📍 Part of the Local LLMs in 2026 guide
Gemma 2 27B needs 16 GB VRAM at Q4. The RTX 4060 Ti 16GB has 16 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 4060 Ti 16GB |
|---|---|---|
| Q4_K_M (recommended) | 16 GB | ✅ |
| Q8_0 (high quality) | 28 GB | ❌ |
Expected Performance
Running Gemma 2 27B at Q4 on the RTX 4060 Ti 16GB, expect approximately ~16 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 16 GB, you have 0 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 4060 Ti 16GB
Pros: 16GB unlocks 14B models, efficient power draw, DLSS 3
Cons: Limited to 128-bit bus, not ideal for batch inference
Price: ~$450 — 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.