Gemma 2 9B Instruct
Gemma · Gemma License
Google's 9B model with effective knowledge distillation. Competitive with Llama 3.1 8B on most benchmarks. Shorter max context (8K) is the main limitation vs Llama.
- Parameters
- 9.24B
- Architecture
- Dense
- Context
- 8,192 tokens
- Released
- 2024-06-27
- Engines
- llama.cpp, ollama, vLLM
Parameters
9.24B
VRAM
6.6 GB
Context
8K
Formats
4
GPUs
19
Gemma 2 9B Instruct (9.24B) requires 6.6 GB VRAM at recommended quality (Q5_K_M). At efficient quality (Q4_K_M), it fits in 5.6 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 320 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.
Source: OwnRig methodology
6.6 GB
Q5_K_M
5.5 GB
8K tokens
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VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 10.2 GB | 9.2 GB |
| recommended | Q5_K_M | 6.6 GB | 5.5 GB |
| efficient | Q4_K_M | 5.6 GB | 4.6 GB |
| compressed | Q3_K_M | 4.6 GB | 3.6 GB |
Context Length Impact
KV cache VRAM at Q5_K_M quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 102 MB | 6.7 GB |
| 4K | 307 MB | 6.9 GB |
| 8K | 614 MB | 7.2 GB |
Compatible GPUs
19 devices| NVIDIA Grace Blackwell Ultra GB300 | Q8_0 | 320 tok/s | Excellent |
| NVIDIA GeForce RTX 3080 10GB | Q4_K_M | 45 tok/s | Excellent |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q5_K_M | 48 tok/s | Excellent |
| NVIDIA GeForce RTX 4090 | Q8_0 | 80 tok/s | Excellent |
| AMD Radeon Pro W7900 | Q8_0 | 86 tok/s | Excellent |
| NVIDIA RTX PRO 6000 Blackwell | Q8_0 | 106 tok/s | Excellent |
| NVIDIA RTX PRO 6000 Blackwell Max-Q | Q8_0 | 98 tok/s | Excellent |
| NVIDIA GeForce RTX 3060 12GB | Q5_K_M | 30 tok/s | Good |
| NVIDIA GeForce RTX 4060 8GB | Q4_K_M | 28 tok/s | Good |
| NVIDIA RTX 4080 Laptop (120-150W) | Q5_K_M | 34 tok/s | Good |
| AMD Radeon RX 7900 XTX | Q8_0 | 69 tok/s | Good |
| NVIDIA GeForce RTX 5060 8GB | Q4_K_M | 32 tok/s | Good |
| Apple M3 Pro (18GB Unified) | Q4_K_M | 13 tok/s | Acceptable |
| NVIDIA RTX 4060 Laptop (40-60W) | Q4_K_M | 17 tok/s | Acceptable |
| NVIDIA RTX 4070 Laptop (80-115W) | Q4_K_M | 20 tok/s | Acceptable |
| AMD Radeon RX 7600 | Q4_K_M | 22 tok/s | Acceptable |
| AMD Radeon RX 9070 | Q8_0 | – | Acceptable |
| AMD Radeon RX 9060 XT 16GB | Q8_0 | – | Acceptable |
| AMD Radeon RX 9060 XT 8GB | Q8_0 | – | Not viable |
Showing 19 of 19 entries
Recommended Builds
Complete PC builds that can run Gemma 2 9B Instruct.
Frequently Asked Questions
- How much VRAM does Gemma 2 9B Instruct need?
- Gemma 2 9B Instruct requires 6.6 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 4.6 GB.
- What is the best GPU for Gemma 2 9B Instruct?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Gemma 2 9B Instruct, achieving 320 tok/s at Q8_0 with an excellent rating.
- What quantization should I use for Gemma 2 9B Instruct?
- For the best quality, use Q5_K_M (6.6 GB VRAM). If your GPU has limited VRAM, Q3_K_M (4.6 GB) is the most efficient option with acceptable quality.
- Is Gemma 2 9B Instruct good for coding?
- Gemma 2 9B Instruct supports coding use cases. For the best coding experience, pair it with an embedding model for local RAG.
Data confidence: verified. 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.