Qwen3.6 35B A3B DFlash — Hardware Requirements & GPU Compatibility
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Qwen3.6 35B A3B DFlash is a 35B-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 21.33 GB of VRAM — see which GPUs and Macs can run it below.
213.9K downloads
271 likesSpecifications
- Publisher
- z-lab
- Family
- Qwen 3.6
- Parameters
- 35B
- Architecture
- DFlashDraftModel
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-04-17
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3.6 35B A3B DFlash Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 15.2 GB | 18.4 GB | 14.88 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 17.4 GB | 20.6 GB | 17.06 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 21.3 GB | 24.5 GB | 21.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 25.3 GB | 28.5 GB | 24.94 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 29.2 GB | 32.4 GB | 28.88 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 35.3 GB | 38.5 GB | 35.00 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 70.3 GB | 73.5 GB | 70.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Qwen3.6 35B A3B DFlash?
Q4_K_M · 21.3 GBQwen3.6 35B A3B DFlash (Q4_K_M) requires 21.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. Using the full 262K context window can add up to 3.2 GB, bringing total usage to 24.5 GB. 7 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 35B A3B DFlash?
Q4_K_M · 21.3 GB41 devices with unified memory can run Qwen3.6 35B A3B 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 35B A3B 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 35B A3B DFlash need?
Qwen3.6 35B A3B DFlash requires 21.3 GB of VRAM at Q4_K_M, or 70.3 GB at BF16. Full 262K context adds up to 3.2 GB (24.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 35B × 4.8 bits ÷ 8 = 21 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M21.3 GBQ4_K_M + full context24.5 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.6 35B A3B DFlash?
Yes, at Q4_K_M (21.3 GB) or lower. Higher quantizations like Q5_K_M (25.3 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3.6 35B A3B DFlash?
For Qwen3.6 35B A3B DFlash, Q4_K_M (21.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (25.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 15.2 GB.
VRAM requirement by quantization
Q2_K15.2 GBQ4_K_M ★21.3 GBQ5_K_M25.3 GBQ6_K29.2 GBQ8_035.3 GBBF1670.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.6 35B A3B DFlash on a Mac?
Qwen3.6 35B A3B DFlash requires at least 15.2 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 35B A3B DFlash locally?
Yes — Qwen3.6 35B A3B DFlash can run locally on consumer hardware. At Q4_K_M quantization it needs 21.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.6 35B A3B DFlash?
At Q4_K_M, Qwen3.6 35B A3B DFlash can reach ~206 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~31 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 ÷ 21.3 × 0.65 = ~244 tok/s
Estimated speed at Q4_K_M (21.3 GB)
~244 tok/s~31 tok/s~244 tok/s~206 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 35B A3B DFlash?
At Q4_K_M, the download is about 21.00 GB. The full-precision BF16 version is 70.00 GB. The smallest option (Q2_K) is 14.88 GB.
- Which GPUs can run Qwen3.6 35B A3B DFlash?
7 consumer GPUs can run Qwen3.6 35B A3B DFlash at Q4_K_M (21.3 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.6 35B A3B DFlash?
41 devices with unified memory can run Qwen3.6 35B A3B DFlash at Q4_K_M (21.3 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.