Mac mini M5 Pro
15-core CPU / 16-core GPU, 24GB
24 GB · 307 GB/s
Check priceMemory guide · 12GB VRAM
2 of the 16 open models we track fit in 12GB VRAM without spilling into system RAM, the biggest being Gemma 4 12B. Speed depends on your card's memory bandwidth, so we show a range for 300–900 GB/s cards. Prices & stock checked 2026-10-23. Prices change daily and some retailers block our checker, so always confirm on the store page.
| Model | Quant | Memory needed | Est. speed, 300–900 GB/s card |
|---|---|---|---|
| Qwen3.5 9Bdense · 9B active · chat, small agents | Q4_K_M | ~7 GB | ~44–120 tok/s |
| Gemma 4 12Bdense · 12B active · chat, vision | Q4_K_M | ~9 GB | ~33–93 tok/s |
We don't track any 12GB cards, so speed is shown for a typical 300–900 GB/s NVIDIA card (slower cards at the low end, faster ones at the high end). Fit assumes ~93% of VRAM is usable. How we estimate
Just out of reach: GPT-OSS 20B (~13 GB at 4-bit), Gemma 4 26B-A4B (~18 GB at 4-bit), Qwen3.8 27B (~19 GB at 4-bit).
We don't track any 12GB cards yet. These are the cheapest tracked rigs with more memory.
15-core CPU / 16-core GPU, 24GB
24 GB · 307 GB/s
Check priceRyzen AI Max+ 395, 64GB / 1TB
64 GB · 256 GB/s
Check price18-core CPU / 32-core GPU, 36GB
36 GB · 460 GB/s
Check priceOf the models we track, Gemma 4 12B is the largest that fits fully in 12GB VRAM (Q4_K_M, about 9 GB including runtime and ~8K context).
Not fully in memory. At 4-bit it needs about 46 GB including runtime and ~8K context.
Memory needed = weights + 1.5 GB + 5% of weights. Decode speed ≈ 1 ÷ (active weights in GB ÷ (efficiency × memory bandwidth) + a small fixed per-token overhead), with efficiency and overhead fitted per platform to 43 public llama.cpp/MLX benchmarks (median error about 5%). They're estimates, not benchmarks.
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