Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop — Hardware Requirements & GPU Compatibility
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Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop is a 12.1B-parameter open language model from DavidAU in the Qwen 3.6 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 7.75 GB of VRAM — see which GPUs and Macs can run it below.
1.1K downloads
24 likesSpecifications
- Publisher
- DavidAU
- Family
- Qwen 3.6
- Parameters
- 12.1B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-05-15
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 5.6 GB | 26.9 GB | 5.16 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 5.8 GB | 27.1 GB | 5.31 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 6.4 GB | 27.7 GB | 5.92 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 6.5 GB | 27.8 GB | 6.07 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 7.8 GB | 29.1 GB | 7.28 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 9.1 GB | 30.4 GB | 8.65 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 10.5 GB | 31.8 GB | 10.01 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 12.6 GB | 33.9 GB | 12.14 GB | 8-bit quantization, near-lossless |
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 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop?
Q4_K_M · 7.8 GBQwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop (Q4_K_M) requires 7.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. Using the full 262K context window can add up to 21.3 GB, bringing total usage to 29.1 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop?
Q4_K_M · 7.8 GB33 devices with unified memory can run Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop
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 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop need?
Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop requires 7.8 GB of VRAM at Q4_K_M, or 24.7 GB at BF16. Full 262K context adds up to 21.3 GB (29.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 12.1B × 4.8 bits ÷ 8 = 7.3 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 21.8 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M7.8 GBQ4_K_M + full context29.1 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop?
Yes, at Q8_0 (12.6 GB) or lower. Higher quantizations like BF16 (24.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop?
For Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop, Q4_K_M (7.8 GB) offers the best balance of quality and VRAM usage. Q5_0 (8.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 5.6 GB.
VRAM requirement by quantization
Q2_K5.6 GBQ3_K_M6.4 GBIQ4_NL7.3 GBQ4_K_M ★7.8 GBQ5_K_S8.8 GBBF1624.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop on a Mac?
Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop requires at least 5.6 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 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop locally?
Yes — Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop can run locally on consumer hardware. At Q4_K_M quantization it needs 7.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop?
At Q4_K_M, Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop can reach ~376 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~85 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 ÷ 7.8 × 0.55 = ~376 tok/s
Estimated speed at Q4_K_M (7.8 GB)
~376 tok/s~85 tok/s~281 tok/s~233 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 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop?
At Q4_K_M, the download is about 7.28 GB. The full-precision BF16 version is 24.27 GB. The smallest option (Q2_K) is 5.16 GB.
- Which GPUs can run Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop?
35 consumer GPUs can run Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop at Q4_K_M (7.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop?
33 devices with unified memory can run Qwen3.6 12B IQ Ultra Heretic Uncensored Thinking v2 Hightop at Q4_K_M (7.8 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.