InternLM 2.5 7B Chat
InternLM · Apache 2.0
Shanghai AI Lab's efficient 7B model with coding and reasoning capabilities.
- Parameters
- 7.74B
- Architecture
- Dense
- Context
- 32,768 tokens
- Released
- 2024-07-03
- Engines
- llama.cpp, ollama, vLLM, TGI
- Builder Tools
- Cursor, Continue, Aider, Open WebUI, LM Studio
Parameters
7.74B
VRAM
6.7 GB
Context
32K
Formats
4
GPUs
19
InternLM 2.5 7B Chat (7.74B) requires 6.7 GB VRAM at recommended quality (Q6_K). At efficient quality (Q4_K_M), it fits in 4.5 GB VRAM, making it compatible with the NVIDIA RTX 4060 Laptop (40-60W). On NVIDIA Grace Blackwell Ultra GB300, expect approximately 350 tok/s at Q8_0. For the best experience, Starter AI Desktop ($582) is recommended.
Source: OwnRig methodology
6.7 GB
Q6_K
5.9 GB
32K tokens
Chat
VRAM Requirements
| Quality | Quantization | VRAM | File Size |
|---|---|---|---|
| full | Q8_0 | 8.6 GB | 7.6 GB |
| recommended | Q6_K | 6.7 GB | 5.9 GB |
| recommended | Q5_K_M | 5.6 GB | 4.9 GB |
| efficient | Q4_K_M | 4.5 GB | 3.9 GB |
Context Length Impact
KV cache VRAM at Q6_K quality. Longer context = more memory.
| Context | KV Cache | Total VRAM |
|---|---|---|
| 2K | 102 MB | 6.8 GB |
| 4K | 307 MB | 7 GB |
| 8K | 512 MB | 7.2 GB |
| 16K | 1 GB | 7.7 GB |
| 32K | 2 GB | 8.7 GB |
Compatible GPUs
19 devices| NVIDIA Grace Blackwell Ultra GB300 | Q8_0 | 350 tok/s | Excellent |
| NVIDIA GeForce RTX 3080 10GB | Q5_K_M | 50 tok/s | Excellent |
| NVIDIA GeForce RTX 4070 Ti 12GB | Q5_K_M | 46 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 | 127 tok/s | Excellent |
| NVIDIA RTX PRO 6000 Blackwell Max-Q | Q8_0 | 117 tok/s | Excellent |
| NVIDIA GeForce RTX 3060 12GB | Q5_K_M | 35 tok/s | Good |
| NVIDIA GeForce RTX 4060 8GB | Q4_K_M | 30 tok/s | Good |
| NVIDIA RTX 4080 Laptop (120-150W) | Q5_K_M | 32 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 | 15 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 19 of 19 entries
Builder Context
InternLM 2.5 7B Chat is commonly used with Cursor, Continue, Aider, Open WebUI, LM Studio. For an AI coding workflow, pair it with an embedding model like nomic-embed-text for local RAG.
Frequently Asked Questions
- How much VRAM does InternLM 2.5 7B Chat need?
- InternLM 2.5 7B Chat requires 6.7 GB VRAM at recommended quality (Q6_K). At lower quality settings, it can fit in as little as 4.5 GB.
- What is the best GPU for InternLM 2.5 7B Chat?
- The NVIDIA Grace Blackwell Ultra GB300 delivers the best performance for InternLM 2.5 7B Chat, achieving 350 tok/s at Q8_0 with an excellent rating.
- What quantization should I use for InternLM 2.5 7B Chat?
- For the best quality, use Q6_K (6.7 GB VRAM). If your GPU has limited VRAM, Q4_K_M (4.5 GB) is the most efficient option with acceptable quality.
- Is InternLM 2.5 7B Chat good for coding?
- Yes. InternLM 2.5 7B Chat is used with Cursor, Continue, Aider, Open WebUI, LM Studio for local AI coding. For the best coding experience, pair it with an embedding model for local RAG.
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.InternLM is a trademark of its respective owner. OwnRig is not affiliated with or endorsed by the model creator.