Gemma · Gemma Terms of Use
Google's strongest open-weight model. Excellent reasoning and instruction following. At 27B parameters, it's the sweet spot between 8B models (too limited) and 70B models (too expensive). Strong multilingual support. Fits on 24GB GPUs at Q4.
Gemma 3 27B (27.23B) requires 22.3 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 16.3 GB VRAM, making it compatible with the NVIDIA GeForce RTX 4060 Ti 16GB. On NVIDIA GeForce RTX 5090, expect approximately 35 tok/s at Q5_K_M. For the best experience, AMD AI Powerhouse ($1,818) is recommended.
— OwnRig methodology, data updated 2026-03-15
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 29.5 GB | 27.2 GB |
| recommended | Q6_K | 22.3 GB | 20.4 GB |
| recommended | Q5_K_M | 19.3 GB | 17.5 GB |
| efficient | Q4_K_M | 16.3 GB | 14.8 GB |
| compressed | Q3_K_M | 13.3 GB | 12.1 GB |
KV cache VRAM at Q6_K quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 307 MB | 22.6 GB |
| 4K | 512 MB | 22.8 GB |
| 8K | 1 GB | 23.3 GB |
| 16K | 2 GB | 24.3 GBexceeds 24 GB |
| 32K | 4.1 GB | 26.4 GBexceeds 24 GB |
| 64K | 8.2 GB | 30.5 GBexceeds 24 GB |
Performance data for Gemma 3 27B across different hardware.
| Device | Quantization | Speed | Rating | Fits in VRAM |
|---|---|---|---|---|
| NVIDIA GeForce RTX 3060 12GB | Q3_K_M | — | Not Viable | ✗ (offload) |
| NVIDIA GeForce RTX 4060 Ti 16GB | Q3_K_M | 6 tok/s | Marginal | ✓ |
| NVIDIA GeForce RTX 4070 Ti Super | Q3_K_M | 12 tok/s | Acceptable | ✓ |
| NVIDIA GeForce RTX 4070 Super | Q3_K_M | — | Not Viable | ✗ (offload) |
| NVIDIA GeForce RTX 4080 Super | Q3_K_M | 14 tok/s | Acceptable | ✓ |
| NVIDIA GeForce RTX 4090 | Q4_K_M | 22 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 3090 | Q4_K_M | 18 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 5080 | Q3_K_M | 18 tok/s | Acceptable | ✓ |
| NVIDIA GeForce RTX 5090 | Q5_K_M | 35 tok/s | Excellent | ✓ |
| Apple M4 Pro (24GB Unified) | Q4_K_M | 8 tok/s | Acceptable | ✓ |
| Apple M4 Pro (48GB) | Q5_K_M | 8 tok/s | Acceptable | ✓ |
| Apple M4 Max (36GB Unified) | Q5_K_M | 15 tok/s | Good | ✓ |
| Apple M4 Max (64GB Unified) | Q6_K | 14 tok/s | Good | ✓ |
| Apple M4 Max (128GB Unified) | Q8_0 | 12 tok/s | Good | ✓ |
| NVIDIA GeForce RTX 4060 8GB | Q3_K_M | — | Not Viable | ✗ (offload) |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q3_K_M | — | Not Viable | ✗ (offload) |
| NVIDIA GeForce RTX 3080 10GB | Q3_K_M | — | Not Viable | ✗ (offload) |
| Apple M3 Pro (18GB Unified) | Q3_K_M | 3 tok/s | Marginal | ✓ |
Gemma 3 27B is commonly used with Cursor, Continue, LM Studio, Open WebUI. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
Complete PC builds that can run Gemma 3 27B.
Data confidence: estimated. Last updated: 2026-03-15. Source