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Apple M3 Ultra

Apple M3 Ultra has 512 GB of unified memory at 819 GB/s — about 357.12 GiB usable after driver and compositor overhead. 2116 of 2118 indexed models fit at 64K context with q8_0 KV. Note only 384 GB of its 512 GB is allocatable to the GPU.

Spec sheet· bandwidth, theoreticalFrom the file· fit from summed bytesPredicted· speed
Memory
512 GB
LPDDR5-6400
Bandwidth
819 GB/s
1024-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
vision language 195text 1817image 2audio tts 21audio asr 39video 16embedding 26

What fits at 64K context

largest quantization that fits, per model · 2116 of 2118 indexed
ModelBest quantParamsWeights●KV●Total in memory◐Headroom◐tok/s◐
Step-3.7-FlashBF16201B366.96 GiB13.30 GiB380.83 GiB3.17 GiB2±8.3%
DeepSeek-V3.2MoEQ4_K_M685B377.56 GiB2.28 GiB380.46 GiB3.54 GiB10±37%
cogito-671b-v2.1MoEQ4_K_M671B377.55 GiB2.28 GiB380.46 GiB3.54 GiB10±37%
Kimi-K2.5MoEIQ3_S1059B377.51 GiB2.28 GiB380.42 GiB3.58 GiB12±37%
DeepSeek-TNG-R1T2-ChimeraMoEQ4_K_M685B377.13 GiB2.28 GiB380.04 GiB3.96 GiB10±37%
cogito-v2-preview-deepseek-671B-MoEMoEQ4_K_M671B377.13 GiB2.28 GiB380.03 GiB3.97 GiB10±37%
DeepSeek-V3-0324MoEQ4_K_M685B376.89 GiB2.28 GiB379.80 GiB4.20 GiB10±37%
DeepSeek-Prover-V2-671BMoEQ4_K_M685B376.71 GiB2.28 GiB379.62 GiB4.38 GiB11±37%
r1-1776MoEQ4_K_M671B376.65 GiB2.28 GiB379.56 GiB4.44 GiB11±37%
DeepSeek-R1MoEQ4_K_M685B376.65 GiB2.28 GiB379.56 GiB4.44 GiB11±37%
GLM-5.1MoEIQ4_XS754B375.69 GiB2.91 GiB379.20 GiB4.80 GiB10±37%
GLM-5MoEIQ4_XS754B375.23 GiB2.91 GiB378.74 GiB5.26 GiB10±37%
Qwen3-Coder-480B-A35B-InstructMoEQ6_K480B367.10 GiB8.23 GiB375.92 GiB8.08 GiB8±37%
Kimi-K2-ThinkingMoEIQ3_XXS1058B367.09 GiB2.28 GiB370.00 GiB14.00 GiB12±37%
DeepSeek-R1-0528MoEQ4_K_S685B367.08 GiB2.28 GiB369.99 GiB14.01 GiB11±37%
DeepSeek-V3.1-TerminusMoEQ4_K_S685B367.08 GiB2.28 GiB369.99 GiB14.01 GiB11±37%
DeepSeek-V3.1MoEQ4_K_S685B367.08 GiB2.28 GiB369.99 GiB14.01 GiB11±37%
Qwen3-Coder-REAP-363B-A35BMoEQ8_0363B359.45 GiB8.23 GiB368.27 GiB15.73 GiB7±37%
GLM-4.5MoEQ8_0358B354.80 GiB12.22 GiB367.61 GiB16.39 GiB8±37%
GLM-4.7MoEQ8_0358B354.80 GiB12.22 GiB367.61 GiB16.39 GiB8±37%
GLM-4.6MoEQ8_0357B353.27 GiB12.22 GiB366.08 GiB17.92 GiB8±37%
GLM-4.6-Derestricted-v3MoEQ8_0357B353.27 GiB12.22 GiB366.08 GiB17.92 GiB8±37%
NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16MoEUD-Q5_K_S561B364.80 GiB0.00 GiB365.38 GiB18.62 GiB10±37%
MiMo-V2.5-ProMoEKV unresolvedUD-IQ3_S1023B351.94 GiB11.62 GiB364.18 GiB19.82 GiB10±37%
Kimi-K2.7-CodeMoEUD-IQ3_XXS1059B351.00 GiB2.28 GiB353.91 GiB30.09 GiB12±37%
Kimi-K2-InstructMoEQ2_K_L1026B347.81 GiB2.28 GiB350.72 GiB33.28 GiB12±37%
GLM-5.2MoEUD-IQ4_NL753B347.07 GiB2.91 GiB350.58 GiB33.42 GiB11±37%
MiniMax-M3MoEQ6_K427B344.03 GiB3.98 GiB348.58 GiB35.42 GiB11±37%
Kimi-K2.6MoEQ2_K_L1059B334.66 GiB2.28 GiB337.57 GiB46.43 GiB13±37%
Hermes-3-Llama-3.1-405BQ6_K406B310.08 GiB16.73 GiB327.65 GiB56.35 GiB2±8.3%
Hermes-4-405BQ6_K406B310.08 GiB16.73 GiB327.65 GiB56.35 GiB2±8.3%
Qwen3.5-397B-A17BMoEQ6_K_L403B325.38 GiB1.00 GiB326.97 GiB57.03 GiB14±37%
Trinity-Large-ThinkingMoEQ6_K_L399B319.94 GiB2.41 GiB322.93 GiB61.07 GiB14±37%
Ornith-1.0-397BMoEQ6_K397B318.87 GiB1.00 GiB320.47 GiB63.53 GiB14±37%
Nex-N2-ProMoEQ6_K397B318.87 GiB1.00 GiB320.47 GiB63.53 GiB14±37%
Llama-4-Maverick-17B-128E-InstructMoEKV unresolvedQ6_K402B306.19 GiB6.38 GiB313.15 GiB70.85 GiB14±37%
MiMo-V2.5MoEKV unresolvedQ8_0311B306.67 GiB3.98 GiB311.25 GiB72.75 GiB12±37%
MiMo-V2-FlashMoEKV unresolvedQ8_0310B305.69 GiB3.98 GiB310.27 GiB73.73 GiB12±37%
Trinity-Large-PreviewMoEQ6_K_L399B305.35 GiB2.41 GiB308.34 GiB75.66 GiB15±37%
Trinity-Large-TrueBaseMoEQ6_K399B305.07 GiB2.41 GiB308.06 GiB75.94 GiB15±37%
Hy3MoEQ8_0299B295.84 GiB10.63 GiB307.05 GiB76.95 GiB10±37%
ERNIE-4.5-300B-A47B-PTQ8_0300B296.43 GiB7.17 GiB304.27 GiB79.73 GiB2±8.3%
dots.llm1.instMoEBF16143B266.00 GiB32.94 GiB299.52 GiB84.48 GiB6±37%
GLM-4.6-REAP-268B-A32BMoEQ8_0269B266.14 GiB12.22 GiB278.95 GiB105.05 GiB9±37%
grok-2MoEQ8_0270B266.72 GiB8.50 GiB275.91 GiB108.09 GiB5±37%
InklingMoEUD-IQ1_M952B265.46 GiB8.77 GiB274.79 GiB109.21 GiB12±37%
Athene-70BF3270.6B262.84 GiB10.63 GiB274.14 GiB109.86 GiB3±8.3%
Solar-Open2-250BMoEQ8_0250B247.86 GiB6.38 GiB254.81 GiB129.19 GiB14±37%
Devstral-2-123B-Instruct-2512BF16125B232.89 GiB11.69 GiB245.29 GiB138.71 GiB3±8.3%
Mistral-Medium-3.5-128BBF16128B232.89 GiB11.69 GiB245.29 GiB138.71 GiB3±8.3%
Qwen3-VL-235B-A22B-InstructMoEQ8_0236B232.77 GiB6.24 GiB239.60 GiB144.40 GiB11±37%
Qwen3-VL-235B-A22B-ThinkingMoEQ8_0236B232.77 GiB6.24 GiB239.60 GiB144.40 GiB11±37%
Qwen3-235B-A22BMoEQ8_0235B232.77 GiB6.24 GiB239.60 GiB144.40 GiB11±37%
Qwen3-235B-A22B-Thinking-2507MoEQ8_0235B232.77 GiB6.24 GiB239.60 GiB144.40 GiB11±37%
Qwen3-235B-A22B-Instruct-2507MoEQ8_0235B232.77 GiB6.24 GiB239.60 GiB144.40 GiB11±37%
DeepSeek-Coder-V2-Instruct-0724MoEQ8_0236B233.41 GiB2.24 GiB236.24 GiB147.76 GiB15±37%
DeepSeek-V2.5MoEQ8_0236B233.41 GiB2.24 GiB236.24 GiB147.76 GiB15±37%
DeepSeek-Coder-V2-InstructMoEQ8_0236B233.41 GiB2.24 GiB236.24 GiB147.76 GiB15±37%
MiniMax-M2.1MoEQ8_0229B226.44 GiB8.23 GiB235.21 GiB148.79 GiB13±37%
MiniMax-M2.5MoEQ8_0229B226.44 GiB8.23 GiB235.21 GiB148.79 GiB13±37%
●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 M3 Ultra run?
2116 of 2118 indexed open-weight models fit a Apple M3 Ultra at 65,536 context with q8_0 KV cache, the largest being Step-3.7-Flash at BF16. That covers text, vision-language, image, video and speech models.
How much usable memory does a Apple M3 Ultra actually have?
Its nameplate is 512 GB, but about 357.12 GiB is available to a model once driver and compositor overhead is accounted for, and only 384 GB of the pool can be allocated to the GPU at all.
Is a Apple M3 Ultra fast for local AI?
Its memory bandwidth is 819 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.