Model guide
What GPU do you need for Gemma 2 27B?
In short
- Gemma 2 27B IT needs about 16.9 GB at Q4_K_M (weights plus runtime overhead; KV cache is extra).
- Smallest card that fits: RTX 3090 (24 GB).
- 9 of 12 cards in our set hold it at this quantization.
You need about 19.8 GB at Q4_K_M, which fits a 24 GB card — an RTX 3090 or 4090 runs Gemma 2 27B locally.
Architecture at a glance
Parameters
27.2B
Layers
46
KV heads
16
Head dim
128
Max context
8K
KV @8K
2.9 GB
Gemma 2 27B keeps 16 KV heads, twice as many as most models this size use, so its cache compresses less. The saving grace is again the 8K context cap: the KV cache cannot grow past about 2.9 GB, so the 24 GB fit holds. At Q6_K the total rises to roughly 25.4 GB and no longer fits 24 GB — quantization choice decides this one.
VRAM by quantization (8K context)
| Quant | Total VRAM | Cheapest GPU that fits |
|---|---|---|
| Q4_K_M | 19.8 GB | RTX 3090 |
| Q5_K_M | 22.5 GB | RTX 3090 |
| Q6_K | 25.4 GB | RTX 5090 |
| Q8_0 | 31.9 GB | RTX A6000 |
| FP16 | 56.8 GB | A100 80GB |
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 27B?
Yes. At Q4_K_M with an 8K context, Gemma 2 27B needs about 19.8 GB, leaving 4.2 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 19.8 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 3090 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
9.24B params · 8.9 GB at Q4_K_M
Same family — the natural size step.
See requirements →32.8B params · 22.2 GB at Q4_K_M
Closest size in another family.
See requirements →32.8B params · 22.2 GB at Q4_K_M
Closest size in another family.
See requirements →Exact memory figures for every quantization: How much VRAM does Gemma 2 27B IT need?
Common questions
What is the minimum GPU for Gemma 2 27B?
At the default Q4_K_M quantization with an 8K context, Gemma 2 27B needs about 19.8 GB of VRAM, so the practical minimum is a RTX 3090. 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 27B need at maximum context?
This model's context caps at 8K tokens, where the total is about 19.8 GB — its worst case is close to its everyday case.
Can an RTX 4090 run Gemma 2 27B?
Yes. At Q4_K_M with an 8K context, Gemma 2 27B needs about 19.8 GB, leaving 4.2 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.