majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS

[!TIP] KV-cache quantization without any fork (recommended, 2026): upstream llama.cpp/Ollama now cover this natively — use -ctk q8_0 -ctv q8_0 (~half KV memory, negligible quality loss: perplexity +0.002–0.05) or -ctk q4_0 -ctv q4_0 (~quarter memory, ≈7.6% perplexity increase). In Ollama: OLLAMA_KV_CACHE_TYPE=q8_0 with OLLAMA_FLASH_ATTENTION=1. Keep K and V types symmetric to stay on the fast fused Flash-Attention path. Since April 2026, mainline llama.cpp also applies Hadamard rotation to KV activations (PR #21038), which greatly improves low-bit KV quality (opt-out: LLAMA_ATTN_ROT_DISABLE=1). The RotorQuant/TurboQuant fork flow below is experimental/legacy: the TurboQuant llama.cpp PR was closed without merging (June 2026) and the fork is unmaintained relative to mainline. It is NOT required to use this model.

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Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS

GGUF IQ4_XS weight-quantized variant of Qwen/Qwen3.5-27B optimised for use with RotorQuant KV cache compression via a dedicated llama.cpp fork.

Important: RotorQuant KV cache types (planar3, iso3) are not available in upstream llama.cpp, standard Ollama, or LM Studio. They require a specific llama.cpp fork. The GGUF file itself is a standard GGUF and works with any llama.cpp-compatible runtime using normal KV cache types (f16, q80, q40, etc.).

Hardware compatibility

Overview

This model combines two independent compression techniques:

Quickstart

llama.cpp / LM Studio / Ollama (upstream)

The GGUF works as a normal quantised model. For KV-cache savings use the upstream path described in the tip at the top of this card (-ctk q8_0 -ctv q8_0, or q4_0); no fork is required.

llama.cpp (upstream)

llama-completion -no-cnv -m Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS.gguf \
  --cache-type-k q8_0 --cache-type-v q8_0 \
  -ngl 99 -fa \
  -p "Explain quantum computing"

LM Studio

1. 1.Download the GGUF file and load in LM Studio.
2. 2.Enable Developer Mode (Settings → Developer).
3. 3.In the model loader's advanced settings, set Flash Attention to ON.
4. 4.Set K Cache Quantization and V Cache Quantization to q8_0 (or q4_0 for more aggressive VRAM savings).
5. 5.Note: LM Studio does not currently support RotorQuant's iso3 cache types. Track this feature request for updates.

Ollama

# Standard Ollama does not support RotorQuant cache types.
# Use with default or q8_0 KV cache via OLLAMA_KV_CACHE_TYPE=q8_0
OLLAMA_KV_CACHE_TYPE=q8_0 OLLAMA_FLASH_ATTENTION=1 ollama run majentik/Qwen3.5-27B-RotorQuant-GGUF-IQ4_XS

Specifications

About the RotorQuant / TurboQuant labels

RotorQuant and TurboQuant are this project's release labels, not distinct quantization algorithms — for any given tier, both brand repos carry byte-identical weights produced with the standard MLX / llama.cpp quantizers. No brand-specific speedup is claimed or measured. The KV-cache fork these labels originally referred to is legacy; for KV-cache memory savings use the upstream options described above (-ctk/-ctv q8_0, OLLAMA_KV_CACHE_TYPE).

Current Status of RotorQuant in the Ecosystem

Recommended Settings

For VRAM-constrained setups, standard q8_0 KV cache quantization already halves KV cache memory with negligible quality impact. Flash Attention should always be enabled — it is required for V cache quantization and improves memory efficiency regardless.

See Also

• —RotorQuant GitHub
• —llama-cpp-turboquant fork
• —TurboQuant llama.cpp discussion
• —TurboQuant paper (arXiv: 2504.19874)
• —Base model: Qwen/Qwen3.5-27B
• —Qwen3.5-27B announcement

Quant trade-off (GGUF lane)

(Current variant — IQ4_XS — is bolded.)

Variants in this family

(Showing 16 sibling variants under majentik/qwen3.5-27b-*. The current variant — RotorQuant-GGUF-IQ4_XS — is bolded.)