Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit — Hardware Requirements & GPU Compatibility
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Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit is a 34.7B-parameter open language model from MLX Community in the Qwen family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 21.18 GB of VRAM — see which GPUs and Macs can run it below.
18.0K downloads
21 likesSpecifications
- Publisher
- MLX Community
- Family
- Qwen
- Parameters
- 34.7B
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-04-23
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How Much VRAM Does Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 21.2 GB | 31.8 GB | 20.80 GB | 4-bit medium quantization — most popular sweet spot |
| Q6_K | 6.60 | 29.0 GB | 39.6 GB | 28.60 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 35.0 GB | 45.7 GB | 34.66 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit?
Q4_K_M · 21.2 GBHuihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit (Q4_K_M) requires 21.2 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 10.6 GB, bringing total usage to 31.8 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit?
Q4_K_M · 21.2 GB21 devices with unified memory can run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit need?
Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit requires 21.2 GB of VRAM at Q4_K_M, or 35.0 GB at Q8_0. Full 262K context adds up to 10.6 GB (31.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 34.7B × 4.8 bits ÷ 8 = 20.8 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M21.2 GBQ4_K_M + full context31.8 GB- Can NVIDIA GeForce RTX 4090 run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit?
Yes, at Q4_K_M (21.2 GB) or lower. Higher quantizations like Q6_K (29.0 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 MLX 8bit?
For Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit, Q4_K_M (21.2 GB) offers the best balance of quality and VRAM usage. Q6_K (29.0 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★21.2 GBQ6_K29.0 GBQ8_035.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit on a Mac?
Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit requires at least 21.2 GB at Q4_K_M, 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 MLX 8bit locally?
Yes — Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit can run locally on consumer hardware. At Q4_K_M quantization it needs 21.2 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 MLX 8bit?
At Q4_K_M, Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit can reach ~138 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 21.2 × 0.55 = ~138 tok/s
Estimated speed at Q4_K_M (21.2 GB)
~138 tok/s~31 tok/s~103 tok/s~85 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 MLX 8bit?
At Q4_K_M, the download is about 20.80 GB. The full-precision Q8_0 version is 34.66 GB.
- Which GPUs can run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit?
5 consumer GPUs can run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit at Q4_K_M (21.2 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 Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit?
21 devices with unified memory can run Huihui Qwen3.6 35B A3B Claude 4.7 Opus Abliterated MLX 8bit at Q4_K_M (21.2 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.