Qwen3.5-122B-A10B-FP8 with 1M Context

The most rapid route to a local installation of this model is through WSL2.

Please follow the instructions listed below to get started.

The setup auto-downloads all needed files (several GBs).

The automated script takes care of everything, tailoring the setup to your specs.

? Release Hash: dd27dae2c2dac88541f26f73021598c8 • ? Date: 2026-07-08 Verify Processor: 4.0 GHz+ boost clock recommended for CPU inference RAM: enough space for background apps and OS overhead Disk Space: 80 GB NVMe SSD required for fast model weights loading Graphics: CUDA Compute Capability 8.0+ required for flash-attention

Turbocharging Language Understanding with Qwen3.5-122B-A10B-FP8

The Qwen3.5-122B-A10B-FP8 model sets a new benchmark in large language tasks, leveraging its colossal 122 billion parameters and innovative A10B architecture to deliver unparalleled performance. This cutting-edge design allows the model to strike an impressive balance between computational efficiency and accuracy, resulting in reduced memory footprint without compromising on output fidelity.

Key Specifications

Unlocking Real-Time Performance

Through its optimized FP8 precision, the Qwen3.5-122B-A10B-FP8 model achieves remarkable performance across diverse NLP tasks, particularly in reasoning and code generation. Its inference latency is remarkably low on modern GPUs, enabling seamless real-time applications without sacrificing quality.

Seamless Multimodal Integration

The Qwen3.5-122B-A10B-FP8 model also supports multimodal inputs, effortlessly integrating with text, images, and audio for comprehensive AI solutions. This versatility empowers developers to build more sophisticated and effective models that cater to diverse user needs.

Benchmarked Excellence

Extensive benchmarks demonstrate the Qwen3.5-122B-A10B-FP8 model’s superiority over previous generations, particularly in reasoning and code generation tasks. Its unparalleled performance opens up new avenues for AI innovation and applications across industries.

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• Downloader pulling optimized model shards for limited bandwith setups
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