Model guide
What GPU do you need for Gemma 2 9B?
In short
- Gemma 2 9B IT needs about 6.2 GB at Q4_K_M (weights plus runtime overhead; KV cache is extra).
- Smallest card that fits: RTX 3060 12GB (12 GB).
- 12 of 12 cards in our set hold it at this quantization.
About 8.9 GB at Q4_K_M, so a 12 GB card is the practical floor — noticeably hungrier than other models near its size.
Architecture at a glance
Parameters
9.24B
Layers
42
KV heads
8
Head dim
256
Max context
8K
KV @8K
2.6 GB
Two Gemma-specific things drive that. First, the head dimension is 256, double the usual 128, which doubles KV cache per token; across 42 layers the 8K cache is 2.6 GB, larger than Llama 8B's despite the similar parameter count. Second, Gemma 2's context caps at 8K by design, so there is no 128K scenario to budget for — the number above is close to the worst case.
VRAM by quantization (8K context)
| Quant | Total VRAM | Cheapest GPU that fits |
|---|---|---|
| Q4_K_M | 8.9 GB | RTX 3060 12GB |
| Q5_K_M | 9.8 GB | RTX 3060 12GB |
| Q6_K | 10.8 GB | RTX 3060 12GB |
| Q8_0 | 13.0 GB | RTX 4060 Ti 16GB |
| FP16 | 21.4 GB | RTX 3090 |
Weights and KV cache are exact arithmetic from the model's published config; overhead (0.75 GB + 5% of weights) is a calibrated estimate. "Fits" means at most 95% of the card. Method on the Engineering Center.
Compatible GPUs at Q4_K_M
Green fits comfortably, amber is tight, faded doesn't fit — each links to that card's full page.
Can an RTX 4090 run Gemma 2 9B?
Yes. At Q4_K_M with an 8K context, Gemma 2 9B needs about 8.9 GB, leaving 15.1 GB of headroom on the 4090's 24 GB.
What about maximum context?
This model's context caps at 8K tokens, where the total is about 8.9 GB — its worst case is close to its everyday case. The KV cache is the part that grows — the weights never change. To see the exact split at any context, run this model through the GPU memory calculator, check an Ollama tag in the Ollama calculator, or size a fine-tune in the training memory calculator.
Running it in the cloud
For rented hardware the sensible floor is the A100 40GB — the smallest datacenter card that holds this model comfortably at Q4_K_M. Marketplace clouds also rent consumer cards; anything from the RTX 3060 12GB up works for this model and usually costs less per hour. Hourly prices move weekly, so we don't print them here — the workspace carries the current figures and weighs rental against electricity for this exact model.
Related models
27.2B params · 19.8 GB at Q4_K_M
Same family — the natural size step.
See requirements →8.03B params · 6.5 GB at Q4_K_M
Closest size in another family.
See requirements →7.62B params · 5.7 GB at Q4_K_M
Closest size in another family.
See requirements →Exact memory figures for every quantization: How much VRAM does Gemma 2 9B IT need?
Common questions
What is the minimum GPU for Gemma 2 9B?
At the default Q4_K_M quantization with an 8K context, Gemma 2 9B needs about 8.9 GB of VRAM, so the practical minimum is a RTX 3060 12GB. Weights and KV cache are exact arithmetic from the model's config; a small runtime overhead estimate is included.
How much VRAM does Gemma 2 9B need at maximum context?
This model's context caps at 8K tokens, where the total is about 8.9 GB — its worst case is close to its everyday case.
Can an RTX 4090 run Gemma 2 9B?
Yes. At Q4_K_M with an 8K context, Gemma 2 9B needs about 8.9 GB, leaving 15.1 GB of headroom on the 4090's 24 GB.
Before you buy
Compare the shortlisted cards head to head in GPU Compare, size the exact context you need in the GPU Memory Calculator, and read which quantization to run before committing to a card — dropping one format down often removes the need for the next tier up.