CodeQwen1.5 7B Chat GGUF — Hardware Requirements & GPU Compatibility
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CodeQwen1.5 7B Chat GGUF is a 7B-parameter open language model from Alibaba in the Qwen family. At Q4_K_M it needs about 4.62 GB of VRAM — see which GPUs and Macs can run it below.
1.1K downloads
110 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 7B
- License
- Other
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HuggingFace
How Much VRAM Does CodeQwen1.5 7B Chat GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.3 GB | — | 2.98 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 3.8 GB | — | 3.41 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 3.9 GB | — | 3.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 4.6 GB | — | 4.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 4.8 GB | — | 4.38 GB | 5-bit legacy quantization |
| Q5_K_M | 5.70 | 5.5 GB | — | 4.99 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.3 GB | — | 5.78 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.7 GB | — | 7.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run CodeQwen1.5 7B Chat GGUF?
Q4_K_M · 4.6 GBCodeQwen1.5 7B Chat 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 CodeQwen1.5 7B Chat GGUF?
Q4_K_M · 4.6 GB33 devices with unified memory can run CodeQwen1.5 7B Chat GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does CodeQwen1.5 7B Chat GGUF need?
CodeQwen1.5 7B Chat GGUF requires 4.6 GB of VRAM at Q4_K_M, or 7.7 GB at Q8_0.
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- What's the best quantization for CodeQwen1.5 7B Chat GGUF?
For CodeQwen1.5 7B Chat GGUF, Q4_K_M (4.6 GB) offers the best balance of quality and VRAM usage. Q5_0 (4.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.3 GB.
VRAM requirement by quantization
Q2_K3.3 GBQ4_03.9 GBQ4_K_M ★4.6 GBQ5_04.8 GBQ5_K_M5.5 GBQ8_07.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run CodeQwen1.5 7B Chat GGUF on a Mac?
CodeQwen1.5 7B Chat GGUF requires at least 3.3 GB at Q2_K, 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 CodeQwen1.5 7B Chat GGUF locally?
Yes — CodeQwen1.5 7B Chat 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 CodeQwen1.5 7B Chat GGUF?
At Q4_K_M, CodeQwen1.5 7B Chat 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)
~631 tok/s~142 tok/s~472 tok/s~390 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of CodeQwen1.5 7B Chat GGUF?
At Q4_K_M, the download is about 4.20 GB. The full-precision Q8_0 version is 7.00 GB. The smallest option (Q2_K) is 2.98 GB.
- Which GPUs can run CodeQwen1.5 7B Chat GGUF?
35 consumer GPUs can run CodeQwen1.5 7B Chat 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 CodeQwen1.5 7B Chat GGUF?
33 devices with unified memory can run CodeQwen1.5 7B Chat 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.