Fedric95 FP32 9B Parameters

VRAM Requirements for
gemma-2-9b-GGUF FP32

To run gemma-2-9b-GGUF locally at FP32 quantization, you need at minimum 38.15 GB of GPU VRAM.

38.15 GB
Required VRAM
36 GB
File Size
8K tokens
Context Window
9B
Parameters
Estimated VRAM Required
38.15
GB
Prosumer / Workstation
0 16GB
RTX 3080 48GB
A6000 80GB+

Recommended GPU Configurations

Budget $1,200 – $1,800

2× RTX 3090 (48GB total)

48 GB

Dual-GPU via tensor parallelism. Best cost per GB at this tier.

Balanced $4,000 – $5,500

NVIDIA A6000 (48GB)

48 GB

Single-card 48GB pro GPU. Clean setup, no multi-GPU overhead.

Ultimate $8,000 – $12,000

NVIDIA A100 40GB SXM

40 GB HBM2e

Data-centre HBM2e bandwidth. Dramatically faster throughput.

📊 VRAM Calculation Breakdown

Model File Size (FP32) 36 GB
Context Overhead (8,192 tokens × 9B × 2 ÷ 1M) 0.147 GB
System Buffer (OS + CUDA runtime) 2.00 GB
Total Required VRAM 38.15 GB

Try a Different Quantization

Use the interactive calculator to compare gemma-2-9b-GGUF across all available formats.

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gemma-2-9b-GGUF — Other Quantizations

Q2_K 2.81 GB file
4.96 GB
Q3_K_L 3.94 GB file
6.09 GB
Q3_K_M 3.94 GB file
6.09 GB
Q3_K_S 3.94 GB file
6.09 GB
Q4_K_M 5.06 GB file
7.21 GB
Q4_K_S 5.06 GB file
7.21 GB
Q5_K_M 6.19 GB file
8.34 GB
Q5_K_S 6.19 GB file
8.34 GB
Q6_K 7.31 GB file
9.46 GB
Q8_0 9.56 GB file
11.71 GB
FP32 Current
38.15 GB
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Frequently Asked Questions

Can I run gemma-2-9b-GGUF FP32 on a consumer GPU?
Running gemma-2-9b-GGUF FP32 locally requires 38.15 GB VRAM, which exceeds consumer GPUs. You'll need prosumer cards like the NVIDIA A6000 (48GB) or an A100 (80GB).
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-GGUF?
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 FP32 quality good enough for production?
FP32 is suitable for specialized use cases. Check community benchmarks for specific quality metrics.