Qwen2.5 Coder 7B Q4 K M GGUF — Hardware Requirements & GPU Compatibility
ChatCode
6.1K downloads
3 likesSpecifications
- Publisher
- itlwas
- Family
- Qwen 2.5
- Parameters
- 7B
- Release Date
- 2024-12-20
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 Coder 7B Q4 K M GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 4.6 GB | — | 4.20 GB | 4-bit medium quantization — most popular sweet spot |
Which GPUs Can Run Qwen2.5 Coder 7B Q4 K M GGUF?
Q4_K_M · 4.6 GBQwen2.5 Coder 7B Q4 K M GGUF (Q4_K_M) requires 4.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. 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 Q4 K M GGUF?
Q4_K_M · 4.6 GB33 devices with unified memory can run Qwen2.5 Coder 7B Q4 K M GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen2.5 Coder 7B Q4 K M GGUF need?
Qwen2.5 Coder 7B Q4 K M GGUF requires 4.6 GB of VRAM at Q4_K_M.
VRAM = Weights + KV Cache + Overhead
Weights = 7B × 4.8 bits ÷ 8 = 4.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M4.6 GB- Can I run Qwen2.5 Coder 7B Q4 K M GGUF on a Mac?
Qwen2.5 Coder 7B Q4 K M GGUF requires at least 4.6 GB at Q4_K_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 Q4 K M GGUF locally?
Yes — Qwen2.5 Coder 7B Q4 K M GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 4.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 Coder 7B Q4 K M GGUF?
At Q4_K_M, Qwen2.5 Coder 7B Q4 K M GGUF can reach ~631 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~142 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 ÷ 4.6 × 0.55 = ~631 tok/s
Estimated speed at Q4_K_M (4.6 GB)
AMD Instinct MI300X~631 tok/sNVIDIA GeForce RTX 4090~142 tok/sNVIDIA H100 SXM~472 tok/sAMD Instinct MI250X~390 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 Q4 K M GGUF?
At Q4_K_M, the download is about 4.20 GB.
- Which GPUs can run Qwen2.5 Coder 7B Q4 K M GGUF?
35 consumer GPUs can run Qwen2.5 Coder 7B Q4 K M GGUF at Q4_K_M (4.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen2.5 Coder 7B Q4 K M GGUF?
33 devices with unified memory can run Qwen2.5 Coder 7B Q4 K M GGUF at Q4_K_M (4.6 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.