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Mac mini M4 16GB vs NVIDIA GeForce RTX 4080 Super

For running models locally, priced in Germany. At 32k context the first runs 22 models fully in memory, the second 30.

In short

Both have 16 GB of memory for models; the Mac mini M4 16GB has 120 GB/s of bandwidth and the NVIDIA GeForce RTX 4080 Super 736 GB/s. At 32k context the NVIDIA GeForce RTX 4080 Super runs 30 of the models we track fully in memory and the Mac mini M4 16GB 22; only the NVIDIA GeForce RTX 4080 Super holds gpt-oss 20B. On the models both run fully in memory, the NVIDIA GeForce RTX 4080 Super is about 5.8× as fast as the Mac mini M4 16GB (estimated, median across models). In Germany the Mac mini M4 16GB costs 949 € at Amazon and the NVIDIA GeForce RTX 4080 Super 1.262 € at Amazon: the Mac mini M4 16GB is 313 € cheaper.

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

Mac mini M4 16GB

949 €

Amazon

Add to build
NVIDIA GeForce RTX 4080 Super

1.262 €

Amazon

Add to build

Specifications

SpecMac mini M4 16GBNVIDIA GeForce RTX 4080 Super
ChipApple M4 (10-core GPU)–
Memory bus128-bit256-bit
Memory bandwidth120 GB/s736 GB/s
FP16 compute3.76 TFLOPS104 TFLOPS
SoftwareMetalCUDA
Unified memory16 GB–
GPU can use by default10.7 GB–
GPU can use, tuned10.666666666666666 GB–
Operating systemmacos–
ProcessorM4 10-core–
Storage256 GB–
GPU–GeForce RTX 4080 Super
Memory–16 GB GDDR6X
Board power–320 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 16GBNVIDIA GeForce RTX 4080 Super
gpt-oss 20Bwon't run85tok/sMXFP4
Gemma 4 12B8tok/sQ5_K_M34tok/sQ8_0
Qwen 3.5 9B9tok/sQ6_K43tok/sQ8_0
Ministral 3 14Bwon't run30tok/sQ5_K_M
Qwen3 14Bwon't run32tok/sQ4_K_M
DeepSeek R1 0528 Qwen3 8B8tok/sQ4_K_M34tok/sQ8_0
Qwen3 8B8tok/sQ4_K_M34tok/sQ8_0
Qwen 3.5 4B14tok/sQ8_081tok/sQ8_0
Ministral 3 8B7tok/sQ5_K_M34tok/sQ8_0
Granite 4.2 8Bwon't run31tok/sQ8_0
Gemma 4 E4B9tok/sQ8_050tok/sQ8_0
LFM2.5 8B-A1B18tok/sQ8_0102tok/sQ8_0
Llama 3.1 8B8tok/sQ5_K_M42tok/sQ6_K
Qwen2.5 0.5B84tok/sQ8_0489tok/sQ8_0
Qwen3.5 0.8B63tok/sQ8_0370tok/sQ8_0
Qwen3.5 2B42tok/sQ5_K_M246tok/sQ5_K_M
DeepSeek R1 Distill Qwen 1.5B28tok/sQ8_0161tok/sQ8_0
MiniCPM5 2B19tok/sQ8_0112tok/sQ8_0
LFM2.5 2.6B16tok/sQ8_093tok/sQ8_0
LFM2.5 VL 3B16tok/sQ8_093tok/sQ8_0
granite vision 4.1 4b12tok/sQ8_072tok/sQ8_0
granite 4.2 3b12tok/sQ8_069tok/sQ8_0
GLM 4.6V Flash10tok/sQ4_K_M40tok/sQ8_0
DeepSeek R1 Distill Llama 8B8tok/sQ5_K_M36tok/sQ8_0
DeepSeek R1 Distill Qwen 7B8tok/sQ8_046tok/sQ8_0
Ministral 3 8B Reasoning 25127tok/sQ5_K_M34tok/sQ8_0
DeepSeek R1 Distill Qwen 14Bwon't run30tok/sQ4_K_M
GLM 4.1V 9B Thinkingwon't run35tok/smlx-8bit
Kimi VL A3Bwon't run77tok/smlx-6bit
Mellum2.1 12B A2.5B Thinkingwon't run86tok/sQ8_0
Qwen 3.8 27Bwon't runoffload
Qwen 3.5 122B-A10Bwon't runoffload
Qwen 3.6 27Bwon't runoffload
Gemma 4 31Bwon't runoffload
Mistral Small 4 119Bwon't runoffload
Qwen 3.5 27Bwon't runoffload
Qwen 3.6 35B-A3Bwon't runoffload
Gemma 4 26B-A4Bwon't runoffload
Devstral Small 2 24Bwon't runoffload
GLM 4.7 Flashwon't runoffload
Qwen 3.5 35B-A3Bwon't runoffload
Nemotron 3.5 Lightning 30B-A3Bwon't runoffload
Kimi Linear 48B-A3Bwon't runoffload
Qwen3 Coder 30B-A3Bwon't runoffload
Qwen3 32Bwon't runoffload
DeepSeek R1 Distill Qwen 32Bwon't runoffload
Gemma 3 27Bwon't runoffload
Mistral Small 3.2 24Bwon't runoffload
Phi-4 14Bwon't runoffload
Devstral Small 2507won't runoffload
LFM2.5 24B A2Bwon't runoffload
Magistral Small 2506won't runoffload
Magistral Small 2507won't runoffload
Magistral Small 2509won't runoffload
granite 4.2 30bwon'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
Nemotron 3 Super 120B-A12Bwon't runwon't run
Qwen3 Coder Nextwon't runwon't run
gpt-oss 120Bwon't runwon't run
GLM 4.5 Airwon't runwon't run
Qwen3 Next 80B-A3Bwon't runwon't run
Llama 3.3 70Bwon't runwon't run
DeepSeek R1won't runwon't run
DeepSeek R1 0528won't runwon't run
DeepSeek R1 Distill Llama 70Bwon't runwon't run
DeepSeek V3 0324won't runwon't run
DeepSeek V3.1won'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 4.5Vwon't runwon't run
GLM 4.6Vwon't runwon't run
GLM 4.7won'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 16GB or the NVIDIA GeForce RTX 4080 Super?
Choose the NVIDIA GeForce RTX 4080 Super to run bigger models. Choose the NVIDIA GeForce RTX 4080 Super for speed. Choose the Mac mini M4 16GB to spend less.
Which can run bigger models, the Mac mini M4 16GB or the NVIDIA GeForce RTX 4080 Super?
At 32k context the NVIDIA GeForce RTX 4080 Super runs 30 of the models we track fully in memory and the Mac mini M4 16GB 22; only the NVIDIA GeForce RTX 4080 Super holds gpt-oss 20B.
Which is faster for local AI, the Mac mini M4 16GB or the NVIDIA GeForce RTX 4080 Super?
On the models both run fully in memory, the NVIDIA GeForce RTX 4080 Super is about 5.8× as fast as the Mac mini M4 16GB (estimated, median across models).
What is the price difference between the Mac mini M4 16GB and the NVIDIA GeForce RTX 4080 Super?
In Germany the Mac mini M4 16GB costs 949 € at Amazon and the NVIDIA GeForce RTX 4080 Super 1.262 € at Amazon: the Mac mini M4 16GB is 313 € cheaper.

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