allocatable to the GPU.

Spec sheet· bandwidth, theoreticalFrom the file· fit from summed bytesPredicted· speed
Memory
48 GB
LPDDR5X-9600
Bandwidth
307 GB/s
256-bit bus
Tensor FP16
—
dense
TDP
—
Context4K8K16K32K64K128K
KV cachef16q8_0q4_0quantizing the KV cache is a ~2× lever on the dominant term at long context
text 1743vision language 183video 16audio tts 21image 2embedding 26audio asr 39

What fits at 8K context

largest quantization that fits, per model · 2030 of 2118 indexed
ModelBest quantParamsWeights●KV●Total in memory◐Headroom◐tok/s◐
Mistral-Medium-3.5-128BUD-IQ2_XXS128B32.54 GiB2.75 GiB36.00 GiB0.00 GiB7±8.3%
Salience-1.5-ProMoEQ8_036.0B35.22 GiB0.16 GiB35.93 GiB0.07 GiB36±37%
Qwable-v1MoEQ8_036.0B35.22 GiB0.16 GiB35.93 GiB0.07 GiB36±37%
T-SearchMoEQ8_036.0B35.22 GiB0.16 GiB35.93 GiB0.07 GiB36±37%
Qwen3.5-35B-A3BMoEQ8_036.0B35.22 GiB0.16 GiB35.93 GiB0.07 GiB36±37%
Qwen3.6-35B-A3B-uncensored-heretic-Native-MTP-PreservedMoEQ8_035.1B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Qwen3.6-35B-A3B-Fable-5-DistillMoEQ8_036.0B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Qwable-v2MoEQ8_036.0B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Qwen3.6-35B-A3B-YOYO-V2MoEQ8_036.0B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Ornith-1.0-35B-FP8-BLOCK-MTPMoEQ8_035.5B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
fable-coder-35B-A3BMoEQ8_036.0B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
PINQWEN-3.6-35B-CLEAN-BF16MoEQ8_036.0B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
UniMath-35B-A3BMoEQ8_036.0B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Ornith-1.0-35B-Heretic-MTPMoEQ8_0—35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Fawen-1.0-35BMoEQ8_036.0B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Qwopus3.6-35B-A3B-v1MoEQ8_036.0B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
CyberStrike-OffSec-35BMoEQ8_035.1B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Qwen3.6-35B-A3BMoEQ8_036.0B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Qwen3.6-35B-A3B-Claude-4.7-Opus-Reasoning-DistilledMoEQ8_036.0B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Qwen3.5-35B-A3B-uncensored-heretic-v2-Native-MTP-PreservedMoEQ8_035.1B35.21 GiB0.16 GiB35.92 GiB0.08 GiB36±37%
Qwen3.6-27B-uncensored-heretic-v2-Native-MTP-PreservedQ8_027.4B34.80 GiB0.50 GiB35.91 GiB0.09 GiB7±8.3%
Qwen3-53B-A3B-2507-THINKING-TOTAL-RECALL-v2-MASTER-CODERMoEI1-Q5_K_S53.0B34.03 GiB1.31 GiB35.88 GiB0.12 GiB22±37%
GLM-4.5-Air-REAP-82B-A12BMoEQ2_K_L81.9B33.84 GiB1.44 GiB35.86 GiB0.14 GiB21±37%
llama-3.2-3b-instructF163.2B34.37 GiB0.88 GiB35.81 GiB0.19 GiB7±8.3%
Laguna-S-2.1MoEUD-IQ2_M118B34.71 GiB0.52 GiB35.80 GiB0.20 GiB31±37%
Mistral-Small-4-119B-2603MoEUD-IQ2_M119B34.99 GiB0.18 GiB35.75 GiB0.25 GiB36±37%
deepseek-coder-33b-instructQ8_033.3B33.00 GiB1.94 GiB35.56 GiB0.44 GiB7±8.3%
deepseek-coder-33b-baseQ8_033.3B33.00 GiB1.94 GiB35.56 GiB0.44 GiB7±8.3%
WhiteRabbitNeo-33B-v1Q8_033.3B33.00 GiB1.94 GiB35.56 GiB0.44 GiB7±8.3%
HarmonicHarlequin_v5-20BI1-Q4_K_M33.3B18.71 GiB16.25 GiB35.55 GiB0.45 GiB7±8.3%
WizardCoder-Python-34B-V1.0Q8_033.7B33.39 GiB1.50 GiB35.54 GiB0.46 GiB7±8.3%
Phind-CodeLlama-34B-v2Q8_033.7B33.39 GiB1.50 GiB35.54 GiB0.46 GiB7±8.3%
CodeLlama-34b-instruct-hfQ8_033.7B33.39 GiB1.50 GiB35.54 GiB0.46 GiB7±8.3%
WizardLM-1.0-Uncensored-CodeLlama-34bQ8_033.7B33.39 GiB1.50 GiB35.54 GiB0.46 GiB7±8.3%
Phind-CodeLlama-34B-Python-v1Q8_033.7B33.39 GiB1.50 GiB35.54 GiB0.46 GiB7±8.3%
Melody1437-27BQ4_K_M27.8B34.41 GiB0.50 GiB35.52 GiB0.48 GiB7±8.3%
llm-surgery-dark-arts-gpt-oss-60b-96a12MoEI1-Q4_160.9B34.76 GiB0.21 GiB35.51 GiB0.49 GiB21±37%
Devstral-2-123B-Instruct-2512UD-IQ2_XXS125B32.04 GiB2.75 GiB35.50 GiB0.50 GiB7±8.3%
GLM-4.6VMoEIQ1_M108B33.46 GiB1.44 GiB35.47 GiB0.53 GiB24±37%
Llama-3_3-Nemotron-Super-49B-v1_5IQ2_S49.9B14.76 GiB20.00 GiB35.45 GiB0.55 GiB7±8.3%
Valkyrie-49B-v2.1I1-IQ2_S49.9B14.76 GiB20.00 GiB35.45 GiB0.55 GiB7±8.3%
Llama-3_3-Nemotron-Super-49B-v1IQ2_S49.9B14.76 GiB20.00 GiB35.45 GiB0.55 GiB7±8.3%
Qwen3-Coder-NextMoEIQ3_M79.7B34.13 GiB0.75 GiB35.42 GiB0.58 GiB35±37%
Qwen3-Next-80B-A3B-ThinkingMoEIQ3_M81.3B34.13 GiB0.75 GiB35.42 GiB0.58 GiB35±37%
Qwen3-Next-80B-A3B-InstructMoEIQ3_M81.3B34.13 GiB0.75 GiB35.42 GiB0.58 GiB35±37%
GLM-Z1-Rumination-32B-0414Q8_033.1B32.81 GiB1.91 GiB35.35 GiB0.65 GiB7±8.3%
Rombo-LLM-V3.0-Qwen-72bI1-IQ3_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
Qwen2.5-72B-Instruct-abliteratedI1-IQ3_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
Qwen2.5-72B-Instruct-abliterated-v2I1-IQ3_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
HuatuoGPT-o1-72BQ3_K_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
MiroThinker-v1.0-72BI1-IQ3_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
EVA-Qwen2.5-72B-v0.2Q3_K_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
Qwen2.5-Math-72B-InstructQ3_K_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
Qwen2.5-72B-InstructQ3_K_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
Malaysian-Qwen2.5-72B-InstructI1-IQ3_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
Qwen2.5-72BI1-IQ3_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
magnum-v4-72bI1-IQ3_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
Kimi-Dev-72BQ3_K_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
KAT-Dev-72B-ExpQ3_K_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
Homer-v1.0-Qwen2.5-72BQ3_K_S72.7B32.12 GiB2.50 GiB35.30 GiB0.70 GiB7±8.3%
●From the file◐Predictedwhat these mean

Speed is modeled, not measured: decode is memory-bandwidth bound, so tokens per second is bytes read per token against achievable bandwidth. Mixture-of-experts models carry a wider band because only the routed experts are read each step, and few have been measured publicly.

Questions people ask

What AI models can a Apple M5 Pro run?
2030 of 2118 indexed open-weight models fit a Apple M5 Pro at 8,192 context with f16 KV cache, the largest being Mistral-Medium-3.5-128B at UD-IQ2_XXS. That covers text, vision-language, image, video and speech models.
How much usable memory does a Apple M5 Pro actually have?
Its nameplate is 48 GB, but about 33.48 GiB is available to a model once driver and compositor overhead is accounted for, and only 36 GB of the pool can be allocated to the GPU at all.
Is a Apple M5 Pro fast for local AI?
Its memory bandwidth is 307 GB/s, and that figure — not teraflops — is what governs token generation speed. Capacity decides what you can run; bandwidth decides how fast it runs.