Apple M4 Max
The Apple M4 Max ships in 36–128 GB unified-memory configurations at 410–546 GB/s. Across those configurations it runs 73 of our 99 tracked models natively in VRAM at 8k context.
More memory means more of our tracked models fit natively; see which configuration you need below.
| Configuration | Bandwidth | CPU cores | Native models | + Offload |
|---|---|---|---|---|
| 128 GB | 546 GB/s | 16 (12P + 4E) | 73 / 99 | 0 |
| 64 GB | 546 GB/s | 16 (12P + 4E) | 63 / 99 | 0 |
| 48 GB | 546 GB/s | 16 (12P + 4E) | 58 / 99 | 0 |
| 36 GB | 410 GB/s | 14 (10P + 4E) | 54 / 99 | 0 |
How much of the Apple M4 Max's memory is actually usable?
macOS and background apps need a slice of the pool before a model gets to use it: this site reserves 8GB on every unified-memory GPU, the same baseline used everywhere else on this site. What's left is real headroom for a model's weights and KV cache:
M4 Max's cheapest configuration outruns M3 Max's fastest one
Like the M3 Max before it, M4 Max ships on two physical dies: a cut-down 14-core CPU/32-core GPU die that only comes with 36GB of memory, and a full 16-core CPU/40-core GPU die at 48GB, 64GB, and 128GB. Comparing the binned base tier against each generation's full-die flagship shows just how far the floor moved in one generation:
M4 Max's binned 36GB configuration (this page) reaches 410 GB/s, 2.5% faster than M3 Max's full, non-binned 400 GB/s flagship die, even though this is the cheapest way into M4 Max. The full-die M4 Max climbs further still, to 546 GB/s, 36.5% ahead of the equivalent M3 Max tier. Since LLM decode is bandwidth-bound, that means the entry-level M4 Max MacBook Pro out-decodes last generation's top-spec M3 Max on every model both fit, a real generational leap, not just a bigger number on the base config's spec sheet.
Apple M4 Max (128GB)
With 128 GB LPDDR5X at 546 GB/s, this configuration runs 73 models natively. With 120 GB of real headroom, this is the first M4 Max configuration to fit genuine frontier-scale MoE models: Qwen3 235B-A22B fits at Q2_K (102.05 GB, 14.8 tok/s) and GPT-OSS 120B reaches Q6_K (107.93 GB, 30.6 tok/s), both about 37% faster than the identically-sized M3 Max 128GB manages on the same fits (10.8 and 22.4 tok/s there), tracking the 546-vs-400 GB/s bandwidth gap almost exactly. Dense models do well too: Llama 3.3 70B and Qwen2.5 72B both reach Q8_0 (86.35 GB and 88.73 GB), near-full precision, at 5.7 and 5.5 tok/s. 73 of the 99 tracked models fit natively.
Apple M4 Max (128GB): the maximum-memory M4 Max configuration, launched October 2024 in the MacBook Pro and, from March 2025, the Mac Studio, on the full, un-binned 16-core CPU (12P+4E)/40-core GPU die at 546 GB/s, the fastest memory bandwidth Apple has ever shipped in a laptop, and a real generational jump from the M3 Max's 400 GB/s ceiling.
With 120 GB of real headroom, this is the first M4 Max configuration to fit genuine frontier-scale MoE models: Qwen3 235B-A22B fits at Q2_K (102.05 GB, 14.8 tok/s) and GPT-OSS 120B reaches Q6_K (107.93 GB, 30.6 tok/s), both about 37% faster than the identically-sized M3 Max 128GB manages on the same fits (10.8 and 22.4 tok/s there), tracking the 546-vs-400 GB/s bandwidth gap almost exactly. Dense models do well too: Llama 3.3 70B and Qwen2.5 72B both reach Q8_0 (86.35 GB and 88.73 GB), near-full precision, at 5.7 and 5.5 tok/s. 73 of the 99 tracked models fit natively.
MLX and llama.cpp's Metal backend are both fully optimized here, and Apple Silicon's efficiency advantage over a discrete-GPU workstation is largest at this end of the lineup; this much memory bandwidth in a laptop chassis has no real equivalent on the discrete-GPU side.
Models the 128 GB configuration runs natively (73)
- DeepSeek V4 Flash 0731 284B284B · MMLU-Pro N/AUD-IQ3_XXS · ~27.4 t/s
- Qwen3 235B-A22B (MoE)235B · MMLU-Pro 84.4Q2_K · ~14.8 t/s
- MiniMax M2.5 229B229B · MMLU-Pro 84.8Q2_K · ~29.6 t/s
- MiniMax M2.7 229B229B · MMLU-Pro 86.0Q2_K · ~29.6 t/s
- Step 3.7 Flash198B · MMLU-Pro N/AQ3_K_M · ~22.8 t/s
Show 68 more
- Step 3.5 Flash196.81B · MMLU-Pro 84.4Q3_K_M · ~22.8 t/s
- Qwen3.8-Flash-Next180B · MMLU-Pro N/AQ3_K_M · ~44.5 t/s
- Mixtral 8x22B Instruct v0.1141B · MMLU-Pro 40.0Q5_K_M · ~4.6 t/s
- Mistral Medium 3.5 128B128B · MMLU-Pro N/AQ5_K_M · ~4.6 t/s
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q6_K · ~15.8 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q6_K · ~13 t/s
- GPT-OSS 120B117B · MMLU-Pro 80.7Q6_K · ~30.6 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q6_K · ~8.9 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q6_K · ~12.7 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q6_K · ~12.7 t/s
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q8_0 · ~5.5 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q8_0 · ~5.7 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q8_0 · ~5.7 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q8_0 · ~5.7 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7BF16 · ~5 t/s
- Command-R 35B35B · MMLU-Pro 33.0BF16 · ~5.4 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3BF16 · ~21.7 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2BF16 · ~6.1 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/ABF16 · ~21.7 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0BF16 · ~6.2 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5BF16 · ~6.5 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0BF16 · ~6.5 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3BF16 · ~6.5 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0BF16 · ~6.5 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3BF16 · ~21.4 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2BF16 · ~6.9 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5BF16 · ~21 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6BF16 · ~21.8 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/ABF16 · ~7.8 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0BF16 · ~7.6 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5BF16 · ~7.9 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2BF16 · ~8 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2BF16 · ~8 t/s
- Bonsai 27B27B · MMLU-Pro ~81.5Ternary (Q2_0) · ~56.5 t/s
- Bonsai 2 27B27B · MMLU-Pro N/ATernary (Q2_0) · ~67.9 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/ABF16 · ~8 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6BF16 · ~17 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8BF16 · ~8.9 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2BF16 · ~9.4 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9BF16 · ~18 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~14.1 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~14.1 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~14.9 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~17 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~17.2 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~16 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~20.6 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~23.9 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/ABF16 · ~23.9 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~25.6 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~25.6 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~25.4 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~27.9 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~28 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~51.4 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~48.5 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~40.4 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~50.4 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~59.5 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~67.2 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~71.9 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0BF16 · ~99.2 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~87.2 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~135 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~158.9 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~187.7 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~396.9 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~413.8 t/s
Apple M4 Max (64GB)
With 64 GB LPDDR5X at 546 GB/s, this configuration runs 63 models natively. With 56 GB of real headroom, this is the first M4 Max tier where 70B-class dense models clear a real quantization instead of the aggressive Q2_K floor: Llama 3.3 70B reaches Q4_K_M (50.75 GB, 9.6 tok/s) and Qwen2.5 72B reaches Q4_K_M (52.12 GB, 9.4 tok/s). Command-R 35B goes further, hitting near-full-precision Q8_0 (53.70 GB, 9.1 tok/s). MoE models are faster still at this size: Qwen 3.5 35B-A3B fits at Q8_0 (41.86 GB, 40.5 tok/s). 63 of the 99 tracked models fit natively.
Apple M4 Max (64GB): the mid-tier M4 Max configuration, available in the MacBook Pro and Mac Studio on the same full 16-core CPU/40-core GPU die as the 48GB and 128GB builds, all sharing the generation's 546 GB/s ceiling; only the 36GB base configuration ships on a slower, cut-down die (see this page's bandwidth note).
With 56 GB of real headroom, this is the first M4 Max tier where 70B-class dense models clear a real quantization instead of the aggressive Q2_K floor: Llama 3.3 70B reaches Q4_K_M (50.75 GB, 9.6 tok/s) and Qwen2.5 72B reaches Q4_K_M (52.12 GB, 9.4 tok/s). Command-R 35B goes further, hitting near-full-precision Q8_0 (53.70 GB, 9.1 tok/s). MoE models are faster still at this size: Qwen 3.5 35B-A3B fits at Q8_0 (41.86 GB, 40.5 tok/s). 63 of the 99 tracked models fit natively.
MLX and llama.cpp's Metal backend are both mature here. Nothing this configuration fits comes close to the default ~48 GB Metal working-set limit (75% of 64 GB), the largest fit here is under 54 GB, so the manual wired_limit override other Apple Silicon pages need doesn't bind at this capacity.
Models the 64 GB configuration runs natively (63)
- Qwen 3.5 122B-A10B (MoE)122B · MMLU-Pro 86.7Q2_K · ~33.9 t/s
- Nemotron 3 Super 120B120B · MMLU-Pro 83.7Q2_K · ~27.3 t/s
- Llama 4 Scout 109B109B · MMLU-Pro 74.3Q2_K · ~18 t/s
- GLM-4.5 Air 106B106B · MMLU-Pro 81.4Q2_K · ~26 t/s
- GLM-4.6V 106B106B · MMLU-Pro 79.9Q2_K · ~26 t/s
Show 58 more
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q4_K_M · ~9.4 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q4_K_M · ~9.6 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q4_K_M · ~9.6 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q4_K_M · ~9.6 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q6_K · ~12 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q8_0 · ~9.1 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q8_0 · ~40.5 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q8_0 · ~11.1 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ8_0 · ~40.5 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q8_0 · ~11.3 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q8_0 · ~12.1 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q8_0 · ~11.9 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3Q8_0 · ~11.9 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q8_0 · ~11.9 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q8_0 · ~39.5 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2Q8_0 · ~12.8 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q8_0 · ~38.2 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6Q8_0 · ~40.9 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/AQ8_0 · ~14.7 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q8_0 · ~13.6 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q8_0 · ~14.4 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q8_0 · ~14.9 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2Q8_0 · ~14.9 t/s
- Bonsai 27B27B · MMLU-Pro ~81.5Ternary (Q2_0) · ~56.5 t/s
- Bonsai 2 27B27B · MMLU-Pro N/ATernary (Q2_0) · ~67.9 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/AQ8_0 · ~14.9 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6Q8_0 · ~31.6 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8BF16 · ~8.9 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2BF16 · ~9.4 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9BF16 · ~18 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~14.1 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~14.1 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~14.9 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~17 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~17.2 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~16 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~20.6 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~23.9 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/ABF16 · ~23.9 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~25.6 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~25.6 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~25.4 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~27.9 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~28 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~51.4 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~48.5 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~40.4 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~50.4 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~59.5 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~67.2 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~71.9 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0BF16 · ~99.2 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~87.2 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~135 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~158.9 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~187.7 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~396.9 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~413.8 t/s
Apple M4 Max (48GB)
With 48 GB LPDDR5X at 546 GB/s, this configuration runs 58 models natively. With 40 GB of real headroom (identical to the M4 Pro 48GB configuration), this tier fits the same 58 of 99 tracked models, but at exactly double the speed on every shared fit: Llama 3.3 70B reaches the same Q2_K (32.88 GB) at 14.9 tok/s here versus 7.4 tok/s on the M4 Pro 48GB, and Qwen2.5 72B reaches 14.5 tok/s versus 7.3 tok/s, both track the 546-vs-273 GB/s bandwidth ratio almost exactly. MoE models do particularly well: Qwen 3.5 35B-A3B fits at near-full Q6_K (32.37 GB, 52.1 tok/s).
Apple M4 Max (48GB): the entry point into M4 Max's full, un-binned 16-core CPU/40-core GPU die, available in the MacBook Pro and Mac Studio, the same 546 GB/s as the 64GB and 128GB builds, a real step up from the cut-down 36GB base configuration's 410 GB/s.
With 40 GB of real headroom (identical to the M4 Pro 48GB configuration), this tier fits the same 58 of 99 tracked models, but at exactly double the speed on every shared fit: Llama 3.3 70B reaches the same Q2_K (32.88 GB) at 14.9 tok/s here versus 7.4 tok/s on the M4 Pro 48GB, and Qwen2.5 72B reaches 14.5 tok/s versus 7.3 tok/s, both track the 546-vs-273 GB/s bandwidth ratio almost exactly. MoE models do particularly well: Qwen 3.5 35B-A3B fits at near-full Q6_K (32.37 GB, 52.1 tok/s).
MLX and llama.cpp's Metal backend are both fully supported. Choosing this over the identically-priced-per-GB M4 Pro 48GB buys roughly 2x decode speed on every model both fit; the Max tier's advantage here is bandwidth, not capacity.
Models the 48 GB configuration runs natively (58)
- Qwen 2.5 72B Instruct72B · MMLU-Pro 71.1Q2_K · ~14.5 t/s
- Llama 3.3 70B Instruct70B · MMLU-Pro 68.9Q2_K · ~14.9 t/s
- DeepSeek R1 Distill Llama 70B70B · MMLU-Pro 70.0Q2_K · ~14.9 t/s
- Llama 3.1 70B Instruct70B · MMLU-Pro 66.4Q2_K · ~14.9 t/s
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q5_K_M · ~13.8 t/s
Show 53 more
- Command-R 35B35B · MMLU-Pro 33.0Q5_K_M · ~12.2 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q6_K · ~52.1 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q6_K · ~14.1 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ6_K · ~52.1 t/s
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q6_K · ~14.4 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q6_K · ~15.5 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q6_K · ~15.2 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3Q6_K · ~15.2 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q6_K · ~15.2 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q8_0 · ~39.5 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2Q8_0 · ~12.8 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q8_0 · ~38.2 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6Q8_0 · ~40.9 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/AQ8_0 · ~14.7 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q8_0 · ~13.6 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q8_0 · ~14.4 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q8_0 · ~14.9 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2Q8_0 · ~14.9 t/s
- Bonsai 27B27B · MMLU-Pro ~81.5Ternary (Q2_0) · ~56.5 t/s
- Bonsai 2 27B27B · MMLU-Pro N/ATernary (Q2_0) · ~67.9 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/AQ8_0 · ~14.9 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6Q8_0 · ~31.6 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q8_0 · ~16.3 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q8_0 · ~17.1 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q8_0 · ~33.7 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0BF16 · ~14.1 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7BF16 · ~14.1 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4BF16 · ~14.9 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6BF16 · ~17 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6BF16 · ~17.2 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2BF16 · ~16 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~20.6 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~23.9 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/ABF16 · ~23.9 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~25.6 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~25.6 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~25.4 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~27.9 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~28 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~51.4 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~48.5 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~40.4 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~50.4 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~59.5 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~67.2 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~71.9 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0BF16 · ~99.2 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~87.2 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~135 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~158.9 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~187.7 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~396.9 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~413.8 t/s
Apple M4 Max (36GB)
With 36 GB LPDDR5X at 410 GB/s, this configuration runs 54 models natively. With 28 GB of real headroom, this configuration fits the same 54 of 99 tracked models as the identically-sized M3 Max 36GB and M3 Pro 36GB (capacity decides what fits, not bandwidth), but runs about 37% faster than the M3 Max 36GB on every shared fit, since 410 GB/s is 37% more than that chip's 300 GB/s. Qwen3 32B reaches Q5_K_M (27.66 GB) at 13.3 tok/s here versus 9.7 tok/s on the M3 Max 36GB, and Qwen 3.5 35B-A3B reaches Q4_K_M (24.06 GB) at 52.4 tok/s versus 38.4 tok/s, both track the 410-vs-300 GB/s ratio closely. Even on this cut-down die, 410 GB/s outright beats the previous generation's full, non-binned M3 Max flagship die (400 GB/s).
Apple M4 Max (36GB): the standard base configuration of Apple's M4 Max chip, launched October 2024 in the MacBook Pro and, from March 2025, the base Mac Studio, on a cut-down 14-core CPU (10P+4E)/32-core GPU die at 410 GB/s, 136 GB/s less than the 546 GB/s the 48GB, 64GB, and 128GB configurations get on the full die. Apple offers no way to pair 36GB with the faster die, the same binned-base-tier pattern the M3 Max shipped a generation earlier (see this page's bandwidth note).
With 28 GB of real headroom, this configuration fits the same 54 of 99 tracked models as the identically-sized M3 Max 36GB and M3 Pro 36GB (capacity decides what fits, not bandwidth), but runs about 37% faster than the M3 Max 36GB on every shared fit, since 410 GB/s is 37% more than that chip's 300 GB/s. Qwen3 32B reaches Q5_K_M (27.66 GB) at 13.3 tok/s here versus 9.7 tok/s on the M3 Max 36GB, and Qwen 3.5 35B-A3B reaches Q4_K_M (24.06 GB) at 52.4 tok/s versus 38.4 tok/s, both track the 410-vs-300 GB/s ratio closely. Even on this cut-down die, 410 GB/s outright beats the previous generation's full, non-binned M3 Max flagship die (400 GB/s).
MLX and llama.cpp's Metal backend are both mature here. The default macOS Metal working-set limit (~75% of 36 GB, 27 GB) sits just under this page's largest fit (Qwen3 32B's 27.66 GB Q5_K_M build), so raising it with `sudo sysctl iogpu.wired_limit_mb=28672` (leaving 8 GB for macOS) covers it with room to spare, the same fix and figure this site notes for the identically-sized M3 Max and M3 Pro 36GB configurations.
Models the 36 GB configuration runs natively (54)
- Mixtral 8x7B Instruct v0.146.7B · MMLU-Pro 29.7Q3_K_M · ~15.1 t/s
- Command-R 35B35B · MMLU-Pro 33.0Q2_K · ~13.6 t/s
- Qwen 3.5 35B-A3B (MoE)35B · MMLU-Pro 85.3Q4_K_M · ~52.4 t/s
- Qwen 3.6 35B35B · MMLU-Pro 85.2Q4_K_M · ~14 t/s
- Ornith 1.5 35B-A3B (MoE)35B · MMLU-Pro N/AQ4_K_M · ~52.4 t/s
Show 49 more
- Yi 1.5 34B Chat34.4B · MMLU-Pro 37.0Q4_K_M · ~14.3 t/s
- Qwen3 32B32.8B · MMLU-Pro 65.5Q5_K_M · ~13.3 t/s
- Qwen 2.5 32B Instruct32.5B · MMLU-Pro 69.0Q4_K_M · ~14.9 t/s
- Qwen 2.5 Coder 32B Instruct32.5B · MMLU-Pro 62.3Q4_K_M · ~14.9 t/s
- DeepSeek R1 Distill Qwen 32B32.5B · MMLU-Pro 65.0Q4_K_M · ~14.9 t/s
- Nemotron 3 Nano 30B32B · MMLU-Pro 78.3Q5_K_M · ~43.4 t/s
- Gemma 4 31B30.7B · MMLU-Pro 85.2Q5_K_M · ~14 t/s
- Qwen3 30B-A3B (MoE)30B · MMLU-Pro 61.5Q5_K_M · ~41.4 t/s
- Nemotron 3.5 Lightning 30B-A3B30B · MMLU-Pro 81.6Q6_K · ~39.7 t/s
- Muse Glimmer 30B27.8B · MMLU-Pro N/AQ6_K · ~14.3 t/s
- Gemma 2 27B Instruct27.2B · MMLU-Pro 38.0Q5_K_M · ~14.6 t/s
- Gemma 3 27B Instruct27B · MMLU-Pro 67.5Q6_K · ~13.8 t/s
- Qwen 3.6 27B27B · MMLU-Pro 86.2Q6_K · ~14.4 t/s
- UI-Mate 27B27B · MMLU-Pro ~86.2Q6_K · ~14.4 t/s
- Bonsai 27B27B · MMLU-Pro ~81.5Ternary (Q2_0) · ~42.4 t/s
- Bonsai 2 27B27B · MMLU-Pro N/ATernary (Q2_0) · ~51 t/s
- Qwen 3.8 27B27B · MMLU-Pro N/AQ6_K · ~14.4 t/s
- Gemma 4 26B (MoE)25.2B · MMLU-Pro 82.6Q6_K · ~30.4 t/s
- Mistral Small 3.1 24B Instruct24B · MMLU-Pro 66.8Q6_K · ~15.6 t/s
- Mistral Small 22B22.2B · MMLU-Pro 49.2Q6_K · ~16.3 t/s
- GPT-OSS 20B21B · MMLU-Pro 67.9Q8_0 · ~25.3 t/s
- Qwen3 14B14.8B · MMLU-Pro 61.0Q8_0 · ~19.2 t/s
- Qwen 2.5 14B Instruct14.7B · MMLU-Pro 63.7Q8_0 · ~19 t/s
- Phi-4 14B Instruct14B · MMLU-Pro 70.4Q8_0 · ~20.2 t/s
- Mistral Nemo 12B Instruct12.2B · MMLU-Pro 35.6Q8_0 · ~22.9 t/s
- Gemma 3 12B Instruct12.2B · MMLU-Pro 60.6Q8_0 · ~23.4 t/s
- Gemma 4 12B (Unified)12B · MMLU-Pro 77.2Q8_0 · ~20.5 t/s
- Gemma 2 9B Instruct9.2B · MMLU-Pro 32.0BF16 · ~15.5 t/s
- Qwen 3.5 9B9B · MMLU-Pro 82.5BF16 · ~18 t/s
- Ornith 1.5 9B9B · MMLU-Pro N/ABF16 · ~18 t/s
- Llama 3.1 8B Instruct8B · MMLU-Pro 48.3BF16 · ~19.2 t/s
- DeepSeek R1 Distill Llama 8B8B · MMLU-Pro 41.0BF16 · ~19.2 t/s
- Qwen3 8B8B · MMLU-Pro 56.7BF16 · ~19.1 t/s
- Qwen 2.5 7B Instruct7.6B · MMLU-Pro 56.3BF16 · ~20.9 t/s
- Mistral 7B Instruct v0.37.25B · MMLU-Pro 30.0BF16 · ~21.1 t/s
- Gemma 3 4B Instruct4B · MMLU-Pro 43.6BF16 · ~38.6 t/s
- Gemma 4 E4B4B · MMLU-Pro 69.4BF16 · ~36.4 t/s
- Phi-3.5 Mini Instruct3.8B · MMLU-Pro 47.4BF16 · ~30.3 t/s
- Phi-4-mini Instruct3.8B · MMLU-Pro 67.3BF16 · ~37.8 t/s
- Llama 3.2 3B Instruct3.2B · MMLU-Pro 24.0BF16 · ~44.7 t/s
- Qwen 2.5 3B Instruct3.1B · MMLU-Pro 32.4BF16 · ~50.4 t/s
- Gemma 2 2B Instruct2.6B · MMLU-Pro 17.8BF16 · ~54 t/s
- Gemma 4 E2B2B · MMLU-Pro 60.0BF16 · ~74.5 t/s
- SmolLM2 1.7B Instruct1.7B · MMLU-Pro 19.0BF16 · ~65.5 t/s
- Qwen 2.5 1.5B Instruct1.5B · MMLU-Pro 16.8BF16 · ~101.4 t/s
- Llama 3.2 1B Instruct1.24B · MMLU-Pro 12.5BF16 · ~119.3 t/s
- Gemma 3 1B Instruct1B · MMLU-Pro 14.7BF16 · ~140.9 t/s
- Qwen 2.5 0.5B Instruct0.5B · MMLU-Pro 10.0BF16 · ~298 t/s
- SmolLM2 360M Instruct0.36B · MMLU-Pro 8.0BF16 · ~310.7 t/s
Too large for any Apple M4 Max configuration (26)
- Llama 3.1 405B Instruct
- DeepSeek V3 671B
- DeepSeek R1 671B
- Llama 4 Maverick 400B
- MiniMax M1 456B
- GLM-4.5 355B
- GLM-4.6 355B
- GLM-4.7 358B
- GLM-5 744B
- Kimi K2.6
- GLM-5.1 754B
- DeepSeek V4 Pro 1.6T
- DeepSeek V4 Flash 284B
- GLM-5.2 753B
- Nemotron 3 Ultra 550B-A55B
- MiMo V2.5 Pro
- Kimi K2.5
- MiniMax M3
- Inkling
- Kimi K3
- Qwen3.8 2.4T-A95B
- DeepSeek V4 Pro 0813 1.6T
- Ornith 1.5 397B (MoE)
- GLM-5.3 753B
- GLM-5.3-Flash 320B
- DeepSeek V4.1 Flash 552B
Compare Apple M4 Max with other GPUs
- Apple M4 Max (128GB)vsApple M3 Max (128GB)128 GB each
- Apple M4 Max (128GB)vsApple M1 Ultra (128GB)128 GB each
- Apple M4 Max (128GB)vsNVIDIA RTX 5090+96 GB VRAM
- Apple M4 Max (128GB)vsApple M3 Ultra (96GB)+32 GB VRAM
- Apple M4 Max (128GB)vsAMD Strix Halo (128GB)128 GB each
- Apple M4 Max (64GB)vsApple M3 Max (64GB)64 GB each
- Apple M4 Max (64GB)vsAMD Strix Halo (64GB)64 GB each
- Apple M4 Max (48GB)vsApple M3 Max (48GB)48 GB each
- Apple M4 Max (36GB)vsApple M3 Max (36GB)36 GB each
Continue reading
Frequently asked questions
- How much memory does the Apple M4 Max have?
- The Apple M4 Max ships in 4 unified-memory configurations: 128 GB, 64 GB, 48 GB, 36 GB, at 410–546 GB/s depending on configuration.
- Should I get the 36 GB or 128 GB Apple M4 Max?
- Both run everything that fits natively in 36 GB. The extra memory in the 128 GB configuration additionally fits DeepSeek V4 Flash 0731 284B, Qwen3 235B-A22B (MoE), MiniMax M2.5 229B, and 16 more models natively in VRAM, worth the upgrade if you plan to run any of those.
- How much VRAM does the Apple M4 Max (128GB) have?
- The Apple M4 Max (128GB) has 128 GB of LPDDR5X with 546 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M4 Max (128GB) best for?
- With 128 GB of unified memory, the Apple M4 Max (128GB) is a high-capacity laptop platform that runs 70B-class dense models and large MoE models natively, with plenty of room for long context.
- What LLMs can the Apple M4 Max (128GB) run locally?
- The Apple M4 Max (128GB) can run 73 of the 99 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen3.8-Flash-Next at Q3_K_M, DeepSeek V4 Flash 0731 284B at UD-IQ3_XXS, Qwen 3.8 27B at BF16.
- Can the Apple M4 Max (128GB) run Gemma 4 31B?
- Yes. The Apple M4 Max (128GB) runs Gemma 4 31B natively in VRAM at BF16 quantization, achieving approximately 6.9 tokens per second.
Show 20 more questions
- Can the Apple M4 Max (128GB) run Qwen 3.6 27B?
- Yes. The Apple M4 Max (128GB) runs Qwen 3.6 27B natively in VRAM at BF16 quantization, achieving approximately 8 tokens per second.
- Can the Apple M4 Max (128GB) run Qwen3 8B?
- Yes. The Apple M4 Max (128GB) runs Qwen3 8B natively in VRAM at BF16 quantization, achieving approximately 25.4 tokens per second.
- How much VRAM does the Apple M4 Max (64GB) have?
- The Apple M4 Max (64GB) has 64 GB of LPDDR5X with 546 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M4 Max (64GB) best for?
- With 64 GB of VRAM, the Apple M4 Max (64GB) is ideal for running 70B-class models at Q4 quantization and large MoE models, a workstation sweet spot for local inference.
- What LLMs can the Apple M4 Max (64GB) run locally?
- The Apple M4 Max (64GB) can run 63 of the 99 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at Q8_0, Ornith 1.5 35B-A3B (MoE) at Q8_0, Qwen 3.5 122B-A10B (MoE) at Q2_K.
- Can the Apple M4 Max (64GB) run Gemma 4 31B?
- Yes. The Apple M4 Max (64GB) runs Gemma 4 31B natively in VRAM at Q8_0 quantization, achieving approximately 12.8 tokens per second.
- Can the Apple M4 Max (64GB) run Qwen 3.6 27B?
- Yes. The Apple M4 Max (64GB) runs Qwen 3.6 27B natively in VRAM at Q8_0 quantization, achieving approximately 14.9 tokens per second.
- Can the Apple M4 Max (64GB) run Qwen3 8B?
- Yes. The Apple M4 Max (64GB) runs Qwen3 8B natively in VRAM at BF16 quantization, achieving approximately 25.4 tokens per second.
- How much VRAM does the Apple M4 Max (48GB) have?
- The Apple M4 Max (48GB) has 48 GB of LPDDR5X with 546 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M4 Max (48GB) best for?
- With 48 GB of VRAM, the Apple M4 Max (48GB) is ideal for running 70B-class models at Q3-class quantization and large MoE models, a workstation sweet spot for local inference.
- What LLMs can the Apple M4 Max (48GB) run locally?
- The Apple M4 Max (48GB) can run 58 of the 99 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at Q8_0, Ornith 1.5 35B-A3B (MoE) at Q6_K, Ornith 1.5 9B at BF16.
- Can the Apple M4 Max (48GB) run Gemma 4 31B?
- Yes. The Apple M4 Max (48GB) runs Gemma 4 31B natively in VRAM at Q8_0 quantization, achieving approximately 12.8 tokens per second.
- Can the Apple M4 Max (48GB) run Qwen 3.6 27B?
- Yes. The Apple M4 Max (48GB) runs Qwen 3.6 27B natively in VRAM at Q8_0 quantization, achieving approximately 14.9 tokens per second.
- Can the Apple M4 Max (48GB) run Qwen3 8B?
- Yes. The Apple M4 Max (48GB) runs Qwen3 8B natively in VRAM at BF16 quantization, achieving approximately 25.4 tokens per second.
- How much VRAM does the Apple M4 Max (36GB) have?
- The Apple M4 Max (36GB) has 36 GB of LPDDR5X with 410 GB/s memory bandwidth (unified system memory, shared between CPU and GPU).
- What is the Apple M4 Max (36GB) best for?
- With 36 GB of VRAM, the Apple M4 Max (36GB) is well-suited for running 7B–32B models at Q4 with room for context, making it a great all-rounder for local LLM inference.
- What LLMs can the Apple M4 Max (36GB) run locally?
- The Apple M4 Max (36GB) can run 54 of the 99 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Qwen 3.8 27B at Q6_K, Ornith 1.5 35B-A3B (MoE) at Q4_K_M, Ornith 1.5 9B at BF16.
- Can the Apple M4 Max (36GB) run Gemma 4 31B?
- Yes. The Apple M4 Max (36GB) runs Gemma 4 31B natively in VRAM at Q5_K_M quantization, achieving approximately 14 tokens per second.
- Can the Apple M4 Max (36GB) run Qwen 3.6 27B?
- Yes. The Apple M4 Max (36GB) runs Qwen 3.6 27B natively in VRAM at Q6_K quantization, achieving approximately 14.4 tokens per second.
- Can the Apple M4 Max (36GB) run Qwen3 8B?
- Yes. The Apple M4 Max (36GB) runs Qwen3 8B natively in VRAM at BF16 quantization, achieving approximately 19.1 tokens per second.