GoogleGemma 2 27B Instruct on NVIDIA GeForce RTX 4060 Ti 16GB
RTX 4060 Ti 16GB runs Gemma 2 27B Instruct at Q4_K_M — 12 tok/s. Usable on 16 GB VRAM — see full quantization options below.
Model Size
27.23B
Device VRAM
16 GB
Bandwidth
288 GB/s
Quantization
Q4_K_M
Performance by Quantization
OwnRig currently has one published compatibility entry for Gemma 2 27B Instruct on NVIDIA GeForce RTX 4060 Ti 16GB at Q4_K_M. This is the best supported pairing we can stand behind today.
| Quantization | Speed | TTFT | Fits in VRAM | Rating | Confidence |
|---|---|---|---|---|---|
| Q4_K_M | 12 tok/s | 700ms | ✓ Yes | Acceptable | estimated |
Notes
Q4_K_M
Tight fit at 15.5GB on 16GB. Works but slow due to bandwidth constraints.
About Gemma 2 27B Instruct
Gemma 2 27B Instruct (27.23B) is a chat, coding, reasoning, multi-purpose model. Google's 27B model with effective knowledge distillation. Reasoning and coding at a size that fits on a single 24 GB GPU at Q4. Limited to 8K context.
View all Gemma 2 27B Instruct hardware options →About NVIDIA GeForce RTX 4060 Ti 16GB
NVIDIA GeForce RTX 4060 Ti 16GB has 16 GB at 288 GB/s. Street price: $449.
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Estimate method: Community benchmarks. Reference hardware source: github.com (2026-01-15)
Performance varies by driver version, inference engine, quantization method, context length, and system configuration. Figures shown are estimates based on community benchmarks and may not reflect your exact setup. Product names are trademarks of their respective owners. OwnRig is independent and not affiliated with any hardware or AI model provider.