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Run LLM Locally

Gemma 3 12B IT on the RTX 4080 SUPER

With 16 GB of VRAM, the RTX 4080 SUPER runs Gemma 3 12B IT at Q4_K_M entirely on the GPU with an 8K context (about 9.1 GB needed). At Q8_0 it fits entirely.

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Gemma 3 12B IT

Q4_K_M · 8K context

Excellent

The model needs about 9.1 GB and fits in the 15.5 GB available, with 6.5 GB to spare.

Your hardware
RTX 4080 SUPER 16 GB · 64 GB RAM
Estimated memory
~9.1 GB estimate
VRAM (usable memory)
15.5 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
61–86 tok/s estimate
Largest context in fast memory
106K tokens
Confidence: low

Based on: memory bandwidth (736 GB/s) and model size

Memory breakdown

~9.1 GB (8.5 GB–9.6 GB) needed · estimate

Fits entirely in VRAM · 6.5 GB left

  • Model weights6.8 GB
  • KV cache0.8 GB
  • Runtime overhead1 GB
  • Safety margin0.4 GB
Assumptions: context 8K tokens · KV cache f16 · llama.cpp (CUDA) · flash attention on · weights = real file size · sliding-window layers keep 1,024 tokens

EstimateData updated 24 days agoSource: Run LLM Locally engine, Hugging Face config.json + model card

What makes this estimate less reliable

  • no benchmark for this hardware and model yet

Other quantizations on the RTX 4080 SUPER

Gemma 3 12B IT on RTX 4080 SUPER + 64 GB RAM · 8K context
Gemma 3 12B IT Q8_08K~14.1 GB · 39–53 tok/s estimateCompatible
Gemma 3 12B IT Q6_K8K~11.4 GB · 48–67 tok/s estimateExcellent
Gemma 3 12B IT Q5_K_M8K~10.2 GB · 54–76 tok/s estimateExcellent
Gemma 3 12B IT Q4_K_M8K~9.1 GB · 61–86 tok/s estimateExcellent
Gemma 3 12B IT IQ4_XS8K~8.3 GB · 66–95 tok/s estimateExcellent
Gemma 3 12B IT Q3_K_M8K~7.8 GB · 70–102 tok/s estimateExcellent

Alternatives

GPUs that run Gemma 3 12B IT Q4_K_M entirely in VRAM

Other models that fit the RTX 4080 SUPER

Where to buy a RTX 4080 SUPER

Prices and links follow the market you choose.

GeForce RTX 4080 SUPER

16 GB GDDR6X · 736 GB/s · 320 W

ExcellentRuns Gemma 3 12B IT Q4_K_M entirely in memory

Price not available

Availability unknown

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Frequently asked questions

Can the RTX 4080 SUPER run Gemma 3 12B IT?
The model needs about 9.1 GB and fits in the 15.5 GB available, with 6.5 GB to spare.
Which quantization should I use on the RTX 4080 SUPER?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~14.1 GB / 15.5 GB.
What is the largest context on the RTX 4080 SUPER?
At Q4_K_M, about 106K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Gemma 3 12B IT (RTX 4080 SUPER)?
We estimate 61–86 tok/s (estimate, confidence low). Based on: memory bandwidth (736 GB/s) and model size.