Local AI

Can RTX 4090 24GB Run Gemma 2 27B? (Tested, 16GB VRAM Needed)

Can the RTX 4090 24GB run Gemma 2 27B locally? Yes — runs at Q4 (recommended quantization). See VRAM requirements, performance estimates, and the best quantization level for your setup.

📍 Part of the Local LLMs in 2026 guide

✅ Yes — runs at Q4 (recommended quantization)
Gemma 2 27B needs 16 GB VRAM at Q4. The RTX 4090 24GB has 24 GB.

● Gemma 2 27B (Google) is a 27B parameter model used for High-quality chat, reasoning, analysis. Best quality under 24GB at Q4 — punches above its weight.

VRAM Requirements

Quantization	VRAM Needed	RTX 4090 24GB
Q4_K_M (recommended)	16 GB	✅
Q8_0 (high quality)	28 GB	❌

Expected Performance

Running Gemma 2 27B at Q4 on the RTX 4090 24GB, expect approximately ~24 tokens/sec with Ollama or llama.cpp. That’s fast enough for interactive chat — you’ll see responses streaming in real-time.

Headroom: With 16 GB used out of 24 GB, you have 8 GB free for KV cache (context window). At 4K context, this is plenty. At 32K+ context you may need to reduce batch size.

About the RTX 4090 24GB

Pros: Fastest consumer GPU, excellent for real-time inference

Cons: Expensive, still only 24GB limits 70B models

Price: ~$1,800 — Check current price on Amazon →

Try It Yourself

🎯 LLM Hardware Checker

Select your exact GPU + RAM and see ALL models you can run.

💾 VRAM Calculator

Pick any model, see exact VRAM at Q4/Q5/Q8/FP16 with context scaling.