Qwen2.5 Coder 7B Instruct — Hardware Requirements & GPU Compatibility
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Qwen2.5 Coder 7B Instruct is a 7.6-billion parameter code-specialized instruction-tuned model from Alibaba Cloud. It is trained on a large corpus of source code and natural language, fine-tuned for programming assistance tasks such as code generation, completion, debugging, and code explanation. The model supports a 128K token context window and runs efficiently on consumer GPUs with 8GB or more of VRAM. It provides a good balance between coding capability and hardware requirements for developers looking to run a local coding assistant. Released under the Apache 2.0 license.
2.1M downloads
666 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 2.5
- Parameters
- 7.6B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-01-12
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 Coder 7B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_M | 2.70 | 3.0 GB | 4.8 GB | 2.57 GB | Importance-weighted 2-bit, medium |
| IQ3_XS | 3.30 | 3.6 GB | 5.3 GB | 3.14 GB | Importance-weighted 3-bit, extra small |
| Q2_K | 3.40 | 3.6 GB | 5.4 GB | 3.24 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.8 GB | 5.5 GB | 3.33 GB | 3-bit small quantization |
| IQ3_M | 3.60 | 3.8 GB | 5.6 GB | 3.43 GB | Importance-weighted 3-bit, medium |
| Q3_K_M | 3.90 | 4.1 GB | 5.9 GB | 3.71 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.2 GB | 6.0 GB | 3.81 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 4.3 GB | 6.1 GB | 3.90 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 4.5 GB | 6.3 GB | 4.09 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 4.7 GB | 6.5 GB | 4.28 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 5.0 GB | 6.8 GB | 4.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q4_K_L | 4.90 | 5.1 GB | 6.8 GB | 4.66 GB | 4-bit large quantization |
| Q5_0 | 5.00 | 5.2 GB | 6.9 GB | 4.76 GB | 5-bit legacy quantization |
| Q5_K_S | 5.50 | 5.7 GB | 7.4 GB | 5.24 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 5.8 GB | 7.6 GB | 5.43 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q5_K_L | 5.80 | 5.9 GB | 7.7 GB | 5.52 GB | 5-bit large quantization |
| Q6_K | 6.60 | 6.7 GB | 8.5 GB | 6.28 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.0 GB | 9.8 GB | 7.62 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 Coder 7B Instruct?
Q4_K_M · 5.0 GBQwen2.5 Coder 7B Instruct (Q4_K_M) requires 5.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 33K context window can add up to 1.8 GB, bringing total usage to 6.8 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen2.5 Coder 7B Instruct?
Q4_K_M · 5.0 GB33 devices with unified memory can run Qwen2.5 Coder 7B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (8)
Frequently Asked Questions
- How much VRAM does Qwen2.5 Coder 7B Instruct need?
Qwen2.5 Coder 7B Instruct requires 5.0 GB of VRAM at Q4_K_M, or 8.0 GB at Q8_0. Full 33K context adds up to 1.8 GB (6.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.6B × 4.8 bits ÷ 8 = 4.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.2 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M5.0 GBQ4_K_M + full context6.8 GB- What's the best quantization for Qwen2.5 Coder 7B Instruct?
For Qwen2.5 Coder 7B Instruct, Q4_K_M (5.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 3.0 GB.
VRAM requirement by quantization
IQ2_M3.0 GB~62%IQ3_M3.8 GB~78%Q4_K_S4.7 GB~88%Q4_K_M ★5.0 GB~89%Q5_K_S5.7 GB~92%Q8_08.0 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 Coder 7B Instruct on a Mac?
Qwen2.5 Coder 7B Instruct requires at least 3.0 GB at IQ2_M, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Qwen2.5 Coder 7B Instruct locally?
Yes — Qwen2.5 Coder 7B Instruct can run locally on consumer hardware. At Q4_K_M quantization it needs 5.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 Coder 7B Instruct?
At Q4_K_M, Qwen2.5 Coder 7B Instruct can reach ~584 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~131 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 5.0 × 0.55 = ~584 tok/s
Estimated speed at Q4_K_M (5.0 GB)
AMD Instinct MI300X~584 tok/sNVIDIA GeForce RTX 4090~131 tok/sNVIDIA H100 SXM~437 tok/sAMD Instinct MI250X~361 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen2.5 Coder 7B Instruct?
At Q4_K_M, the download is about 4.57 GB. The full-precision Q8_0 version is 7.62 GB. The smallest option (IQ2_M) is 2.57 GB.