Build with Kimi K2.5 Multimodal VLM Using NVIDIA GPU-Accelerated Endpoints

2026-02-04 · Source: NVIDIA Technical Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, Emerging Technologies & Innovation · Depth: Intermediate, quick

Summary

Kimi K2.5 is the latest open vision language model (VLM) from the Kimi family, designed for general-purpose multimodal tasks including agentic AI workflows, chat, reasoning, coding, and mathematics. It was trained using the open-source Megatron-LM framework, which provides accelerated computing and GPU optimization through various parallelism types. The model architecture features 384 experts with a single dense layer, achieving a 3.2% activation rate of parameters per token. For vision capabilities, Kimi K2.5 incorporates a large training vocabulary of 164K vision-specific tokens and utilizes the MoonViT3d Vision Tower to convert images and video frames into embeddings. Developers can access Kimi K2.5 through free GPU-accelerated endpoints on build.nvidia.com for prototyping, or via the NVIDIA-hosted API.

Key takeaway

For AI Engineers and ML Engineers evaluating multimodal models for agentic workflows, Kimi K2.5 offers a robust, open VLM with specialized vision processing. You should explore its capabilities on NVIDIA's GPU-accelerated endpoints for prototyping or consider fine-tuning with the NeMo Framework to adapt it for domain-specific enterprise use cases.

Key insights

Kimi K2.5 is a multimodal VLM optimized for agentic AI and diverse tasks, built on Megatron-LM with a specialized vision tower.

Principles

• Specialized routing enhances multimodal efficiency.
• Parallelism is key for massive transformer training.

Method

Kimi K2.5 uses a 384-expert architecture with a single dense layer and a MoonViT3d Vision Tower for visual processing, trained with Megatron-LM for scalability.

In practice

• Prototype with Kimi K2.5 on build.nvidia.com.
• Fine-tune with NVIDIA NeMo Framework.
• Deploy with vLLM serving framework.

Topics

• Kimi K2.5
• Vision Language Models
• Megatron-LM
• NVIDIA NeMo Framework
• GPU Acceleration

Code references

• NVIDIA-NeMo/Automodel

Best for: AI Engineer, Machine Learning Engineer, AI Architect

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Editorial summary, takeaway, and curation by AIssential. Original article published by NVIDIA Technical Blog.