Qwen3.6 27B DFlash — Hardware Requirements & GPU Compatibility
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Qwen3.6 27B DFlash is a 27B-parameter open language model from z-lab in the Qwen 3.6 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 16.55 GB of VRAM — see which GPUs and Macs can run it below.
69.9K downloads
382 likesSpecifications
- Publisher
- z-lab
- Family
- Qwen 3.6
- Parameters
- 27B
- Architecture
- DFlashDraftModel
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-04-23
- 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 |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 11.8 GB | 18.5 GB | 11.47 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 13.5 GB | 20.2 GB | 13.16 GB | 3-bit medium quantization |
| IQ4_XS | 4.30 | 14.9 GB | 21.5 GB | 14.51 GB | Importance-weighted 4-bit, compact |
| IQ4_NL | 4.50 | 15.5 GB | 22.2 GB | 15.19 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 16.6 GB | 23.2 GB | 16.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 19.6 GB | 26.3 GB | 19.24 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 22.6 GB | 29.3 GB | 22.27 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 27.4 GB | 34.0 GB | 27.00 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 54.4 GB | 61.0 GB | 54.00 GB | Brain floating point 16 — preferred for training |
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 DFlash?
Q4_K_M · 16.6 GBQwen3.6 27B DFlash (Q4_K_M) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 262K context window can add up to 6.7 GB, bringing total usage to 23.2 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Qwen3.6 27B DFlash?
Q4_K_M · 16.6 GB41 devices with unified memory can run Qwen3.6 27B DFlash, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen3.6 27B DFlash
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 Qwen3.6 27B DFlash need?
Qwen3.6 27B DFlash requires 16.6 GB of VRAM at Q4_K_M, or 54.4 GB at BF16. Full 262K context adds up to 6.7 GB (23.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27B × 4.8 bits ÷ 8 = 16.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M16.6 GBQ4_K_M + full context23.2 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.6 27B DFlash?
Yes, at Q6_K (22.6 GB) or lower. Higher quantizations like Q8_0 (27.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3.6 27B DFlash?
For Qwen3.6 27B DFlash, Q4_K_M (16.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (19.6 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 11.8 GB.
VRAM requirement by quantization
Q2_K11.8 GBIQ4_XS14.9 GBQ4_K_M ★16.6 GBQ5_K_M19.6 GBQ6_K22.6 GBBF1654.4 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 11.8 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 DFlash locally?
Yes — Qwen3.6 27B DFlash can run locally on consumer hardware. At Q4_K_M quantization it needs 16.6 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 ~266 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~40 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 ÷ 16.6 × 0.65 = ~314 tok/s
Estimated speed at Q4_K_M (16.6 GB)
~314 tok/s~40 tok/s~314 tok/s~266 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 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 DFlash?
8 consumer GPUs can run Qwen3.6 27B DFlash at Q4_K_M (16.6 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 DFlash?
41 devices with unified memory can run Qwen3.6 27B DFlash at Q4_K_M (16.6 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.