Qwen2.5 72B Instruct Abliterated — Hardware Requirements & GPU Compatibility
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An abliterated (uncensored) version of Alibaba's Qwen2.5 72B Instruct, modified by huihui-ai. Abliteration is a technique that removes or weakens the model's built-in refusal mechanisms and safety guardrails, resulting in a model that is more willing to respond to a broader range of prompts without declining. The base Qwen2.5 72B Instruct is one of Alibaba's flagship open models at 72.7 billion parameters. This is a full-precision or minimally modified version of the weights, so running it locally requires substantial VRAM, typically 40GB or more even with quantization applied on top. Users interested in this model should understand that abliterated models lack standard safety filtering and should be used responsibly. The underlying Qwen2.5 72B architecture delivers strong performance across reasoning, coding, writing, and multilingual tasks.
244.3K downloads
51 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen 2.5
- Parameters
- 72.7B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-10-26
- License
- Other
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HuggingFace
How Much VRAM Does Qwen2.5 72B Instruct Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 31.9 GB | 41.9 GB | 30.90 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 32.8 GB | 42.9 GB | 31.81 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 36.4 GB | 46.5 GB | 35.44 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 44.6 GB | 54.7 GB | 43.62 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 52.8 GB | 62.8 GB | 51.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 61.0 GB | 71.0 GB | 59.98 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 73.7 GB | 83.7 GB | 72.71 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Qwen2.5 72B Instruct Abliterated?
Q4_K_M · 44.6 GBQwen2.5 72B Instruct Abliterated (Q4_K_M) requires 44.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 58+ GB is recommended. Using the full 33K context window can add up to 10.1 GB, bringing total usage to 54.7 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Qwen2.5 72B Instruct Abliterated?
Q4_K_M · 44.6 GB26 devices with unified memory can run Qwen2.5 72B Instruct Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (64 GB).
Runs great
— Plenty of headroom
Where to Download Qwen2.5 72B Instruct Abliterated
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen2.5 72B Instruct Abliterated need?
Qwen2.5 72B Instruct Abliterated requires 44.6 GB of VRAM at Q4_K_M, or 146.4 GB at BF16. Full 33K context adds up to 10.1 GB (54.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 72.7B × 4.8 bits ÷ 8 = 43.6 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11.1 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M44.6 GBQ4_K_M + full context54.7 GB- Can NVIDIA GeForce RTX 5090 run Qwen2.5 72B Instruct Abliterated?
Yes, at Q2_K (31.9 GB) or lower. Higher quantizations like Q3_K_S (32.8 GB) exceed the NVIDIA GeForce RTX 5090's 32 GB.
- What's the best quantization for Qwen2.5 72B Instruct Abliterated?
For Qwen2.5 72B Instruct Abliterated, Q4_K_M (44.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (52.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 31.9 GB.
VRAM requirement by quantization
Q2_K31.9 GBQ3_K_L38.2 GBQ4_K_S41.9 GBQ4_K_M ★44.6 GBQ6_K61.0 GBBF16146.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 72B Instruct Abliterated on a Mac?
Qwen2.5 72B Instruct Abliterated requires at least 31.9 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 72B Instruct Abliterated locally?
Yes — Qwen2.5 72B Instruct Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 44.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 72B Instruct Abliterated?
At Q4_K_M, Qwen2.5 72B Instruct Abliterated can reach ~99 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 44.6 × 0.65 = ~117 tok/s
Estimated speed at Q4_K_M (44.6 GB)
~117 tok/s~117 tok/s~99 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 72B Instruct Abliterated?
At Q4_K_M, the download is about 43.62 GB. The full-precision BF16 version is 145.41 GB. The smallest option (Q2_K) is 30.90 GB.
- Which GPUs can run Qwen2.5 72B Instruct Abliterated?
No single consumer GPU has enough VRAM to run Qwen2.5 72B Instruct Abliterated at Q4_K_M (44.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run Qwen2.5 72B Instruct Abliterated?
27 devices with unified memory can run Qwen2.5 72B Instruct Abliterated at Q4_K_M (44.6 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.