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InfiX.ai - Decentralized GenAI
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InfiX.ai

Powering the Last Mile
of GenAI

Our Mission and Vision

InfiX.ai is pioneering collaborative generative AI through low-resource training, model fusion, GUI agents, and multimodal intelligence.

Mission

At InfiX.ai, we believe in making AI more accessible, efficient, and capable. Our research focuses on developing innovative techniques that push the boundaries of what AI systems can achieve while maintaining practical applicability.

Vision

We are not only building another model - We are building the system that builds all models.

ACCESSIBLE EFFICIENT CAPABLE

Research Focus

Low-Precision Training & Efficient Inference

Reduce training time, memory pressure, and inference cost to improve model training and deployment efficiency. Achieve up to 22% shorter training time and 19% higher throughput while maintaining near-BF16 accuracy.

Low-Precision Training & Efficient Inference 1
Low-Precision Training & Efficient Inference 2
Low-Precision Training & Efficient Inference 3

Model Fusion & Model Merging

Fuse model strengths into stronger Expert AI without multi-model inference overhead. Support fusion of 10B-scale foundation models across multiple domains, requiring only one ten-thousandth of the GPU hours needed for full pretraining.

Model Fusion & Model Merging 1

Latest Research

ICML 2026Apr 30 , 2026

BPDQ: Bit-Plane Decomposition Quantization on a Variable Grid for Large Language Models

Large language model inference is often bounded by memory footprint and memory bandwidth in resource-constrained deployments, making quantization a fundamental technique for efficient serving. While post-training quantization maintains high fidelity at 4-bit, it deteriorates at 2-3 bits. BPDQ constructs a variable quantization grid via bit-planes and scalar coefficients, then iteratively refines them with approximate second-order information and progressive error compensation to minimize output discrepancy. In the 2-bit regime, BPDQ enables serving Qwen2.5-72B on a single RTX 3090 with 83.85% GSM8K accuracy, compared with 90.83% at 16-bit.

ICML 2026Apr 30 , 2026

InfiMed-ORBIT: Aligning LLMs on Open-Ended Complex Tasks via Rubric-Based Incremental Training

ORBIT is an open-ended rubric-based incremental training framework for high-stakes medical dialogue. It combines synthetic dialogue generation, retrieval-augmented rubric construction, difficulty filtering, and rubric-guided reinforcement learning to align LLMs on context-dependent consultation tasks. Applied to Qwen3-4B-Instruct, ORBIT raises the HealthBench-Hard score from 7.0 to 27.5 using only 2k training samples, with larger rubric sets improving performance to 33.6 and 37.3.

ICML 2026Apr 30 , 2026

Model Merging Scaling Laws in Large Language Models

This work studies empirical scaling laws for language model merging measured by cross-entropy. Across 10,866 merged models, base sizes from 0.5B to 72B, nine domains, and four methods, the paper identifies a compact floor-plus-tail power law that links model size and expert count. The law explains why most merging gains arrive early, why variability shrinks as more experts are included, and how to plan expert count under a budget.

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InfiXspeed

Build your own Expert AI
with FP8-powered InfiXspeed.

InfiXspeed is InfiX.ai’s enterprise platform for efficient domain AI development. It helps organizations turn proprietary data, business workflows, and expert knowledge into domain-specific Expert AI that can be trained, evaluated, and deployed in secure, controlled environments.

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GitHub

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Hugging Face

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Contact Us

contact@infix.io