Qwen3.5-27B-RotorQuant-MLX-8bit

MLX 8-bit weight-quantized variant of Qwen/Qwen3.5-27B with RotorQuant KV cache compression for efficient inference on Apple Silicon.

Overview

This model combines two complementary compression techniques:

• MLX 8-bit weight quantization — reduces model size from ~54GB to ~27GB
• RotorQuant KV cache compression — compresses key-value caches during inference using Clifford algebra block-diagonal rotations, enabling longer contexts with less VRAM

Quickstart

from mlx_lm import load, generate
from turboquant import IsoQuantCache

model, tokenizer = load("majentik/Qwen3.5-27B-RotorQuant-MLX-8bit")

# Standard generation
prompt = "Explain the theory of relativity"
response = generate(model, tokenizer, prompt=prompt, max_tokens=2048)
print(response)

Specifications

Property	Value
Base Model	Qwen/Qwen3.5-27B
Parameters	27B
Weight Quantization	MLX 8-bit affine
KV Cache Method	RotorQuant (Clifford algebra block-diagonal rotations)
Model Size	~27 GB
Context Length	262K (native), 1M+ (extended)
Platform	Apple Silicon (M1/M2/M3/M4/M5)

What is RotorQuant?

RotorQuant uses Clifford algebra block-diagonal rotations for KV cache quantization, achieving superior efficiency compared to vector-quantization-based approaches like TurboQuant:

Metric	RotorQuant	TurboQuant
Prefill speed	5.3x faster	baseline
Decode speed	28% faster	baseline
Quantizer parameters	44x fewer	baseline
Perplexity	6.91	7.07

RotorQuant compresses the KV cache to ~3-bit effective precision while maintaining lower perplexity than TurboQuant's 4-bit approach, thanks to the mathematical efficiency of geometric algebra rotations.

Thinking Mode

Qwen3.5-27B generates extended reasoning before responses by default. The combination of weight quantization and KV cache compression is especially valuable here — thinking tokens consume significant memory that is reduced by both techniques working together.

Memory Estimate

Configuration	Model Weights	KV Cache (128K ctx)	Total
FP16 (baseline)	~54 GB	~13 GB	~67 GB
MLX 8-bit + RotorQuant	~27 GB	~1.3 GB	~28.3 GB

See Also

• Base model
• KV-cache-only variant
• RotorQuant GitHub