Qwen2.5 3B Instruct Abliterated — Hardware Requirements & GPU Compatibility
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Qwen2.5 3B Instruct Abliterated is a 3.1B-parameter open language model from huihui-ai in the Qwen 2.5 family. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 2.23 GB of VRAM — see which GPUs and Macs can run it below.
1.0K downloads
13 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen 2.5
- Parameters
- 3.1B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2024-11-03
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen2.5 3B Instruct Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.7 GB | 2.8 GB | 1.31 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.7 GB | 2.9 GB | 1.35 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.9 GB | 3.0 GB | 1.50 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 2.2 GB | 3.4 GB | 1.85 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 2.6 GB | 3.7 GB | 2.20 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 2.9 GB | 4.0 GB | 2.55 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 3.5 GB | 4.6 GB | 3.09 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 3B Instruct Abliterated?
Q4_K_M · 2.2 GBQwen2.5 3B Instruct Abliterated (Q4_K_M) requires 2.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 33K context window can add up to 1.1 GB, bringing total usage to 3.4 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen2.5 3B Instruct Abliterated?
Q4_K_M · 2.2 GB59 devices with unified memory can run Qwen2.5 3B Instruct Abliterated, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Qwen2.5 3B 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 3B Instruct Abliterated need?
Qwen2.5 3B Instruct Abliterated requires 2.2 GB of VRAM at Q4_K_M, or 6.5 GB at BF16. Full 33K context adds up to 1.1 GB (3.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.1B × 4.8 bits ÷ 8 = 1.9 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.5 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M2.2 GBQ4_K_M + full context3.4 GB- What's the best quantization for Qwen2.5 3B Instruct Abliterated?
For Qwen2.5 3B Instruct Abliterated, Q4_K_M (2.2 GB) offers the best balance of quality and VRAM usage. Q5_K_S (2.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.7 GB.
VRAM requirement by quantization
Q2_K1.7 GBQ3_K_L2.0 GBQ4_K_M ★2.2 GBQ5_K_S2.5 GBQ5_K_M2.6 GBBF166.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen2.5 3B Instruct Abliterated on a Mac?
Qwen2.5 3B Instruct Abliterated requires at least 1.7 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 3B Instruct Abliterated locally?
Yes — Qwen2.5 3B Instruct Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 2.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen2.5 3B Instruct Abliterated?
At Q4_K_M, Qwen2.5 3B Instruct Abliterated can reach ~1973 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~294 tok/s. 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 ÷ 2.2 × 0.65 = ~2332 tok/s
Estimated speed at Q4_K_M (2.2 GB)
~2332 tok/s~294 tok/s~2332 tok/s~1973 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 3B Instruct Abliterated?
At Q4_K_M, the download is about 1.85 GB. The full-precision BF16 version is 6.17 GB. The smallest option (Q2_K) is 1.31 GB.
- Which GPUs can run Qwen2.5 3B Instruct Abliterated?
50 consumer GPUs can run Qwen2.5 3B Instruct Abliterated at Q4_K_M (2.2 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen2.5 3B Instruct Abliterated?
59 devices with unified memory can run Qwen2.5 3B Instruct Abliterated at Q4_K_M (2.2 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.