📍 Part of the Local LLMs in 2026 guide
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.
About the speed figure. The tokens/sec number above is an estimate, not a measured benchmark. Real throughput depends on your runtime and backend (Ollama, llama.cpp, vLLM), the exact quantization you download, context length, memory bandwidth, and whether any layers are offloaded to CPU. Treat it as a rough guide to the tier of performance, not a promised result.