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Gemma 2 9B IT on the RTX 3080

With 10 GB of VRAM, the RTX 3080 runs Gemma 2 9B IT at Q4_K_M entirely on the GPU with an 8K context (about 8.7 GB needed). At Q8_0 it does not fit in VRAM.

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Gemma 2 9B IT

Q4_K_M · 8K context

Compatible

The model needs about 8.7 GB and fits in the 9.7 GB available (0.9 GB left).

Your hardware
RTX 3080 10 GB · 64 GB RAM
Estimated memory
~8.7 GB estimate
VRAM (usable memory)
9.7 GB
GPU only
Yes
CPU/GPU offload
Not required
Generation speed
71–101 tok/s estimate
Largest context in fast memory
8K tokens
Confidence: low

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

Memory breakdown

~8.7 GB (8.2 GB–9.3 GB) needed · estimate

Fits entirely in VRAM · 0.9 GB left

  • Model weights5.4 GB
  • KV cache2 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 4,096 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 2 9B IT on RTX 3080 + 64 GB RAM · 8K context
Gemma 2 9B IT Q8_08K~12.7 GB · 6.5–13 tok/s estimateCPU/GPU offload
Gemma 2 9B IT Q6_K8K~10.5 GB · 18–33 tok/s estimateCPU/GPU offload
Gemma 2 9B IT Q5_K_M8K~9.6 GB · 64–90 tok/s estimateCompatible with limits
Gemma 2 9B IT Q4_K_M8K~8.7 GB · 71–101 tok/s estimateCompatible
Gemma 2 9B IT IQ4_XS8K~8.2 GB · 76–109 tok/s estimateExcellent
Gemma 2 9B IT Q3_K_M8K~7.8 GB · 80–116 tok/s estimateExcellent

Alternatives

GPUs that run Gemma 2 9B 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 2 9B IT Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

No current offer imported for this market: the link opens a marketplace search.

Frequently asked questions

Can the RTX 3080 run Gemma 2 9B IT?
The model needs about 8.7 GB and fits in the 9.7 GB available (0.9 GB left).
Which quantization should I use on the RTX 3080?
Q5_K_M is the highest-precision quantization that fits entirely in VRAM at 8K context. ~9.6 GB / 9.7 GB.
What is the largest context on the RTX 3080?
At Q4_K_M, about 8K tokens fit entirely in VRAM with an f16 KV cache. A q8_0 KV cache roughly doubles that.
How fast is Gemma 2 9B IT (RTX 3080)?
We estimate 71–101 tok/s (estimate, confidence low). Based on: memory bandwidth (760 GB/s) and model size.