CodeQwen1.5 7B — Hardware Requirements & GPU Compatibility
ChatCode
CodeQwen1.5 7B is a 7.3B-parameter open language model from Alibaba in the Qwen family. It supports a context window of up to 65,536 tokens. At Q4_K_M it needs about 4.78 GB of VRAM — see which GPUs and Macs can run it below.
1.6K downloads
103 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen
- Parameters
- 7.3B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 92,416
- License
- Other
Get Started
HuggingFace
How Much VRAM Does CodeQwen1.5 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.5 GB | 7.7 GB | 3.08 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.6 GB | 7.8 GB | 3.17 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.0 GB | 8.1 GB | 3.53 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.1 GB | 8.2 GB | 3.63 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 4.8 GB | 8.9 GB | 4.35 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.6 GB | 9.8 GB | 5.17 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.4 GB | 10.6 GB | 5.98 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 7.7 GB | 11.8 GB | 7.25 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run CodeQwen1.5 7B?
Q4_K_M · 4.8 GBCodeQwen1.5 7B (Q4_K_M) requires 4.8 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 66K context window can add up to 4.2 GB, bringing total usage to 8.9 GB. 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?
Q4_K_M · 4.8 GB33 devices with unified memory can run CodeQwen1.5 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomBenchmarks
View all 2 →Related Models
Derivatives (1)
Frequently Asked Questions
- How much VRAM does CodeQwen1.5 7B need?
CodeQwen1.5 7B requires 4.8 GB of VRAM at Q4_K_M, or 7.7 GB at Q8_0. Full 66K context adds up to 4.2 GB (8.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.3B × 4.8 bits ÷ 8 = 4.4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.5 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M4.8 GBQ4_K_M + full context8.9 GB- What's the best quantization for CodeQwen1.5 7B?
For CodeQwen1.5 7B, Q4_K_M (4.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (5.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.5 GB.
VRAM requirement by quantization
Q2_K3.5 GBQ4_04.1 GBQ4_K_S4.5 GBQ4_K_M ★4.8 GBQ5_K_S5.4 GBQ8_07.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run CodeQwen1.5 7B on a Mac?
CodeQwen1.5 7B requires at least 3.5 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 locally?
Yes — CodeQwen1.5 7B can run locally on consumer hardware. At Q4_K_M quantization it needs 4.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is CodeQwen1.5 7B?
At Q4_K_M, CodeQwen1.5 7B can reach ~610 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~137 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 ÷ 4.8 × 0.55 = ~610 tok/s
Estimated speed at Q4_K_M (4.8 GB)
~610 tok/s~137 tok/s~456 tok/s~377 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?
At Q4_K_M, the download is about 4.35 GB. The full-precision Q8_0 version is 7.25 GB. The smallest option (Q2_K) is 3.08 GB.
- Which GPUs can run CodeQwen1.5 7B?
35 consumer GPUs can run CodeQwen1.5 7B at Q4_K_M (4.8 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?
33 devices with unified memory can run CodeQwen1.5 7B at Q4_K_M (4.8 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.