majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-2bit

Qwen3.6 35B-A3B - RotorQuant MLX 2-bit

2-bit weight-quantized MLX version of Qwen/Qwen3.6-35B-A3B with RotorQuant KV-cache quantization. Optimized for Apple Silicon inference via the MLX framework. RotorQuant delivers 5.3x faster prefill and 28% faster decode compared to TurboQuant. The most aggressive quantization, fitting the full model in the smallest possible footprint. Only 3B parameters are active per token despite 26B total, making this model significantly more efficient at inference time than its parameter count suggests.

Approximate model size: ~9 GB

Model Specifications

Property	Value
Base Model	Qwen/Qwen3.6-35B-A3B
Parameters	35 billion total (3 billion active per token)
Architecture	Mixture-of-Experts (MoE) (3B active per token)
Modality	Multimodal: image + video + text input, text output
License	Apache 2.0
Weight Quantization	2-bit (~9 GB)
KV-Cache Quantization	RotorQuant
Framework	MLX (Apple Silicon)

Quickstart

import mlx.core as mx
from mlx_lm import load, generate

model, tokenizer = load("majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-2bit")

prompt = "Describe this image in detail."
response = generate(model, tokenizer, prompt=prompt, max_tokens=512)
print(response)

For multimodal usage with images:

from mlx_vlm import load, generate

model, processor = load("majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-2bit")

prompt = "What do you see in this image?"
output = generate(model, processor, prompt=prompt, image="path/to/image.jpg", max_tokens=512)
print(output)

What is RotorQuant?

RotorQuant is a high-performance KV-cache quantization method that achieves significantly better throughput than TurboQuant. Combined with 2-bit weight quantization in MLX, this provides maximum compression with the best available KV-cache performance: the smallest possible model footprint plus the fastest compressed KV cache for efficient long-context generation.

Key advantages over TurboQuant:

• 5.3x faster prefill
• 28% faster decode
• Equivalent memory savings

Note: 2-bit quantization is the most aggressive option and may result in some quality degradation compared to higher-precision variants. It is best suited for experimentation, rapid prototyping, or hardware-constrained environments.

KV-Cache Quantization Comparison

Method	Prefill Speed	Decode Speed	Memory Savings	Reference
TurboQuant	1x (baseline)	1x (baseline)	High	arXiv: 2504.19874
RotorQuant	5.3x faster	28% faster	High	GitHub

Memory Estimates (Qwen3.6 35B-A3B)

Precision	Approximate Size	MLX Variant
FP16 (original)	~70 GB (approx.)	--
8-bit quantized	~35 GB	RotorQuant-MLX-8bit
4-bit quantized	~18 GB	RotorQuant-MLX-4bit
2-bit quantized	~9 GB	This model

Hardware Requirements

This model requires approximately 9 GB of unified memory. Recommended hardware:

• Apple M1 (16 GB+)
• Apple M2 (16 GB+)
• Apple M3 (16 GB+)
• Apple M4 (16 GB+)
• Any Apple Silicon Mac with 16 GB+ unified memory

See Also

• Qwen/Qwen3.6-35B-A3B -- Base model
• majentik/Qwen3.6-35B-A3B-RotorQuant -- RotorQuant KV-cache only (transformers)
• majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-8bit -- MLX 8-bit variant
• majentik/Qwen3.6-35B-A3B-RotorQuant-MLX-4bit -- MLX 4-bit variant
• majentik/Qwen3.6-35B-A3B-TurboQuant-MLX-2bit -- TurboQuant MLX 2-bit variant
• RotorQuant GitHub
• MLX Framework

Quant trade-off (MLX lane)

Bits	Approx size	Use case	Recommendation
2-bit	~9.1 GB	Aggressive quantization	Very low-RAM Macs
3-bit	~13 GB	Lossy but small	Low-RAM Macs
4-bit	~15 GB	Balanced default	Recommended for most Macs
5-bit	~18 GB	Higher fidelity	Quality-sensitive
6-bit	~21 GB	Approaching FP16 quality	High-fidelity
8-bit	~27 GB	Near-lossless reference	Fidelity-critical work

(Current variant — 2bit — is bolded.)

Variants in this family

(Showing 24 sibling variants under majentik/qwen3.6-35b-a3b-*. The current variant — RotorQuant-MLX-2bit — is bolded.)

Variant	Runtime	Approx size	Use case
RotorQuant	runtime modifier	n/a	KV-cache root (weight-agnostic)
RotorQuant-AWQ-4bit	transformers	~22 GB	GPU 4-bit (AutoAWQ)
RotorQuant-AWQ-8bit	transformers	~38 GB	GPU 8-bit (AutoAWQ)
RotorQuant-GGUF-IQ4_XS	llama.cpp	~30 GB	Lossy 4-bit, low-RAM CPU/edge
RotorQuant-GGUF-Q2_K	llama.cpp	~21 GB	Lossy, low-RAM CPU/edge
RotorQuant-GGUF-Q3_K_M	llama.cpp	~27 GB	Smaller 3-bit, CPU-friendly
RotorQuant-GGUF-Q4_K_M	llama.cpp	~38 GB	Balanced default
RotorQuant-GGUF-Q5_K_M	llama.cpp	~46 GB	Higher fidelity, more RAM
RotorQuant-GGUF-Q8_0	llama.cpp	~74 GB	Near-lossless reference
RotorQuant-MLX-2bit	mlx-lm	~11 GB	Apple Silicon, smallest
RotorQuant-MLX-3bit	mlx-lm	~16 GB	Apple Silicon, small
RotorQuant-MLX-4bit	mlx-lm	~22 GB	Apple Silicon balanced
RotorQuant-MLX-5bit	mlx-lm	~27 GB	Apple Silicon, higher fidelity
RotorQuant-MLX-6bit	mlx-lm	~32 GB	Apple Silicon, near-lossless
RotorQuant-MLX-8bit	mlx-lm	~41 GB	Apple Silicon reference
TurboQuant	runtime modifier	n/a	KV-cache root (weight-agnostic)
TurboQuant-AWQ-4bit	transformers	~22 GB	GPU 4-bit (AutoAWQ)
TurboQuant-AWQ-8bit	transformers	~38 GB	GPU 8-bit (AutoAWQ)
TurboQuant-MLX-2bit	mlx-lm	~11 GB	Apple Silicon, smallest
TurboQuant-MLX-3bit	mlx-lm	~16 GB	Apple Silicon, small
TurboQuant-MLX-4bit	mlx-lm	~22 GB	Apple Silicon balanced
TurboQuant-MLX-5bit	mlx-lm	~27 GB	Apple Silicon, higher fidelity
TurboQuant-MLX-6bit	mlx-lm	~32 GB	Apple Silicon, near-lossless
TurboQuant-MLX-8bit	mlx-lm	~41 GB	Apple Silicon reference