Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark — Hardware Requirements & GPU Compatibility
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Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark is a 27.8B-parameter open language model from DreamFast in the Qwen 3.6 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 17.42 GB of VRAM — see which GPUs and Macs can run it below.
937 downloads
5 likesSpecifications
- Publisher
- DreamFast
- Family
- Qwen 3.6
- Parameters
- 27.8B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-04-30
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 12.6 GB | 69.4 GB | 11.81 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 14.3 GB | 71.1 GB | 13.54 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 17.4 GB | 74.2 GB | 16.67 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 20.5 GB | 77.4 GB | 19.79 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 23.7 GB | 80.5 GB | 22.92 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 28.5 GB | 85.3 GB | 27.78 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 56.3 GB | 113.1 GB | 55.56 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 Uncensored HauhauCS Aggressive Safetensor Benchmark?
Q4_K_M · 17.4 GBQwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark (Q4_K_M) requires 17.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 262K context window can add up to 56.8 GB, bringing total usage to 74.2 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 Uncensored HauhauCS Aggressive Safetensor Benchmark?
Q4_K_M · 17.4 GB41 devices with unified memory can run Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark need?
Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark requires 17.4 GB of VRAM at Q4_K_M, or 56.3 GB at BF16. Full 262K context adds up to 56.8 GB (74.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27.8B × 4.8 bits ÷ 8 = 16.7 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M17.4 GBQ4_K_M + full context74.2 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark?
Yes, at Q6_K (23.7 GB) or lower. Higher quantizations like Q8_0 (28.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark?
For Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark, Q4_K_M (17.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (20.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 12.6 GB.
VRAM requirement by quantization
Q2_K12.6 GBQ4_K_M ★17.4 GBQ5_K_M20.5 GBQ6_K23.7 GBQ8_028.5 GBBF1656.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark on a Mac?
Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark 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 Uncensored HauhauCS Aggressive Safetensor Benchmark locally?
Yes — Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark can run locally on consumer hardware. At Q4_K_M quantization it needs 17.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark?
At Q4_K_M, Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark can reach ~253 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~38 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 ÷ 17.4 × 0.65 = ~299 tok/s
Estimated speed at Q4_K_M (17.4 GB)
~299 tok/s~38 tok/s~299 tok/s~253 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 Uncensored HauhauCS Aggressive Safetensor Benchmark?
At Q4_K_M, the download is about 16.67 GB. The full-precision BF16 version is 55.56 GB. The smallest option (Q2_K) is 11.81 GB.
- Which GPUs can run Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark?
8 consumer GPUs can run Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark at Q4_K_M (17.4 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 Uncensored HauhauCS Aggressive Safetensor Benchmark?
41 devices with unified memory can run Qwen3.6 27B Uncensored HauhauCS Aggressive Safetensor Benchmark at Q4_K_M (17.4 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.