Qwen3.5-27B-RotorQuant-2bit

2-bit KV cache compression for Qwen/Qwen3.5-27B using RotorQuant.

This is a KV-cache-only repository. It contains no model weight files — only the configuration and model card for applying RotorQuant 2-bit KV cache quantization at runtime on the original Qwen3.5-27B weights.

Overview

Qwen3.5-27B is a 27B-parameter hybrid transformer with 262K native context and built-in thinking mode (the model generates internal reasoning tokens before answering). Thinking mode makes KV cache compression especially valuable, since the reasoning chain can consume substantial cache memory.

RotorQuant applies rotation-based isotropic quantization to the KV cache, achieving better quality and speed than standard quantization approaches at the same bit width.

RotorQuant Advantages

Metric	RotorQuant 2-bit	Standard 2-bit
Prefill speed	5.3x faster	Baseline
Decode speed	28% faster	Baseline
Perplexity	6.91	7.07

RotorQuant achieves lower perplexity (better quality) while also being faster — a rare combination at aggressive quantization levels.

Specifications

Property	Value
Base model	Qwen/Qwen3.5-27B
Parameters	27B
Architecture	Hybrid Transformer
Native context	262,144 tokens
Thinking mode	Yes
KV cache method	RotorQuant 2-bit (IsoQuant)
KV cache compression	~10x vs FP16
Weights	Original (FP16/BF16, loaded separately)

Memory Estimates

Component	Estimate
Model weights (BF16)	~54 GB
KV cache at 128K context (2-bit RotorQuant)	~1.3 GB
KV cache at 128K context (FP16, baseline)	~12.8 GB

Quickstart

from transformers import AutoModelForCausalLM, AutoTokenizer
from turboquant import IsoQuantCache

model_id = "Qwen/Qwen3.5-27B"

tokenizer = AutoTokenizer.from_pretrained(model_id)
model = AutoModelForCausalLM.from_pretrained(model_id, torch_dtype="auto", device_map="auto")

# Apply 2-bit RotorQuant KV cache compression
cache = IsoQuantCache(bits=2)

messages = [{"role": "user", "content": "Explain the Riemann hypothesis in simple terms."}]
inputs = tokenizer.apply_chat_template(messages, return_tensors="pt").to(model.device)

outputs = model.generate(
    inputs,
    max_new_tokens=2048,
    past_key_values=cache,
)
print(tokenizer.decode(outputs[0], skip_special_tokens=True))

Quality Notes

• 2-bit is aggressive quantization, but RotorQuant's rotation-based approach preserves more quality than standard methods (perplexity 6.91 vs 7.07).
• Best suited for memory-constrained scenarios where fitting long-context inference on limited hardware is essential.
• For higher quality with moderate compression, consider 4-bit KV cache variants.
• Thinking mode reasoning quality may be more sensitive to cache quantization since the model relies on cached reasoning tokens for its final answer.

References

• RotorQuant — Rotation-based isotropic KV cache quantization
• Qwen3.5-27B base model

See Also

• majentik/Qwen3.5-27B-TurboQuant-2bit — TurboQuant 2-bit KV cache variant
• majentik/Qwen3.5-27B-TurboQuant-MLX-2bit — MLX 2-bit weights + TurboQuant KV cache
• majentik/Qwen3.5-27B-RotorQuant-MLX-2bit — MLX 2-bit weights + RotorQuant KV cache