Quick Answer: Apple M4 Max offers 128GB VRAM and starts around current market pricing. It delivers approximately 96 tokens/sec on meta-llama/Llama-3.2-3B. It typically draws 45W under load.
This GPU offers reliable throughput for local AI workloads. Pair it with the right model quantization to hit your desired tokens/sec, and monitor prices below to catch the best deal.
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| Model | Quantization | Tokens/sec | VRAM used |
|---|---|---|---|
| meta-llama/Llama-3.2-3B | Q4 | 96.22 tok/sEstimated Auto-generated benchmark | 2GB |
| facebook/sam3 | Q4 | 95.79 tok/sEstimated Auto-generated benchmark | 1GB |
| google-bert/bert-base-uncased | Q4 | 93.60 tok/sEstimated Auto-generated benchmark | 1GB |
| WeiboAI/VibeThinker-1.5B | Q4 | 93.07 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2b | Q4 | 92.62 tok/sEstimated Auto-generated benchmark | 1GB |
| unsloth/Llama-3.2-1B-Instruct | Q4 | 91.57 tok/sEstimated Auto-generated benchmark | 1GB |
| allenai/OLMo-2-0425-1B | Q4 | 91.07 tok/sEstimated Auto-generated benchmark | 1GB |
| google-t5/t5-3b | Q4 | 90.45 tok/sEstimated Auto-generated benchmark | 2GB |
| inference-net/Schematron-3B | Q4 | 90.36 tok/sEstimated Auto-generated benchmark | 2GB |
| google/embeddinggemma-300m | Q4 | 90.08 tok/sEstimated Auto-generated benchmark | 1GB |
| google/gemma-2-2b-it | Q4 | 88.95 tok/sEstimated Auto-generated benchmark | 1GB |
| context-labs/meta-llama-Llama-3.2-3B-Instruct-FP16 | Q4 | 88.80 tok/sEstimated Auto-generated benchmark | 2GB |
| apple/OpenELM-1_1B-Instruct | Q4 | 88.43 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-granite/granite-3.3-2b-instruct | Q4 | 87.40 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B-Instruct | Q4 | 86.68 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B-Instruct | Q4 | 86.09 tok/sEstimated Auto-generated benchmark | 1GB |
| ibm-research/PowerMoE-3b | Q4 | 85.97 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/Llama-3.2-3B-Instruct | Q4 | 84.68 tok/sEstimated Auto-generated benchmark | 2GB |
| bigcode/starcoder2-3b | Q4 | 84.29 tok/sEstimated Auto-generated benchmark | 2GB |
| unsloth/gemma-3-1b-it | Q4 | 83.24 tok/sEstimated Auto-generated benchmark | 1GB |
| deepseek-ai/DeepSeek-OCR | Q4 | 83.16 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Llama-3.2-1B | Q4 | 83.03 tok/sEstimated Auto-generated benchmark | 1GB |
| TinyLlama/TinyLlama-1.1B-Chat-v1.0 | Q4 | 82.98 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2.5-3B | Q4 | 82.58 tok/sEstimated Auto-generated benchmark | 2GB |
| LiquidAI/LFM2-1.2B | Q4 | 81.79 tok/sEstimated Auto-generated benchmark | 1GB |
| nari-labs/Dia2-2B | Q4 | 81.37 tok/sEstimated Auto-generated benchmark | 2GB |
| google/gemma-3-1b-it | Q4 | 80.72 tok/sEstimated Auto-generated benchmark | 1GB |
| microsoft/DialoGPT-small | Q4 | 80.47 tok/sEstimated Auto-generated benchmark | 4GB |
| GSAI-ML/LLaDA-8B-Base | Q4 | 80.43 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/DeepSeek-V3.1 | Q4 | 80.30 tok/sEstimated Auto-generated benchmark | 4GB |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q4 | 80.24 tok/sEstimated Auto-generated benchmark | 2GB |
| meta-llama/Meta-Llama-3-8B | Q4 | 80.09 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-4B-Thinking-2507-FP8 | Q4 | 79.87 tok/sEstimated Auto-generated benchmark | 2GB |
| Qwen/Qwen2-7B-Instruct | Q4 | 79.79 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-Guard-3-1B | Q4 | 79.71 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen2-0.5B-Instruct | Q4 | 79.69 tok/sEstimated Auto-generated benchmark | 3GB |
| ibm-granite/granite-docling-258M | Q4 | 79.67 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-3.2-3B-Instruct | Q4 | 79.46 tok/sEstimated Auto-generated benchmark | 2GB |
| deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B | Q4 | 79.30 tok/sEstimated Auto-generated benchmark | 3GB |
| tencent/HunyuanOCR | Q4 | 79.24 tok/sEstimated Auto-generated benchmark | 1GB |
| Qwen/Qwen3-Embedding-0.6B | Q4 | 79.13 tok/sEstimated Auto-generated benchmark | 3GB |
| Qwen/Qwen3-8B-Base | Q4 | 79.02 tok/sEstimated Auto-generated benchmark | 4GB |
| meta-llama/Llama-2-7b-hf | Q4 | 78.99 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen3-Embedding-4B | Q4 | 78.86 tok/sEstimated Auto-generated benchmark | 2GB |
| MiniMaxAI/MiniMax-M2 | Q4 | 78.74 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-7B | Q4 | 78.70 tok/sEstimated Auto-generated benchmark | 4GB |
| Qwen/Qwen2.5-0.5B-Instruct | Q4 | 78.68 tok/sEstimated Auto-generated benchmark | 3GB |
| kaitchup/Phi-3-mini-4k-instruct-gptq-4bit | Q4 | 78.43 tok/sEstimated Auto-generated benchmark | 2GB |
| mistralai/Mistral-7B-v0.1 | Q4 | 78.39 tok/sEstimated Auto-generated benchmark | 4GB |
| Tongyi-MAI/Z-Image-Turbo | Q4 | 78.37 tok/sEstimated Auto-generated benchmark | 4GB |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
| Model | Quantization | Verdict | Estimated speed | VRAM needed |
|---|---|---|---|---|
| Qwen/Qwen2.5-0.5B | Q8 | Fits comfortably | 55.26 tok/sEstimated | 5GB (have 128GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | Q8 | Fits comfortably | 57.57 tok/sEstimated | 3GB (have 128GB) |
| deepseek-ai/deepseek-coder-1.3b-instruct | FP16 | Fits comfortably | 35.28 tok/sEstimated | 6GB (have 128GB) |
| microsoft/phi-4 | Q4 | Fits comfortably | 74.68 tok/sEstimated | 4GB (have 128GB) |
| microsoft/phi-4 | Q8 | Fits comfortably | 53.84 tok/sEstimated | 7GB (have 128GB) |
| meta-llama/Llama-3.1-8B | Q4 | Fits comfortably | 71.58 tok/sEstimated | 4GB (have 128GB) |
| LiquidAI/LFM2-1.2B | FP16 | Fits comfortably | 33.45 tok/sEstimated | 4GB (have 128GB) |
| mistralai/Mistral-7B-Instruct-v0.1 | Q4 | Fits comfortably | 76.39 tok/sEstimated | 4GB (have 128GB) |
| Qwen/Qwen3-8B-FP8 | FP16 | Fits comfortably | 29.00 tok/sEstimated | 17GB (have 128GB) |
| microsoft/DialoGPT-small | Q4 | Fits comfortably | 80.47 tok/sEstimated | 4GB (have 128GB) |
| microsoft/DialoGPT-small | Q8 | Fits comfortably | 53.01 tok/sEstimated | 7GB (have 128GB) |
| microsoft/DialoGPT-small | FP16 | Fits comfortably | 29.39 tok/sEstimated | 15GB (have 128GB) |
| Qwen/Qwen3-30B-A3B | Q8 | Fits comfortably | 28.07 tok/sEstimated | 31GB (have 128GB) |
| Qwen/Qwen3-30B-A3B | FP16 | Fits comfortably | 15.38 tok/sEstimated | 61GB (have 128GB) |
| unsloth/mistral-7b-v0.3-bnb-4bit | Q4 | Fits comfortably | 73.71 tok/sEstimated | 4GB (have 128GB) |
| swiss-ai/Apertus-8B-Instruct-2509 | Q8 | Fits comfortably | 49.10 tok/sEstimated | 9GB (have 128GB) |
| microsoft/Phi-3.5-mini-instruct | FP16 | Fits comfortably | 26.21 tok/sEstimated | 15GB (have 128GB) |
| unsloth/DeepSeek-R1-Distill-Qwen-32B-bnb-4bit | Q4 | Fits comfortably | 26.30 tok/sEstimated | 16GB (have 128GB) |
| ibm-research/PowerMoE-3b | Q4 | Fits comfortably | 85.97 tok/sEstimated | 2GB (have 128GB) |
| ibm-research/PowerMoE-3b | Q8 | Fits comfortably | 65.06 tok/sEstimated | 3GB (have 128GB) |
| ibm-research/PowerMoE-3b | FP16 | Fits comfortably | 31.89 tok/sEstimated | 6GB (have 128GB) |
| lmstudio-community/Qwen3-4B-Thinking-2507-MLX-6bit | Q4 | Fits comfortably | 73.02 tok/sEstimated | 2GB (have 128GB) |
| meta-llama/Llama-Guard-3-8B | Q4 | Fits comfortably | 66.68 tok/sEstimated | 4GB (have 128GB) |
| meta-llama/Llama-Guard-3-8B | Q8 | Fits comfortably | 51.02 tok/sEstimated | 9GB (have 128GB) |
| deepseek-ai/DeepSeek-V3-0324 | FP16 | Fits comfortably | 27.61 tok/sEstimated | 15GB (have 128GB) |
| huggyllama/llama-7b | Q4 | Fits comfortably | 68.90 tok/sEstimated | 4GB (have 128GB) |
| huggyllama/llama-7b | Q8 | Fits comfortably | 52.00 tok/sEstimated | 7GB (have 128GB) |
| huggyllama/llama-7b | FP16 | Fits comfortably | 25.72 tok/sEstimated | 15GB (have 128GB) |
| Qwen/Qwen2.5-Coder-7B-Instruct | Q8 | Fits comfortably | 48.54 tok/sEstimated | 7GB (have 128GB) |
| Qwen/Qwen3-4B-Thinking-2507 | Q8 | Fits comfortably | 48.45 tok/sEstimated | 4GB (have 128GB) |
| Qwen/Qwen3-4B-Thinking-2507 | FP16 | Fits comfortably | 26.29 tok/sEstimated | 9GB (have 128GB) |
| HuggingFaceH4/zephyr-7b-beta | Q4 | Fits comfortably | 70.43 tok/sEstimated | 4GB (have 128GB) |
| HuggingFaceH4/zephyr-7b-beta | Q8 | Fits comfortably | 50.62 tok/sEstimated | 7GB (have 128GB) |
| Qwen/Qwen3-235B-A22B | FP16 | Not supported | 3.28 tok/sEstimated | 460GB (have 128GB) |
| trl-internal-testing/tiny-LlamaForCausalLM-3.2 | Q4 | Fits comfortably | 68.17 tok/sEstimated | 4GB (have 128GB) |
| skt/kogpt2-base-v2 | Q8 | Fits comfortably | 51.87 tok/sEstimated | 7GB (have 128GB) |
| skt/kogpt2-base-v2 | FP16 | Fits comfortably | 26.74 tok/sEstimated | 15GB (have 128GB) |
| ibm-granite/granite-docling-258M | Q4 | Fits comfortably | 79.67 tok/sEstimated | 4GB (have 128GB) |
| bigcode/starcoder2-3b | FP16 | Fits comfortably | 36.05 tok/sEstimated | 6GB (have 128GB) |
| Qwen/Qwen2.5-3B | FP16 | Fits comfortably | 36.52 tok/sEstimated | 6GB (have 128GB) |
| nvidia/NVIDIA-Nemotron-Nano-9B-v2 | Q4 | Fits comfortably | 56.11 tok/sEstimated | 5GB (have 128GB) |
| Alibaba-NLP/gte-Qwen2-1.5B-instruct | Q4 | Fits comfortably | 72.49 tok/sEstimated | 3GB (have 128GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct-FP8 | Q8 | Fits comfortably | 10.44 tok/sEstimated | 78GB (have 128GB) |
| Qwen/Qwen3-Next-80B-A3B-Instruct-FP8 | FP16 | Not supported | 5.12 tok/sEstimated | 156GB (have 128GB) |
| Qwen/Qwen2.5-Coder-32B-Instruct | Q4 | Fits comfortably | 24.68 tok/sEstimated | 17GB (have 128GB) |
| Qwen/Qwen2.5-32B-Instruct | FP16 | Fits comfortably | 10.32 tok/sEstimated | 66GB (have 128GB) |
| mistralai/Mistral-7B-v0.1 | Q4 | Fits comfortably | 78.39 tok/sEstimated | 4GB (have 128GB) |
| mistralai/Mistral-7B-v0.1 | Q8 | Fits comfortably | 52.72 tok/sEstimated | 7GB (have 128GB) |
| LiquidAI/LFM2-1.2B | Q4 | Fits comfortably | 81.79 tok/sEstimated | 1GB (have 128GB) |
| mlx-community/gpt-oss-20b-MXFP4-Q8 | FP16 | Fits comfortably | 13.97 tok/sEstimated | 41GB (have 128GB) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
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