readygpu

Mac mini M4 Pro 48GB vs NVIDIA RTX 6000 Ada Generation

For running models locally, priced in the UK. At 32k context the first runs 61 models fully in memory, the second 65.

In short

Both have 48 GB of memory for models; the Mac mini M4 Pro 48GB has 273 GB/s of bandwidth and the NVIDIA RTX 6000 Ada Generation 960 GB/s. At 32k context the NVIDIA RTX 6000 Ada Generation runs 65 of the models we track fully in memory and the Mac mini M4 Pro 48GB 61; only the NVIDIA RTX 6000 Ada Generation holds Qwen 3.5 122B-A10B. On the models both run fully in memory, the NVIDIA RTX 6000 Ada Generation is about 3.4× as fast as the Mac mini M4 Pro 48GB (estimated, median across models).

Updated . Speeds are estimates; how they are worked out.

Mac mini M4 Pro 48GB

£3,000

eBay

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NVIDIA RTX 6000 Ada Generation

Not priced here

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Specifications

SpecMac mini M4 Pro 48GBNVIDIA RTX 6000 Ada Generation
ChipApple M4 Pro (20-core GPU)–
Memory bus256-bit384-bit
Memory bandwidth273 GB/s960 GB/s
FP16 compute8.08 TFLOPS91.1 TFLOPS
SoftwareMetalCUDA
Unified memory48 GB–
GPU can use by default36 GB–
GPU can use, tuned40 GB–
Operating systemmacos–
ProcessorM4 Pro 14-core–
Storage512 GB–
GPU–RTX 6000 Ada Generation
Memory–48 GB GDDR6
Board power–300 W
PCIe generation–Gen 4
PCIe lanes–x16

What each runs

Estimated decode speed with the most precise quant that fits each one est.

ModelMac mini M4 Pro 48GBNVIDIA RTX 6000 Ada Generation
Qwen 3.8 27B6tok/sQ8_019tok/sQ8_0
Qwen 3.5 122B-A10Bwon't run83tok/sUD-Q2_K_XL
Qwen 3.6 27B6tok/sQ8_019tok/sQ8_0
Gemma 4 31B5tok/sQ8_017tok/sQ8_0
Qwen3 Coder Nextwon't run110tok/sQ4_K_M
Mistral Small 4 119Bwon't run114tok/sUD-Q2_K_XL
Qwen 3.6 35B-A3B28tok/sQ8_094tok/sQ8_0
Qwen 3.5 27B6tok/sQ8_019tok/sQ8_0
Gemma 4 26B-A4B21tok/sQ8_071tok/sQ8_0
Qwen 3.5 35B-A3B28tok/sQ8_094tok/sQ8_0
GLM 4.7 Flash15tok/sQ8_051tok/sQ8_0
Devstral Small 2 24B6tok/sQ8_020tok/sQ8_0
Qwen3 Next 80B-A3Bwon't run134tok/sQ4_K_M
Nemotron 3.5 Lightning 30B-A3B30tok/sUD-Q6_K_XL100tok/sUD-Q6_K_XL
Kimi Linear 48B-A3B44tok/sQ5_K_M129tok/sQ6_K
gpt-oss 20B33tok/sMXFP4110tok/sMXFP4
Qwen3 Coder 30B-A3B17tok/sQ8_056tok/sQ8_0
Qwen3 32B5tok/sQ6_K14tok/sQ8_0
Gemma 4 12B13tok/sQ8_044tok/sQ8_0
Qwen 3.5 9B17tok/sQ8_056tok/sQ8_0
Mistral Small 3.2 24B6tok/sQ8_020tok/sQ8_0
Gemma 3 27B6tok/sQ8_019tok/sQ8_0
DeepSeek R1 Distill Qwen 32B5tok/sQ6_K14tok/sQ8_0
Ministral 3 14B9tok/sQ8_030tok/sQ8_0
Qwen3 14B8tok/sQ8_028tok/sQ8_0
DeepSeek R1 0528 Qwen3 8B13tok/sQ8_044tok/sQ8_0
Qwen3 8B13tok/sQ8_044tok/sQ8_0
Phi-4 14B8tok/sQ8_027tok/sQ8_0
Qwen 3.5 4B32tok/sQ8_0106tok/sQ8_0
Ministral 3 8B13tok/sQ8_044tok/sQ8_0
Granite 4.2 8B12tok/sQ8_041tok/sQ8_0
Gemma 4 E4B19tok/sQ8_065tok/sQ8_0
LFM2.5 8B-A1B40tok/sQ8_0134tok/sQ8_0
Llama 3.1 8B16tok/sQ6_K55tok/sQ6_K
Qwen2.5 0.5B190tok/sQ8_0637tok/sQ8_0
Qwen3.5 0.8B144tok/sQ8_0483tok/sQ8_0
Qwen3.5 2B96tok/sQ5_K_M321tok/sQ5_K_M
DeepSeek R1 Distill Qwen 1.5B63tok/sQ8_0210tok/sQ8_0
MiniCPM5 2B43tok/sQ8_0146tok/sQ8_0
Spark X2.5 4B41tok/smlx-4bit138tok/smlx-4bit
LFM2.5 2.6B36tok/sQ8_0122tok/sQ8_0
LFM2.5 VL 3B36tok/sQ8_0122tok/sQ8_0
Mellum2.1 12B A2.5B Thinking34tok/sQ8_0113tok/sQ8_0
FrogNano 4B 260931tok/sQ8_0104tok/sQ8_0
granite vision 4.1 4b28tok/sQ8_094tok/sQ8_0
granite 4.2 3b27tok/sQ8_091tok/sQ8_0
Kimi VL A3B Thinking 250626tok/sQ8_087tok/sQ8_0
Kimi VL A3B25tok/smlx-8bit84tok/smlx-8bit
Ministral 3 3B Instruct 251225tok/sQ8_083tok/sQ8_0
LFM2.5 24B A2B22tok/sQ8_074tok/sQ8_0
GLM 4 9B 041419tok/smlx-5bit62tok/smlx-5bit
DeepSeek R1 Distill Qwen 7B18tok/sQ8_060tok/sQ8_0
GLM 4.6V Flash16tok/sQ8_053tok/sQ8_0
DeepSeek R1 Distill Llama 8B14tok/sQ8_046tok/sQ8_0
GLM 4.1V 9B Thinking14tok/smlx-8bit45tok/smlx-8bit
Ministral 3 8B Reasoning 251213tok/sQ8_044tok/sQ8_0
Ministral 3 14B Reasoning 25129tok/sQ8_030tok/sQ8_0
DeepSeek R1 Distill Qwen 14B8tok/sQ8_027tok/sQ8_0
Qwen3.8 27B OBLITERATED8tok/smlx-4bit26tok/smlx-4bit
Devstral Small 25076tok/sQ8_020tok/sQ8_0
Magistral Small 25066tok/sQ8_020tok/sQ8_0
Magistral Small 25076tok/sQ8_020tok/sQ8_0
Magistral Small 25096tok/sQ8_020tok/sQ8_0
Muse Glimmer 30B5tok/sQ8_018tok/sQ8_0
granite 4.2 30b5tok/sQ6_K15tok/sQ8_0
Nemotron 3 Super 120B-A12Bwon't runoffload
gpt-oss 120Bwon't runoffload
GLM 4.5 Airwon't runoffload
Llama 3.3 70Bwon't runoffload
DeepSeek R1 Distill Llama 70Bwon't runoffload
GLM 4.5Vwon't runoffload
GLM 4.6Vwon't runoffload
Kimi Dev 72Bwon't runoffload
DeepSeek V4 Flashwon't runwon't run
GLM 5.3 Flashwon't runwon't run
Qwen 3.8 Flash Nextwon't runwon't run
MiniMax M2.7won't runwon't run
DeepSeek R1won't runwon't run
DeepSeek R1 0528won't runwon't run
DeepSeek R1 Zerowon't runwon't run
DeepSeek V3won't runwon't run
DeepSeek V3 0324won't runwon't run
DeepSeek V3.1won't runwon't run
DeepSeek V3.1 Terminuswon't runwon't run
DeepSeek V3.2won't runwon't run
DeepSeek V4 Flash 0731won't runwon't run
DeepSeek V4 Flash Vision Expwon't runwon't run
Devstral 2 123B Instruct 2512won't runwon't run
GLM 4.5won't runwon't run
GLM 4.6won't runwon't run
GLM 4.7won't runwon't run
GLM 5won't runwon't run
GLM 5.1won't runwon't run
GLM 5.2won't runwon't run
GLM 5.3won't runwon't run
Kimi K2won't runwon't run
Kimi K2 Instruct 0905won't runwon't run
Kimi K2 Thinkingwon't runwon't run
Kimi K2.5won't runwon't run
MiniMax M2won't runwon't run
MiniMax M2.1won't runwon't run
MiniMax M2.5won't runwon't run
MiniMax M3won't runwon't run
Mistral Medium 3.5 128Bwon't runwon't run

Questions

Which is better for local AI, the Mac mini M4 Pro 48GB or the NVIDIA RTX 6000 Ada Generation?
Choose the NVIDIA RTX 6000 Ada Generation to run bigger models. Choose the NVIDIA RTX 6000 Ada Generation for speed.
Which can run bigger models, the Mac mini M4 Pro 48GB or the NVIDIA RTX 6000 Ada Generation?
At 32k context the NVIDIA RTX 6000 Ada Generation runs 65 of the models we track fully in memory and the Mac mini M4 Pro 48GB 61; only the NVIDIA RTX 6000 Ada Generation holds Qwen 3.5 122B-A10B.
Which is faster for local AI, the Mac mini M4 Pro 48GB or the NVIDIA RTX 6000 Ada Generation?
On the models both run fully in memory, the NVIDIA RTX 6000 Ada Generation is about 3.4× as fast as the Mac mini M4 Pro 48GB (estimated, median across models).

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