Qwen 2.5 7B Instruct
Qwen · Apache 2.0
Alibaba's 7B model with 128K context window support. Competitive with Llama 3.1 8B across benchmarks. Apache 2.0 license. Wide multilingual support.
- Parameters
- 7.62B
- Architecture
- Dense
- Context
- 131,072 tokens
- Released
- 2024-09-19
- Engines
- llama.cpp, ollama, vLLM
- Builder Tools
- Continue, LM Studio, Open WebUI
Parameters
7.62B
VRAM
5.5 GB
Context
128K
Formats
4
GPUs
20
Qwen 2.5 7B Instruct (7.62B) requires 5.5 GB VRAM at recommended quality (Q5_K_M). At efficient quality (Q4_K_M), it fits in 4.7 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 360 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.
Source: OwnRig methodology
5.5 GB
Q5_K_M
4.6 GB
128K tokens
Chat
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 8.5 GB | 7.6 GB |
| recommended | Q5_K_M | 5.5 GB | 4.6 GB |
| efficient | Q4_K_M | 4.7 GB | 3.8 GB |
| compressed | Q3_K_M | 3.9 GB | 3 GB |
Context Length Impact
KV cache VRAM at Q5_K_M quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 102 MB | 5.6 GB |
| 4K | 205 MB | 5.7 GB |
| 8K | 512 MB | 6 GB |
| 16K | 1 GB | 6.5 GB |
| 32K | 1.9 GB | 7.4 GB |
| 64K | 3.8 GB | 9.3 GB |
| 128K | 7.7 GB | 13.2 GB |
Compatible GPUs
20 devices| NVIDIA Grace Blackwell Ultra GB300 | Q8_0 | 360 tok/s | Excellent |
| NVIDIA GeForce RTX 3080 10GB | Q5_K_M | 52 tok/s | Excellent |
| NVIDIA GeForce RTX 4070 Super | Q5_K_M | 52 tok/s | Excellent |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q5_K_M | 48 tok/s | Excellent |
| NVIDIA GeForce RTX 4090 | Q8_0 | 88 tok/s | Excellent |
| AMD Radeon Pro W7900 | Q8_0 | 95 tok/s | Excellent |
| NVIDIA RTX PRO 6000 Blackwell | Q8_0 | 129 tok/s | Excellent |
| NVIDIA RTX PRO 6000 Blackwell Max-Q | Q8_0 | 119 tok/s | Excellent |
| NVIDIA GeForce RTX 3060 12GB | Q5_K_M | 33 tok/s | Good |
| NVIDIA GeForce RTX 4060 8GB | Q4_K_M | 30 tok/s | Good |
| NVIDIA RTX 4080 Laptop (120-150W) | Q5_K_M | 34 tok/s | Good |
| AMD Radeon RX 7900 XTX | Q8_0 | 76 tok/s | Good |
| NVIDIA GeForce RTX 5060 8GB | Q4_K_M | 35 tok/s | Good |
| Apple M3 Pro (18GB Unified) | Q4_K_M | 16 tok/s | Acceptable |
| NVIDIA RTX 4060 Laptop (40-60W) | Q4_K_M | 18 tok/s | Acceptable |
| NVIDIA RTX 4070 Laptop (80-115W) | Q4_K_M | 21 tok/s | Acceptable |
| AMD Radeon RX 7600 | Q4_K_M | 23 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 20 of 20 entries
Builder Context
Qwen 2.5 7B Instruct is commonly used with 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 Qwen 2.5 7B Instruct.
Budget AI Desktop
Your own AI coding setup for under $800
Runs 7 models
Budget Home AI Server
Always-on AI assistant for the whole household
Runs 7 models
Compact SFF AI Build
Serious AI power in a compact, desk-friendly form factor
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Frequently Asked Questions
- How much VRAM does Qwen 2.5 7B Instruct need?
- Qwen 2.5 7B Instruct requires 5.5 GB VRAM at recommended quality (Q5_K_M). At lower quality settings, it can fit in as little as 3.9 GB.
- What is the best GPU for Qwen 2.5 7B Instruct?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for Qwen 2.5 7B Instruct, achieving 360 tok/s at Q8_0 with an excellent rating.
- What quantization should I use for Qwen 2.5 7B Instruct?
- For the best quality, use Q5_K_M (5.5 GB VRAM). If your GPU has limited VRAM, Q3_K_M (3.9 GB) is the most efficient option with acceptable quality.
- Is Qwen 2.5 7B Instruct good for coding?
- Yes. Qwen 2.5 7B Instruct is used with 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: 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.Qwen is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.