Install Cosmos-Reason2-2B via WebGPU (Browser) No-Internet Version

Using Docker is the absolute quickest way to install this model on your local machine.

Please follow the instructions listed below to get started.

The installer automatically pulls the model (could be multiple GBs).

You don’t need to tweak anything, as the installer will automatically pick the highest performing setup for you.

💾 File hash: b43599696f1612f65500d1431b6e08e8 (Update date: 2026-06-22)



  • CPU: AVX2/AVX-512 instruction set required for llama.cpp
  • RAM: minimum 16 GB for stable 8B model loading
  • Disk Space: required: fast PCIe 4.0 drive for instant boots
  • 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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