Gemma VRAM Requirements (2B, 7B, 27B)

Gemma VRAM Requirements Overview

Google’s Gemma family brings Gemini-derived architecture to open-weight models. From the lightweight 2B to the capable 27B, Gemma models are competitive with similar-sized models from Meta and Mistral. This guide covers VRAM requirements for every Gemma variant to help you pick the right dedicated GPU server for Gemma hosting.

Gemma 2 introduced significant architecture improvements including sliding window attention alternating with full attention, and group-query attention across all sizes. These changes make Gemma 2 models more efficient than their predecessors at similar parameter counts.

Complete VRAM Table (All Models)

Gemma 1 Models

Gemma 2 Models

Gemma 2 9B replaces the original Gemma 7B with better performance at a similar VRAM footprint. Gemma 2 27B is the largest variant and requires at least 16 GB at 4-bit quantization. For comparisons with similar-sized models, see our LLaMA 3 VRAM requirements and Phi VRAM requirements pages.

Which GPU Do You Need?

Gemma 2 2B on an RTX 3050 in FP16 is one of the cheapest production-capable LLM setups available.

Context Length Impact on VRAM

Gemma 2 models support 8,192 tokens of context. KV cache usage scales accordingly:

Gemma 2’s alternating sliding window / full attention design helps keep KV cache more manageable than pure full-attention models at the same size. The 8K context limit is shorter than LLaMA 3’s 128K but sufficient for most chat and RAG applications.

Batch Size Impact on VRAM

Gemma 2 2B at 4-bit can serve 16 concurrent users within just 5.2 GB, making it viable even on the cheapest GPUs for high-throughput applications.

Practical Deployment Recommendations

• Edge/low-cost: Gemma 2 2B on RTX 3050 (FP16). Cheapest LLM deployment with reasonable quality.
• Personal assistant: Gemma 2 9B on RTX 4060 (4-bit). 20-25 tok/s, good general-purpose model.
• Production API: Gemma 2 9B on RTX 4060 Ti (FP16). Full quality with batch support for 4-8 users.
• High quality: Gemma 2 27B on RTX 3090 (INT8 or 4-bit). Strong benchmark performance at 15-25 tok/s.
• Maximum quality: Gemma 2 27B FP16 on 2x RTX 3090. Full precision with batching headroom.

For cost analysis, see our cheapest GPU for AI inference guide and the LLM cost calculator.

Quick Setup Commands

Ollama

curl -fsSL https://ollama.com/install.sh | sh
ollama run gemma2:2b
ollama run gemma2:9b
ollama run gemma2:27b

vLLM

# Gemma 2 9B FP16 on RTX 4060 Ti
vllm serve google/gemma-2-9b-it \
  --dtype float16 --max-model-len 8192

# Gemma 2 27B AWQ on RTX 3090
vllm serve google/gemma-2-27b-it \
  --quantization awq --max-model-len 4096

For full deployment guides, see our Ollama hosting and vLLM hosting pages. Compare with other models on our best GPU for LLM inference page and use the benchmark tool for real-time comparisons.