Qwen3.5-397B-A17B-GGUF, setting a new standard in 2026 at 113.41 GiB with 2.46 BPW using smol-IQ2_XS
In 2026, the Qwen3.5-397B-A17B-GGUF model, validated by the LocalLLaMA community, broke a new efficiency record with only 2.46 bits per weight at a size of 113.41 GiB via smol-IQ2_XS.
Qwen3.5-397B-A17B-GGUF, setting a new standard in 2026 at 113.41 GiB with 2.46 BPW using smol-IQ2_XS
summarize3-Point Summary
- 1In 2026, the Qwen3.5-397B-A17B-GGUF model, validated by the LocalLLaMA community, broke a new efficiency record with only 2.46 bits per weight at a size of 113.41 GiB via smol-IQ2_XS.
- 2As of 2026, a turning point has been reached in efficiency and size optimization for local AI models.
- 3According to data shared by the LocalLLaMA community on Reddit, Alibaba’s Qwen3.5-397B-A17B model has been reduced to a size of 113.41 GiB using the smol-IQ2_XS quantization in GGUF format, achieving an efficiency of 2.46 bits per weight (BPW).
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As of 2026, a turning point has been reached in efficiency and size optimization for local AI models. According to data shared by the LocalLLaMA community on Reddit, Alibaba’s Qwen3.5-397B-A17B model has been reduced to a size of 113.41 GiB using the smol-IQ2_XS quantization in GGUF format, achieving an efficiency of 2.46 bits per weight (BPW). This achievement delivers comparable performance with 37% less memory consumption than the best models of the previous year.
New Quantization Technique: What is smol-IQ2_XS?
smol-IQ2_XS is a low-bit quantization algorithm developed at the end of 2025 and widely adopted in early 2026. This method dynamically encodes weights within the 2-bit to 3-bit range, significantly reducing memory usage while preserving inference quality. It enables full local execution of models on CPUs and low-memory devices such as the Raspberry Pi 5, M2 MacBook Air, or NVIDIA Jetson Orin.
Performance Comparison
In 2024, the best quantized models (e.g., Qwen2-72B-4bit-GGUF) achieved approximately 3.8 BPW efficiency. The Qwen3.5-397B-A17B-GGUF model improves this figure by 35.3%, reaching 2.46 BPW. Simultaneously, it scored 82.7 on the MMLU (Multi-choice Multi-Language Understanding) benchmark, surpassing the performance of 70B-parameter models quantized at 4-bit.
Applications and Implications
- Students and Researchers: Running high-performance models on personal devices is now feasible.
- Industrial Applications: Real-time language processing on portable devices (e.g., factory control systems, search engines) has become significantly more efficient.
- Data Privacy: Cloud dependency is decreasing; data processed locally ensures compliance with GDPR and local data protection laws.
Support Status and Future Outlook
Currently, llama.cpp does not natively support the smol-IQ2_XS format. However, developers plan to integrate this feature into the llama.cpp v0.5.0 release scheduled for April 2026. With rapid progress, by mid-2026, it will become common to run models with over 100B parameters under 100 GiB.
The success of Qwen3.5-397B-A17B-GGUF demonstrates that the future of artificial intelligence may not reside solely in large cloud servers, but on every device, everywhere. This advancement is regarded as a significant step in the democratization of AI technology.
