Memory guide · 12GB VRAM

What can you run on 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.

Models that fit in 12GB VRAM

ModelQuantMemory neededEst. speed, 300–900 GB/s card
Qwen3.5 9Bdense · 9B active · chat, small agentsQ4_K_M~7 GB~44–120 tok/s
Gemma 4 12Bdense · 12B active · chat, visionQ4_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).

Want more than 12GB?

We don't track any 12GB cards yet. These are the cheapest tracked rigs with more memory.

FAQ

What's the biggest model I can run on 12GB VRAM?

Of 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).

Can I run Llama 3.3 70B on 12GB VRAM?

Not fully in memory. At 4-bit it needs about 46 GB including runtime and ~8K context.

How are fit and speed estimated?

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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