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.
Qwen2.5 72B Instruct Abliterated runs on a 64 GB+ Mac or a workstation GPU
Best local pick → Mac Pro M2 Ultra (64 GB) · Q4_K_M · 49.3 GB · ~13 tok/s
Macs from 48 GB (Q3_K_L · ~11 tok/s)
Other
Not set up for it?
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwen2.5 72B Instruct Abliterated
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/Qwen2.5-72B-Instruct-abliterated-GGUF:Q4_K_MQ4_K_M · needs ~49.3 GB · 16K context
Download: Qwen2.5-72B-Instruct-abliterated.Q4_K_M.gguf47.42 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 96 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on A100 80GB (30 GB headroom) · Q4_K_M
- Generation speed
- ~30 tok/s
- generation speed
- Cost per 1M output tokens
- $10.62
- per 1M output tokens
How Much VRAM Does Qwen2.5 72B Instruct Abliterated Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 31.9 GB | 41.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Does not fit: needs 36.6 GB | |
| 32.8 GB | 42.9 GB | 3-bit small — 3-bit small quantization | Does not fit: needs 37.5 GB | |
| 36.4 GB | 46.5 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 41.1 GB | |
| 38.2 GB | 48.3 GB | 3-bit large — 3-bit large quantization | Does not fit: needs 42.9 GB | |
| 44.6 GB | 54.7 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 49.3 GB | |
| 52.8 GB | 62.8 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 57.5 GB | |
| 61.0 GB | 71 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 65.7 GB | |
| 73.7 GB | 83.8 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 78.4 GB |
Selected Q4_K_M
4-bit medium quantization49.3GBat 16K context
- Weights
- 43.6 GB
- KV cache
- 5.4 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 25.3 GB more
Or .
Which Hardware 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 consumer GPU has enough memory. 52 devices with unified memory can run Qwen2.5 72B Instruct Abliterated, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Pro M2 Ultra (64 GB).
64–80 GB
fits · 14.7 GB free · up to 32K ctx
3 Mac classes · 1 device family
M2 Ultra*64 GB~13Simulate output on Mac Pro M2 Ultra (64 GB)M5 Max64 GB~10Simulate output on Mac Studio M5 Max (64 GB)Orin~3Simulate output on NVIDIA Jetson AGX Orin 64GB
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Qwen2.5 72B Instruct Abliterated
Qwen 2.5 family · 44 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Llama 3.3 70B InstructMeta | 80.0 | 51.3 GB | —needs > 24 GB | same size |
| NVIDIA Nemotron 3.5 Bolt 74B A9B BF16NVIDIA | — | 54.2 GB | —needs > 24 GB | same size |
| Kolibri 1 BF16Aleph-Alpha | — | 22.5 GB | ~237 | same size |
| Qwen2 VL 72B InstructAlibaba | — | 49.7 GB | —needs > 24 GB | same size |
| Qwen1.5 72B ChatAlibaba | — | 86.6 GB | —needs > 24 GB | same size |
| CodeLlama 70B Instruct HFMeta | — | 23.2 GB | ~29 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 11.1 GB (at full 33K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwen2.5 72B Instruct Abliterated on a Mac?
Yes — Mac Studio M5 Max (48 GB) and 55 other Macs can run Qwen2.5 72B Instruct Abliterated, the smallest at Q3_K_L. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 44.6 GB of usable unified memory (RAM minus macOS overhead), from Mac Pro M2 Ultra (64 GB).
Can I run Qwen2.5 72B Instruct Abliterated locally?
Yes — Qwen2.5 72B Instruct Abliterated can run locally on consumer hardware. 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 ~108 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)
Real-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?
52 devices with unified memory can run Qwen2.5 72B Instruct Abliterated at Q4_K_M (44.6 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.