Can I Run Google Gemma 2 9B Instruct on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
Full VRAM footprint analysis, tokens/sec generation benchmark, and copy-paste Ollama & vLLM runtime commands for running Google Gemma 2 9B Instruct (9.2B) on NVIDIA GeForce RTX 3090 (24GB GDDR6X) (24 GB).
✓ Fits Comfortably in VRAM
Local Hardware Configurations & Pricing
NVIDIA GeForce RTX 5090 (32GB GDDR7)
Maximum tokens/sec on 27B–35B models & Blackwell FP4 quantization
NVIDIA GeForce RTX 4090 (24GB GDDR6X)
The 24GB CUDA enthusiast standard for 14B–32B Q4_K_M models
2x NVIDIA RTX 3090 Dual Rig (48GB Total VRAM)
Best price-to-VRAM homelab rig for Llama 3.3 70B Q4_K_M & Qwen 2.5 Coder 32B Q8_0
NVIDIA GeForce RTX 3090 (24GB GDDR6X)
The #1 budget 24GB card for local LLMs, running 14B–32B models and Q4 70B quantized
Cloud Instance Options for Google Gemma 2 9B Instruct
On-DemandDeploy this model on an on-demand cloud GPU without local hardware constraints or power usage:
Quantization Precision Matrix
32K ctx • KV: Q8_0| Quantization | Weights | KV Cache | Total VRAM | Est. Speed | Fit Status |
|---|---|---|---|---|---|
| FP1616 bpw | 18.4 GB | 5.64 GB | 25.43 GB (106%) | ~7 t/s | PARTIAL |
| Q8_08.5 bpw | 9.75 GB | 5.64 GB | 16.48 GB (69%) | ~57 t/s | FITS |
| Q6_K6.56 bpw | 7.54 GB | 5.64 GB | 14.19 GB (59%) | ~71 t/s | FITS |
| Q5_K_M5.68 bpw | 6.53 GB | 5.64 GB | 13.15 GB (55%) | ~81 t/s | FITS |
| Q4_K_M4.8 bpw | 5.52 GB | 5.64 GB | 12.1 GB (50%) | ~93 t/s | FITS |
| FP44.25 bpw | 4.88 GB | 5.64 GB | 11.44 GB (48%) | ~103 t/s | FITS |
| IQ3_XS3.3 bpw | 3.77 GB | 5.64 GB | 10.29 GB (43%) | ~128 t/s | FITS |
Frequently Asked Questions: Google Gemma 2 9B Instruct on NVIDIA GeForce RTX 3090 (24GB GDDR6X)
How much VRAM does Google Gemma 2 9B Instruct need on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
At standard 4-bit quantization (Q4_K_M) with 32K context, Google Gemma 2 9B Instruct requires 7.87 GB of total VRAM (weights + KV cache + CUDA runtime overhead).
What inference speed will Google Gemma 2 9B Instruct achieve on NVIDIA GeForce RTX 3090 (24GB GDDR6X)?
Based on NVIDIA GeForce RTX 3090 (24GB GDDR6X)'s 936 GB/s memory bandwidth, expected decode speed is approximately 104 tokens per second.
Can I expand context beyond 32K tokens?
Each additional 32K tokens of context requires additional KV cache memory. For longer context windows, consider 8-bit or 4-bit KV cache quantization.