NVIDIA + OpenAI: модели GPT с открытым весом теперь работают локально на ПК с RTX AI

A new chapter in local AI computing is here. OpenAI has released two powerful open-weight models — gpt-oss-20b and gpt-oss-120b — specifically optimized to run on NVIDIA RTX and RTX Professional GPUs. These models are designed for reasoning, chain-of-thought, and complex natural language tasks, and are now bringing LLM-level intelligence to everyday RTX-powered desktops.

💡 What’s New?
gpt-oss-20b and gpt-oss-120b are trained with a context length of 131K tokens, enabling significantly deeper prompts, document analysis, and in-memory reasoning.
With RTX 5090, the models can achieve up to 256 tokens/sec inference speed — making locally deployed models not only viable but blazing fast.
Optimized for MXFP4 precision, these models maintain high accuracy while reducing compute and memory usage — ideal for PCs and edge deployments.

🛠️ Developer Ecosystem & Tools:
Developers can deploy and fine-tune these models on their local machines using tools such as:
Ollama – CLI tool for managing and running local models
llama.cpp – Efficient C++ implementation for inference
Microsoft’s AI Foundry Local – End-to-end environment for building local AI workflows
All of these are now optimized for the RTX AI PC ecosystem, making advanced AI development more accessible than ever.

🔍 Why This Matters:
Empowers developers, researchers, and startups to build and test LLM applications locally, without relying on cloud inference.
Accelerates on-device generative AI, including intelligent search, RAG, summarization, and coding copilots — all running on consumer hardware.
Offers privacy, cost-efficiency, and speed—critical for enterprise prototyping, offline workflows, and distributed development.
With this release, RTX AI PCs are evolving from high-performance gaming and content creation machines into AI innovation platforms, bridging the gap between open models and real-time user applications.

💬 As AI becomes more open, more local, and more efficient — the next wave of LLM-based apps may begin not in a cloud data center, but right on your desk.