Gemma 3 27B
Gemma · Gemma Terms of Use
Google's largest open-weight model before Gemma 4. Capable reasoning and instruction following. At 27B parameters, it sits between 8B models (too limited) and 70B models (too expensive). Wide multilingual support. Fits on 24 GB GPUs at Q4.
- Parameters
- 27.23B
- Architecture
- Dense
- Context
- 128,000 tokens
- Released
- 2025-03-12
- Engines
- llama.cpp, ollama, vLLM
- Builder Tools
- Cursor, Continue, LM Studio, Open WebUI
Parameters
27.23B
VRAM
22.3 GB
Context
125K
Formats
5
GPUs
42
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 RTX 4090 Laptop (150-175W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 130 tok/s at Q8_0. For the best experience, AMD AI Powerhouse ($1,818) is recommended.
Source: OwnRig methodology
22.3 GB
Q6_K
20.4 GB
125K tokens
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VRAM Requirements
| 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 |
Context Length Impact
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 |
Compatible GPUs
42 devicesShowing 42 of 42 entries
Builder Context
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.
Recommended Builds
Complete PC builds that can run Gemma 3 27B.
Frequently Asked Questions
- How much VRAM does Gemma 3 27B need?
- Gemma 3 27B requires 22.3 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 13.3 GB.
- What is the best GPU for Gemma 3 27B?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Gemma 3 27B, achieving 130 tok/s at Q8_0 with an excellent rating.
- Can I run Gemma 3 27B on an RTX 4060 Ti?
- Yes. On the NVIDIA GeForce RTX 4060 Ti 16GB, Gemma 3 27B runs at 6 tok/s (Q3_K_M, marginal).
- What quantization should I use for Gemma 3 27B?
- For the best quality, use Q6_K (22.3 GB VRAM). If your GPU has limited VRAM, Q3_K_M (13.3 GB) is the most efficient option with acceptable quality.
- Is Gemma 3 27B good for coding?
- Yes. Gemma 3 27B is used with Cursor, Continue, LM Studio, Open WebUI for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.
Related Guides
Data confidence: estimated. Source
VRAM requirements are calculated from model parameters and may vary by inference engine, context length, and batch size. Performance estimates are based on community benchmarks and should be verified for your specific configuration.Gemma is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.