Qwen3.6-35B-A3B-NVFP4

2026.07.05
お知らせ

The most efficient approach for a local installation is leveraging Docker containers.

Check out the detailed setup guide below to begin.

The framework seamlessly downloads the massive neural network binaries.

The engine benchmarks your hardware to apply the most effective operational mode.

🧩 Hash sum → 5ca30f969238a4db13471b55e9bfc2a0 — Update date: 2026-07-04

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CPU: AVX2/AVX-512 instruction set required for llama.cpp
RAM: required: 16 GB absolute minimum for small models
Storage:100 GB free space for HuggingFace cache folder
Graphic Processor: hardware Tensor Cores support needed for FP16 acceleration

The Qwen3.6-35B-A3B-NVFP4 model represents a significant leap in large language model efficiency, combining 35 billion parameters with an innovative A3B architecture that optimizes both performance and computational cost. By leveraging NVFP4 quantization, the model achieves unprecedented memory savings while maintaining high accuracy across a wide range of NLP tasks. It supports an extended context window of up to 128 K tokens, enabling deeper understanding of long documents and complex reasoning chains. Benchmarks show that the model delivers state‑of‑the‑art results in multilingual generation, code synthesis, and reasoning, all with significantly lower inference latency compared to previous 35 B‑parameter models. The accompanying

provides a quick technical comparison with competing models, highlighting its superior parameter efficiency and hardware utilization.

Parameters	35 B
Context Length	128 K tokens
Quantization	NVFP4
Architecture	A3B

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