Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated — Hardware Requirements & GPU Compatibility
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Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated is a 36.0B-parameter open language model from huihui-ai in the Qwen 3.6 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 21.95 GB of VRAM — see which GPUs and Macs can run it below.
55.3K downloads
166 likesSpecifications
- Publisher
- huihui-ai
- Family
- Qwen 3.6
- Parameters
- 36.0B
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-04-21
- License
- Apache 2.0
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How Much VRAM Does Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 15.7 GB | 26.3 GB | 15.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 16.1 GB | 26.8 GB | 15.73 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 17.9 GB | 28.6 GB | 17.53 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 21.9 GB | 32.6 GB | 21.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 26 GB | 36.6 GB | 25.62 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 30.0 GB | 40.7 GB | 29.66 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 36.3 GB | 47.0 GB | 35.95 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 Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated?
Q4_K_M · 21.9 GBHuihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated (Q4_K_M) requires 21.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 29+ GB is recommended. Using the full 262K context window can add up to 10.7 GB, bringing total usage to 32.6 GB. 7 GPUs 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).
Which Devices Can Run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated?
Q4_K_M · 21.9 GB41 devices with unified memory can run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated, 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 Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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Frequently Asked Questions
- How much VRAM does Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated need?
Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated requires 21.9 GB of VRAM at Q4_K_M, or 72.3 GB at BF16. Full 262K context adds up to 10.7 GB (32.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 36.0B × 4.8 bits ÷ 8 = 21.6 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M21.9 GBQ4_K_M + full context32.6 GB- Can NVIDIA GeForce RTX 4090 run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated?
Yes, at Q4_K_M (21.9 GB) or lower. Higher quantizations like Q5_K_S (25.1 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated?
For Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated, Q4_K_M (21.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (25.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 15.7 GB.
VRAM requirement by quantization
Q2_K15.7 GBQ3_K_L18.8 GBQ4_K_M ★21.9 GBQ5_K_S25.1 GBQ5_K_M26.0 GBBF1672.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated on a Mac?
Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated requires at least 15.7 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 Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated locally?
Yes — Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated can run locally on consumer hardware. At Q4_K_M quantization it needs 21.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated?
At Q4_K_M, Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated can reach ~201 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~30 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 ÷ 21.9 × 0.65 = ~237 tok/s
Estimated speed at Q4_K_M (21.9 GB)
~237 tok/s~30 tok/s~237 tok/s~201 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated?
At Q4_K_M, the download is about 21.57 GB. The full-precision BF16 version is 71.90 GB. The smallest option (Q2_K) is 15.28 GB.
- Which GPUs can run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated?
7 consumer GPUs can run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated at Q4_K_M (21.9 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090.
- Which devices can run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated?
41 devices with unified memory can run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated at Q4_K_M (21.9 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.