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Run LLM Locally

Gemma 2 9B IT on the RTX 3070

Gemma 2 9B IT at Q4_K_M needs about 8.7 GB, more than the 8 GB of the RTX 3070: part of the model runs from system RAM, which slows generation. At Q3_K_M it does not fit in VRAM.

Gemma 2 9B IT

Q4_K_M · 8K context

CPU/GPU offload

The model needs about 8.7 GB but only 7.7 GB of VRAM is usable: 1.1 GB must be loaded into system RAM. It runs, 87% on the GPU, but more slowly.

Your hardware
RTX 3070 8 GB · 64 GB RAM
Estimated memory
~8.7 GB estimate
VRAM (usable memory)
7.7 GB
GPU only
No
CPU/GPU offload
Required (1.1 GB)
Generation speed
15–27 tok/s estimate
Largest context in fast memory
2K tokens
Confidence: low

Based on: memory bandwidth (448 GB/s) and model size · system RAM bandwidth (~51 GB/s)

Memory breakdown

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

1.1 GB must be offloaded to system RAM

  • 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
  • split CPU/GPU runs vary a lot with the CPU and PCIe link
  • system RAM speed unknown (dual-channel DDR4-3200 assumed)

Other quantizations on the RTX 3070

Gemma 2 9B IT on RTX 3070 + 64 GB RAM · 8K context
Gemma 2 9B IT Q8_08K~12.7 GB · 4–8 tok/s estimateCPU/GPU offload
Gemma 2 9B IT Q6_K8K~10.5 GB · 7–13 tok/s estimateCPU/GPU offload
Gemma 2 9B IT Q5_K_M8K~9.6 GB · 9.5–18 tok/s estimateCPU/GPU offload
Gemma 2 9B IT Q4_K_M8K~8.7 GB · 15–27 tok/s estimateCPU/GPU offload
Gemma 2 9B IT IQ4_XS8K~8.2 GB · 22–41 tok/s estimateCPU/GPU offload
Gemma 2 9B IT Q3_K_M8K~7.8 GB · 35–62 tok/s estimateCPU/GPU offload

Alternatives

Hardware that runs Gemma 2 9B IT Q4_K_M entirely in memory

Prices and links follow the market you choose.

GeForce RTX 3080 (10 GB)

NVIDIA

CompatibleRuns Gemma 2 9B IT Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

Radeon RX 7600 XT

AMD

ExcellentRuns Gemma 2 9B IT Q4_K_M entirely in memory

Price not available

Availability unknown

View on amazon.com

Affiliate link

AMD Ryzen AI Max+ 395

Unified memory PC

ExcellentRuns 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 3070 run Gemma 2 9B IT?
The model needs about 8.7 GB but only 7.7 GB of VRAM is usable: 1.1 GB must be loaded into system RAM. It runs, 87% on the GPU, but more slowly.
Which quantization should I use on the RTX 3070?
None fits entirely in VRAM at 8K context; the smallest, Q3_K_M, still needs offload.
What is the largest context on the RTX 3070?
At Q4_K_M, about 2K 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 3070)?
We estimate 15–27 tok/s (estimate, confidence low). Based on: memory bandwidth (448 GB/s) and model size, system RAM bandwidth (~51 GB/s).