unsloth / image-text-to-text updated 2 months ago

Qwen3.6-27B-MTP-GGUF

Name: Qwen3.6-27B-MTP-GGUF
Author: unsloth

See Unsloth Dynamic 2.0 GGUFs for our quantization benchmarks. MTP enables ~1.5-2x faster inference with no accuracy loss. You can now run Qwen3.6 MTP GGUFs in Unsloth Studio Unsloth Studio auto sets the ideal MTP settings for your hardware (Mac, CPU, GPU):

Params

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Context

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Downloads 30d

1.2 M

Likes

1,262

Commercial use: allowed apache-2.0 Not gated GGUF View on Hugging Face ↗

Download history

daily snapshots · 10 days

3.0 M1.2 M

Jul 28Jul 31Aug 3Aug 6

Can you run it?

Estimated VRAM at 8K context unless noted. Pick your hardware to see the verdict per quantization.

File	Quant	Size	Est. VRAM	Verdict on RTX 4090 · 24 GB
Qwen3.6-27B-Q3_K_S.gguf	Q3_K_S	12.6 GB	14.3 GB	✅ Runs comfortably
Qwen3.6-27B-Q3_K_M.gguf	Q3_K_M	13.8 GB	15.7 GB	✅ Runs comfortably
Qwen3.6-27B-IQ4_XS.gguf	IQ4_XS	15.7 GB	17.8 GB	✅ Runs comfortably
Qwen3.6-27B-Q4_0.gguf	Q4_0	16.1 GB	18.2 GB	✅ Runs comfortably
Qwen3.6-27B-IQ4_NL.gguf	IQ4_NL	16.3 GB	18.5 GB	✅ Runs comfortably
Qwen3.6-27B-Q4_K_M.gguf	Q4_K_M	17.1 GB	19.3 GB	✅ Runs comfortably
Qwen3.6-27B-Q4_1.gguf	Q4_1	17.5 GB	19.8 GB	⚠️ Tight — reduce context
Qwen3.6-27B-BF16-00001-of-00002.gguf	GGUF	49.9 GB	55.4 GB	❌ Won’t fit

Estimate: file size × 1.1 + KV cache at 8K + 0.5 GB overhead. Not a benchmark — how we calculate this.

Specifications

Licence: apache-2.0
First seen on the Hub: 2026-05-11
Base model: Qwen3.6-27B
Training datasets: undisclosed
Added to our catalog: 2026-07-28

Family

Base model and the most-downloaded derivatives in the catalog.

Base Qwen3.6-27B Qwen 7.0 M

Compare with

Qwen3.5-9B 12.4 M gemma-4-31B-it 11.9 M gemma-4-26B-A4B-it 11.7 M Qwen2.5-VL-7B-Instruct 9.3 M Qwen3.6-35B-A3B-FP8 8.9 M Qwen2.5-VL-3B-Instruct 8.2 M

Appears in

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