Qwen3.6 27B DFlash — Hardware Requirements & GPU Compatibility
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Qwen3.6 27B DFlash is a 1.7B-parameter open language model from z-lab in the Qwen family. At Q4_K_M it needs about 1.14 GB of VRAM — see which GPUs and Macs can run it below.
76.5K downloads
340 likesSpecifications
- Publisher
- z-lab
- Family
- Qwen
- Parameters
- 1.7B
- Release Date
- 2026-04-27
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does Qwen3.6 27B DFlash Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ4_XS | 4.30 | 1.0 GB | — | 0.93 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 1.1 GB | — | 0.97 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 1.1 GB | — | 1.04 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.4 GB | — | 1.23 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.6 GB | — | 1.43 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.9 GB | — | 1.73 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3.6 27B DFlash?
Q4_K_M · 1.1 GBQwen3.6 27B DFlash (Q4_K_M) requires 1.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3.6 27B DFlash?
Q4_K_M · 1.1 GB33 devices with unified memory can run Qwen3.6 27B DFlash, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Qwen3.6 27B DFlash need?
Qwen3.6 27B DFlash requires 1.1 GB of VRAM at Q4_K_M, or 1.9 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 1.7B × 4.8 bits ÷ 8 = 1 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.1 GB- What's the best quantization for Qwen3.6 27B DFlash?
For Qwen3.6 27B DFlash, Q4_K_M (1.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.4 GB) provides better quality if you have the VRAM. The smallest option is IQ4_XS at 1.0 GB.
VRAM requirement by quantization
IQ4_XS1.0 GBIQ4_NL1.1 GBQ4_K_M ★1.1 GBQ5_K_M1.4 GBQ6_K1.6 GBQ8_01.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.6 27B DFlash on a Mac?
Qwen3.6 27B DFlash requires at least 1.0 GB at IQ4_XS, 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 DFlash locally?
Yes — Qwen3.6 27B DFlash can run locally on consumer hardware. At Q4_K_M quantization it needs 1.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.6 27B DFlash?
At Q4_K_M, Qwen3.6 27B DFlash can reach ~2557 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~575 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 1.1 × 0.55 = ~2557 tok/s
Estimated speed at Q4_K_M (1.1 GB)
~2557 tok/s~575 tok/s~1911 tok/s~1581 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3.6 27B DFlash?
At Q4_K_M, the download is about 1.04 GB. The full-precision Q8_0 version is 1.73 GB. The smallest option (IQ4_XS) is 0.93 GB.
- Which GPUs can run Qwen3.6 27B DFlash?
35 consumer GPUs can run Qwen3.6 27B DFlash at Q4_K_M (1.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.6 27B DFlash?
33 devices with unified memory can run Qwen3.6 27B DFlash at Q4_K_M (1.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.