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Gemma 3 12B IT on the RTX 4090

With 24 GB of VRAM, the RTX 4090 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 23.3 GB available, with 14.2 GB to spare.

Your hardware
RTX 4090 24 GB · 64 GB RAM
Estimated memory
~9.1 GB estimate
VRAM (usable memory)
23.3 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
78–115 tok/s estimate
Largest context in fast memory
128K tokens
Confidence: low

Based on: memory bandwidth (1,008 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 14.2 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 4090

Gemma 3 12B IT on RTX 4090 + 64 GB RAM · 8K context
Gemma 3 12B IT Q8_08K~14.1 GB · 51–72 tok/s estimateExcellent
Gemma 3 12B IT Q6_K8K~11.4 GB · 63–90 tok/s estimateExcellent
Gemma 3 12B IT Q5_K_M8K~10.2 GB · 70–101 tok/s estimateExcellent
Gemma 3 12B IT Q4_K_M8K~9.1 GB · 78–115 tok/s estimateExcellent
Gemma 3 12B IT IQ4_XS8K~8.3 GB · 84–125 tok/s estimateExcellent
Gemma 3 12B IT Q3_K_M8K~7.8 GB · 90–135 tok/s estimateExcellent

Alternatives

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

Other models that fit the RTX 4090

Where to buy a RTX 4090

Prices and links follow the market you choose.

GeForce RTX 4090

24 GB GDDR6X · 1,008 GB/s · 450 W

ExcellentRuns Gemma 3 12B IT Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

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Frequently asked questions

Can the RTX 4090 run Gemma 3 12B IT?
The model needs about 9.1 GB and fits in the 23.3 GB available, with 14.2 GB to spare.
Which quantization should I use on the RTX 4090?
Q8_0 is the highest-precision quantization that fits entirely in VRAM at 8K context. ~14.1 GB / 23.3 GB.
What is the largest context on the RTX 4090?
At Q4_K_M, about 128K 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 4090)?
We estimate 78–115 tok/s (estimate, confidence low). Based on: memory bandwidth (1,008 GB/s) and model size.