Qwen2.5 Coder 32B Instruct Abliterated — Hardware Requirements & GPU Compatibility
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566 downloads
34 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen 2.5
- Parameters
- 32B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-11-25
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen2.5 Coder 32B Instruct Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 14.4 GB | 22.5 GB | 13.60 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 16.4 GB | 24.5 GB | 15.60 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 16.8 GB | 24.9 GB | 16.00 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 20.0 GB | 28.1 GB | 19.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 20.8 GB | 28.9 GB | 20.00 GB | 5-bit legacy quantization |
| Q5_K_M | 5.70 | 23.6 GB | 31.7 GB | 22.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 27.2 GB | 35.3 GB | 26.40 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 32.8 GB | 40.9 GB | 32.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen2.5 Coder 32B Instruct Abliterated?
Q4_K_M · 20.0 GBQwen2.5 Coder 32B Instruct Abliterated (Q4_K_M) requires 20.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 27+ GB is recommended. Using the full 33K context window can add up to 8.1 GB, bringing total usage to 28.1 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen2.5 Coder 32B Instruct Abliterated?
Q4_K_M · 20.0 GB21 devices with unified memory can run Qwen2.5 Coder 32B Instruct Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen2.5 Coder 32B Instruct Abliterated need?
Qwen2.5 Coder 32B Instruct Abliterated requires 20.0 GB of VRAM at Q4_K_M, or 32.8 GB at Q8_0. Full 33K context adds up to 8.1 GB (28.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32B × 4.8 bits ÷ 8 = 19.2 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 8.9 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M20.0 GBQ4_K_M + full context28.1 GB- Can NVIDIA GeForce RTX 4090 run Qwen2.5 Coder 32B Instruct Abliterated?
Yes, at Q5_K_M (23.6 GB) or lower. Higher quantizations like Q6_K (27.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen2.5 Coder 32B Instruct Abliterated?
For Qwen2.5 Coder 32B Instruct Abliterated, Q4_K_M (20.0 GB) offers the best balance of quality and VRAM usage. Q5_0 (20.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.4 GB.
VRAM requirement by quantization
Q2_K14.4 GB~75%Q4_016.8 GB~85%Q4_K_M ★20.0 GB~89%Q5_020.8 GB~90%Q5_K_M23.6 GB~92%Q8_032.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 Coder 32B Instruct Abliterated on a Mac?
Qwen2.5 Coder 32B Instruct Abliterated requires at least 14.4 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 Qwen2.5 Coder 32B Instruct Abliterated locally?
Yes — Qwen2.5 Coder 32B Instruct Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 20.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 Coder 32B Instruct Abliterated?
At Q4_K_M, Qwen2.5 Coder 32B Instruct Abliterated can reach ~146 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~33 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 ÷ 20.0 × 0.55 = ~146 tok/s
Estimated speed at Q4_K_M (20.0 GB)
AMD Instinct MI300X~146 tok/sNVIDIA GeForce RTX 4090~33 tok/sNVIDIA H100 SXM~109 tok/sAMD Instinct MI250X~90 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 32B Instruct Abliterated?
At Q4_K_M, the download is about 19.20 GB. The full-precision Q8_0 version is 32.00 GB. The smallest option (Q2_K) is 13.60 GB.
- Which GPUs can run Qwen2.5 Coder 32B Instruct Abliterated?
5 consumer GPUs can run Qwen2.5 Coder 32B Instruct Abliterated at Q4_K_M (20.0 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwen2.5 Coder 32B Instruct Abliterated?
21 devices with unified memory can run Qwen2.5 Coder 32B Instruct Abliterated at Q4_K_M (20.0 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.