Descriptions:
MiroMind’s MiroThinker 1.7 Mini is a newly released open-source reasoning model positioning itself as a strong option for agentic and long-horizon research tasks. Built on the Qwen 3 30B architecture as a 31-billion-parameter mixture-of-experts model, it supports a 256K token context window and up to 300 tool calls per task — specifications designed specifically for multi-step agent workflows.
Fahd Mirza runs the model locally on an NVIDIA H100 with 80GB VRAM using vLLM, walking through the full installation process, quantization choices, and real VRAM consumption (approximately 77GB). The video runs two distinct tests: a creative HTML coding task (an animated Mars electrical storm) where the model produces broken output that even smaller models like Qwen 8B handle better, and a multi-step agentic stock analysis of Nvidia using MCP servers, Serper API web search, and MiroMind’s custom XML-based tool-calling format.
The agentic test is where the model is designed to shine — it executes 15 reasoning turns, calls external tools in sequence, and works toward a structured research answer. For developers building reliable research agents or long-chain reasoning pipelines and willing to run on high-VRAM hardware, MiroThinker 1.7 Mini offers an interesting open-weights alternative, though its general coding performance lags behind Claude and GPT-4o by a notable margin.
📺 Source: Fahd Mirza · Published March 12, 2026
🏷️ Format: Hands On Build
