Qwen2.5 Coder 7B Instruct Abliterated — Hardware Requirements & GPU Compatibility
ChatCode
2.1K downloads
10 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen 2.5
- Parameters
- 7.6B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-11-25
- License
- Apache 2.0
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How Much VRAM Does Qwen2.5 Coder 7B Instruct Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| 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 |
| 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 |
| Q4_K_M | 4.80 | 5.0 GB | 6.8 GB | 4.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.8 GB | 7.6 GB | 5.43 GB | 5-bit medium quantization — good quality/size tradeoff |
| 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 Abliterated?
Q4_K_M · 5.0 GBQwen2.5 Coder 7B Instruct Abliterated (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 Abliterated?
Q4_K_M · 5.0 GB33 devices with unified memory can run Qwen2.5 Coder 7B Instruct Abliterated, 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 Instruct Abliterated need?
Qwen2.5 Coder 7B Instruct Abliterated 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 Abliterated?
For Qwen2.5 Coder 7B Instruct Abliterated, 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 Abliterated on a Mac?
Qwen2.5 Coder 7B Instruct Abliterated 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 Abliterated locally?
Yes — Qwen2.5 Coder 7B Instruct Abliterated 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 Abliterated?
At Q4_K_M, Qwen2.5 Coder 7B Instruct Abliterated 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 Abliterated?
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.
- Which GPUs can run Qwen2.5 Coder 7B Instruct Abliterated?
35 consumer GPUs can run Qwen2.5 Coder 7B Instruct Abliterated at Q4_K_M (5.0 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 Instruct Abliterated?
33 devices with unified memory can run Qwen2.5 Coder 7B Instruct Abliterated at Q4_K_M (5.0 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.