hotdogs·Qwen

Qwen27b Abliterated Fable MTP — Hardware Requirements & GPU Compatibility

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Qwen27b Abliterated Fable MTP is a 27B-parameter open language model from hotdogs in the Qwen family. At Q4_K_M it needs about 17.82 GB of VRAM — see which GPUs and Macs can run it below.

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Specifications

Publisher
hotdogs
Family
Qwen
Parameters
27B
Release Date
2026-07-02
License
agpl-3.0

Get Started

How Much VRAM Does Qwen27b Abliterated Fable MTP Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q2_Kest.3.4012.6 GB
Q3_K_Mest.3.9014.5 GB
Q4_K_M4.8017.8 GB
Q5_K_Mest.5.7021.2 GB
Q6_Kest.6.6024.5 GB
Q8_0est.8.0029.7 GB
BF16est.16.0059.4 GB

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 Qwen27b Abliterated Fable MTP?

Q4_K_M · 17.8 GB

Qwen27b Abliterated Fable MTP (Q4_K_M) requires 17.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 24+ GB is recommended. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.

Which Devices Can Run Qwen27b Abliterated Fable MTP?

Q4_K_M · 17.8 GB

41 devices with unified memory can run Qwen27b Abliterated Fable MTP, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).

Runs great

Plenty of headroom

Related Models

Frequently Asked Questions

How much VRAM does Qwen27b Abliterated Fable MTP need?

Qwen27b Abliterated Fable MTP requires 17.8 GB of VRAM at Q4_K_M, or 59.4 GB at BF16.

VRAM = Weights + KV Cache + Overhead

Weights = 27B × 4.8 bits ÷ 8 = 16.2 GB

KV Cache + Overhead 1.6 GB (at 2K context + ~0.3 GB framework)

VRAM usage by quantization

17.8 GB

Learn more about VRAM estimation →

Can NVIDIA GeForce RTX 4090 run Qwen27b Abliterated Fable MTP?

Yes, at Q5_K_M (21.2 GB) or lower. Higher quantizations like Q6_K (24.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.

What's the best quantization for Qwen27b Abliterated Fable MTP?

For Qwen27b Abliterated Fable MTP, Q4_K_M (17.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (21.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 12.6 GB.

VRAM requirement by quantization

Q2_K
12.6 GB
Q4_K_M
17.8 GB
Q5_K_M
21.2 GB
Q6_K
24.5 GB
Q8_0
29.7 GB
BF16
59.4 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Qwen27b Abliterated Fable MTP on a Mac?

Qwen27b Abliterated Fable MTP requires at least 12.6 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 Qwen27b Abliterated Fable MTP locally?

Yes — Qwen27b Abliterated Fable MTP can run locally on consumer hardware. At Q4_K_M quantization it needs 17.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwen27b Abliterated Fable MTP?

At Q4_K_M, Qwen27b Abliterated Fable MTP can reach ~247 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~37 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 B2008000 ÷ 17.8 × 0.65 = ~292 tok/s

Estimated speed at Q4_K_M (17.8 GB)

~292 tok/s
~37 tok/s
~292 tok/s
~247 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Qwen27b Abliterated Fable MTP?

At Q4_K_M, the download is about 16.20 GB. The full-precision BF16 version is 54.00 GB. The smallest option (Q2_K) is 11.47 GB.

Which GPUs can run Qwen27b Abliterated Fable MTP?

8 consumer GPUs can run Qwen27b Abliterated Fable MTP at Q4_K_M (17.8 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.

Which devices can run Qwen27b Abliterated Fable MTP?

41 devices with unified memory can run Qwen27b Abliterated Fable MTP at Q4_K_M (17.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.