This step‑by‑step tutorial shows how to quickly launch OpenAI’s open‑source gpt‑oss‑20b model on Alibaba Cloud PAI without writing code, configure the deployment, and start chatting with the model using the Cherry Studio client.
The article provides a detailed analysis of OpenAI’s newly released open‑source GPT‑OSS models (20B and 120B) and the closed‑source GPT‑5 family, covering their architectures, training pipelines, benchmark results, practical usage observations, pricing, and the mixed user feedback that surrounds GPT‑5.
OpenAI has unveiled its first open‑source weight models in over five years—gpt‑oss‑120B and gpt‑oss‑20B—detailing their MoE architecture, quantization techniques, benchmark performance, licensing, and the industry’s mixed reactions, while hinting at future open‑source AI developments.
OpenAI’s new open‑source gpt‑oss models, available in 20B and 120B sizes, can be run locally via Ollama with features like agentic capabilities, configurable reasoning, fine‑tuning, and MXFP4 quantization, and the article provides step‑by‑step installation, usage, and integration instructions.
OpenAI's gpt-oss series introduces two open‑source large language models—gpt‑oss‑120b and gpt‑oss‑20b—featuring Mixture‑of‑Experts architecture, 4‑bit MXFP4 quantization, extensive benchmark results, and broad deployment options across cloud and consumer hardware.
OpenAI has unveiled GPT‑OSS, an open‑source large language model series featuring a 120‑billion‑parameter version for high‑throughput production and a 20‑billion‑parameter version for low‑latency consumer hardware, both using Mixture‑of‑Experts architecture, 4‑bit quantization, and released under the permissive Apache 2.0 license.
This article analyzes the surprising design choices of the rumored GPT‑OSS 120B model, explaining the rationale behind a 64‑dimensional attention head, the equal hidden and intermediate sizes, and other quirky parameters such as MLP bias and KV‑sink SWA, backed by theoretical formulas and empirical benchmarks.
