Google DeepMind FP16 9B Parameters

VRAM Requirements for
Gemma 2 9B FP16

To run Gemma 2 9B locally at FP16 quantization, you need at minimum 20.6 GB of GPU VRAM.

20.6 GB
Required VRAM
18.49 GB
File Size
8K tokens
Context Window
9B
Parameters
Estimated VRAM Required
20.6
GB
High-End Consumer GPU
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $600 – $900

Used RTX 3090 (24GB)

24 GB

Best used-market value for 24GB VRAM. Solid for 30B-class models.

Balanced $1,599 – $1,999

RTX 4090 (24GB)

24 GB

Fastest 24GB consumer GPU. Excellent for daily local inference.

Ultimate $2,500 – $3,500

NVIDIA A5000 (32GB)

32 GB

Pro workstation card with ECC memory. Maximum headroom at 24GB.

📊 VRAM Calculation Breakdown

Model File Size (FP16) 18.49 GB
Context Overhead (8,192 tokens × 9B × 2 ÷ 1M) 0.147 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 20.6 GB

Try a Different Quantization

Use the interactive calculator to compare Gemma 2 9B across all available formats.

Open Live Calculator →

Gemma 2 9B — Other Quantizations

Q4_K_M 5.44 GB file
7.6 GB
Q6_K 7.06 GB file
9.2 GB
Q8_0 9.83 GB file
12 GB
FP16 Current
20.6 GB
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Frequently Asked Questions

Can I run Gemma 2 9B FP16 on a consumer GPU?
Yes! At 20.6 GB VRAM required, a single high-end consumer GPU like the RTX 4090 (24GB) can handle this workload. You can also use multiple GPUs for tensor parallelism.
What happens if I don't have enough VRAM?
If your GPU VRAM is insufficient, llama.cpp and similar tools will offload model layers to system RAM (CPU inference). This is much slower — expect 10-50× the generation latency compared to full GPU inference.
Can I use multiple GPUs to run Gemma 2 9B?
Yes! Tools like llama.cpp, vLLM, and Ollama support tensor parallelism across multiple GPUs. For example, 2× RTX 3090 (24GB each) gives you 48GB total VRAM, which can run many large models.
Is FP16 quality good enough for production?
FP16/BF16 is the standard precision used for production inference and serves as the quality baseline. All fine-tuned models are typically served at this precision.