YTan2000·Qwen 3.6

Qwen3.6 27B MTP TQ3 4S — Hardware Requirements & GPU Compatibility

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Qwen3.6 27B MTP TQ3 4S is a 27B-parameter open language model from YTan2000 in the Qwen 3.6 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
YTan2000
Family
Qwen 3.6
Parameters
27B
Release Date
2026-06-18
License
Apache 2.0

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How Much VRAM Does Qwen3.6 27B MTP TQ3 4S 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_Mest.4.8017.8 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 Qwen3.6 27B MTP TQ3 4S?

Q4_K_M · 17.8 GB

Qwen3.6 27B MTP TQ3 4S (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 Qwen3.6 27B MTP TQ3 4S?

Q4_K_M · 17.8 GB

41 devices with unified memory can run Qwen3.6 27B MTP TQ3 4S, 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 Qwen3.6 27B MTP TQ3 4S need?

Qwen3.6 27B MTP TQ3 4S 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 Qwen3.6 27B MTP TQ3 4S?

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 Qwen3.6 27B MTP TQ3 4S?

For Qwen3.6 27B MTP TQ3 4S, 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 Qwen3.6 27B MTP TQ3 4S on a Mac?

Qwen3.6 27B MTP TQ3 4S 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 Qwen3.6 27B MTP TQ3 4S locally?

Yes — Qwen3.6 27B MTP TQ3 4S 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 Qwen3.6 27B MTP TQ3 4S?

At Q4_K_M, Qwen3.6 27B MTP TQ3 4S 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 Qwen3.6 27B MTP TQ3 4S?

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 Qwen3.6 27B MTP TQ3 4S?

8 consumer GPUs can run Qwen3.6 27B MTP TQ3 4S 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 Qwen3.6 27B MTP TQ3 4S?

41 devices with unified memory can run Qwen3.6 27B MTP TQ3 4S 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.