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Gemma 3 12B IT on the RTX 3080

With 10 GB of VRAM, the RTX 3080 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 does not fit in VRAM.

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Gemma 3 12B IT

Q4_K_M · 8K context

Compatible

The model needs about 9.1 GB and fits in the 9.7 GB available (0.6 GB left).

Your hardware
RTX 3080 10 GB · 64 GB RAM
Estimated memory
~9.1 GB estimate
VRAM (usable memory)
9.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
62–89 tok/s estimate
Largest context in fast memory
17K tokens
Confidence: low

Based on: memory bandwidth (760 GB/s) and model size

Memory breakdown

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

Fits entirely in VRAM · 0.6 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 3080

Gemma 3 12B IT on RTX 3080 + 64 GB RAM · 8K context
Gemma 3 12B IT Q8_08K~14.1 GB · 4.5–8.5 tok/s estimateCPU/GPU offload
Gemma 3 12B IT Q6_K8K~11.4 GB · 10–20 tok/s estimateCPU/GPU offload
Gemma 3 12B IT Q5_K_M8K~10.2 GB · 22–42 tok/s estimateCPU/GPU offload
Gemma 3 12B IT Q4_K_M8K~9.1 GB · 62–89 tok/s estimateCompatible
Gemma 3 12B IT IQ4_XS8K~8.3 GB · 68–97 tok/s estimateCompatible
Gemma 3 12B IT Q3_K_M8K~7.8 GB · 72–105 tok/s estimateExcellent

Alternatives

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

Other models that fit the RTX 3080

Where to buy a RTX 3080

Prices and links follow the market you choose.

GeForce RTX 3080 (10 GB)

10 GB GDDR6X · 760 GB/s · 320 W

CompatibleRuns Gemma 3 12B IT Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

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Frequently asked questions

Can the RTX 3080 run Gemma 3 12B IT?
The model needs about 9.1 GB and fits in the 9.7 GB available (0.6 GB left).
Which quantization should I use on the RTX 3080?
Q4_K_M is the highest-precision quantization that fits entirely in VRAM at 8K context. ~9.1 GB / 9.7 GB.
What is the largest context on the RTX 3080?
At Q4_K_M, about 17K 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 3080)?
We estimate 62–89 tok/s (estimate, confidence low). Based on: memory bandwidth (760 GB/s) and model size.