Gemma 3 27B

Overview

Google's flagship Gemma 3 at 27B — multimodal, 128K context, and an LMArena Elo of 1338 that beats Llama 3.1 405B at 15x smaller. Sets the bar for open chat under 30B.

When to pick this model

• You want the strongest open chat model under 30B
• You need multimodal input and long context in one model
• You're standardizing on Google's open stack
• You have a 24GB+ GPU and want frontier-class chat locally

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	16 GB
Q5_K_M	19 GB
Q8_0	29 GB
FP16 (no quantization)	54 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

Published benchmark scores

Benchmark	Score
LMArena Elo	73
MMLU	78.6
MMLU-Pro	67.5
MATH	89

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

Strengths

• LMArena Elo 1338 — beats Llama 3.1 405B at 15x smaller
• Multimodal with vision input
• 128K context window
• 140 language coverage

Limitations

• Gemma License rather than Apache
• No thinking mode for hard reasoning
• Trails dedicated reasoners on math benchmarks

Architecture & training

Architecture: Dense VLM · sliding-window attention

Training: 14T tokens.

Quick start

ollama run gemma3:27b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.