ai-gpu
Gemma 27B VRAM Calculator
How much VRAM does Google Gemma 27B need? At FP16 it requires 54 GB. At INT4 it fits in ~14 GB — runnable on an RTX 4090 or A10G.
Google Gemma 2 27B: Efficient Quality
Google's Gemma 2 27B is one of the most efficient large language models available. It achieves near-70B quality with half the VRAM requirement.
VRAM by Quantization
| Quantization | VRAM needed | Minimum GPU |
|---|---|---|
| FP16 | ~54 GB | A100 80GB |
| INT8 | ~27 GB | RTX 4090 24GB + overflow |
| INT4 / GGUF Q4 | ~14 GB | RTX 4090 (comfortable) |
| GGUF Q8_0 | ~27 GB | A100 40GB |
Gemma 2 Family
| Model | INT4 VRAM | Quality tier |
|---|---|---|
| Gemma 2 2B | ~1.3 GB | Lightweight tasks |
| Gemma 2 9B | ~5 GB | Strong 7B-class |
| Gemma 2 27B | ~14 GB | Near-70B quality |
Architecture Highlights
Gemma 2 uses sliding window attention and grouped-query attention (GQA) to reduce memory and improve throughput. This makes it faster in inference than similarly-sized models, and allows longer context at lower VRAM cost than standard attention.
Key Terms
Full glossary →VRAM (Video RAM)
Memory on a GPU used to store model weights, activations, and KV cache during LLM inference. VRAM is the primary constraint when running large language models locally.
Quantization
A technique to reduce model memory usage by representing weights in lower precision (INT8, INT4, GGUF-Q4). Quantization trades a small accuracy loss for significant VRAM reduction.
Frequently Asked Questions
Can Gemma 27B run on a single RTX 4090?
At GGUF Q4_K_M, Gemma 27B requires ~15 GB VRAM — it fits on an RTX 4090 (24 GB) with room for the KV cache. For production serving via vLLM at INT4, you need slightly more headroom. An A10G 24GB or L40S 48GB is more comfortable.
How does Gemma 2 27B compare to Llama 3 70B?
Gemma 2 27B is competitive with Llama 3 70B on several benchmarks despite being 2.6× smaller. Google's training recipe produces a very capable model for its size. For VRAM-constrained setups, Gemma 2 27B offers near-70B quality at 13–15 GB INT4.
What are the Gemma model sizes?
Gemma 2 comes in three sizes: 2B (very lightweight, ~1 GB INT4), 9B (~5 GB INT4, excellent quality-per-VRAM), and 27B (~14 GB INT4). The 9B model is particularly strong — it outperforms Llama 3 8B on many tasks at similar VRAM.
Is Gemma safe for commercial use?
Yes. Gemma models are released under Google's Gemma Terms of Use which permits commercial deployment. Unlike Llama models (which have usage restrictions above a certain scale), Gemma's license is more permissive for production use.
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