Qwen3.6 40B Deckard MTP — Hardware Requirements & GPU Compatibility
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Qwen3.6 40B Deckard MTP is a 40B-parameter open language model from PiehSoft in the Qwen 3.6 family. At Q4_K_M it needs about 26.40 GB of VRAM — see which GPUs and Macs can run it below.
3.2K downloads
17 likesSpecifications
- Publisher
- PiehSoft
- Family
- Qwen 3.6
- Parameters
- 40B
- Release Date
- 2026-05-28
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3.6 40B Deckard MTP Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 18.7 GB | — | 17.00 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 21.4 GB | — | 19.50 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 26.4 GB | — | 24.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 31.4 GB | — | 28.50 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 36.3 GB | — | 33.00 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 44 GB | — | 40.00 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 88 GB | — | 80.00 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 40B Deckard MTP?
Q4_K_M · 26.4 GBQwen3.6 40B Deckard MTP (Q4_K_M) requires 26.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 35+ GB is recommended. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen3.6 40B Deckard MTP?
Q4_K_M · 26.4 GB31 devices with unified memory can run Qwen3.6 40B Deckard MTP, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3.6 40B Deckard MTP need?
Qwen3.6 40B Deckard MTP requires 26.4 GB of VRAM at Q4_K_M, or 88 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 40B × 4.8 bits ÷ 8 = 24 GB
KV Cache + Overhead ≈ 2.4 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M26.4 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.6 40B Deckard MTP?
Yes, at Q3_K_M (21.4 GB) or lower. Higher quantizations like Q4_K_M (26.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3.6 40B Deckard MTP?
For Qwen3.6 40B Deckard MTP, Q4_K_M (26.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (31.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 18.7 GB.
VRAM requirement by quantization
Q2_K18.7 GBQ4_K_M ★26.4 GBQ5_K_M31.4 GBQ6_K36.3 GBQ8_044.0 GBBF1688.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.6 40B Deckard MTP on a Mac?
Qwen3.6 40B Deckard MTP requires at least 18.7 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 40B Deckard MTP locally?
Yes — Qwen3.6 40B Deckard MTP can run locally on consumer hardware. At Q4_K_M quantization it needs 26.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.6 40B Deckard MTP?
At Q4_K_M, Qwen3.6 40B Deckard MTP can reach ~167 tok/s on AMD Instinct MI350X. 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 ÷ 26.4 × 0.65 = ~197 tok/s
Estimated speed at Q4_K_M (26.4 GB)
~197 tok/s~197 tok/s~167 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 40B Deckard MTP?
At Q4_K_M, the download is about 24.00 GB. The full-precision BF16 version is 80.00 GB. The smallest option (Q2_K) is 17.00 GB.
- Which GPUs can run Qwen3.6 40B Deckard MTP?
1 consumer GPU can run Qwen3.6 40B Deckard MTP at Q4_K_M (26.4 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Qwen3.6 40B Deckard MTP?
35 devices with unified memory can run Qwen3.6 40B Deckard MTP at Q4_K_M (26.4 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.