Qubrid AI Accelerates Open-Source Model Inferencing with NVIDIA AI Infrastructure and One Single API for Enterprise Agents

Driving shift to open-source based Agents with an Open, Inference-First full-Stack AI Platform

SAN JOSE, Calif., March 16, 2026 /PRNewswire/ -- Qubrid AI, a leading Open, Inference-First Full-Stack AI Platform company, today at NVIDIA GTC 2026 announced the addition and acceleration of over forty open-source models powered by NVIDIA AI infrastructure. Enterprise agent developers can simply integrate a single API provided by Qubrid and inference over forty models from within their agentic application, decide which model suits their requirements and then scale using NVIDIA GPU VMs or dedicated GPU servers all running on Qubrid's advanced AI platform.

"Open-source models are no longer experimental alternatives - they are becoming the backbone of production AI agents," said Pranay Prakash, CEO of Qubrid AI. "We founded the company on a solid foundation of open-source inferencing leveraging NVIDIA AI infrastructure and our strategy has proven to be correct. More enterprise agent developers are embracing open-source models due to rapid innovation and price competitiveness. We are excited to expand our open-source model inferencing portfolio for developers looking to build commercial agents and applications."

The NVIDIA Stack Inside Qubrid

Every model on Qubrid runs on NVIDIA accelerated computing instances, with NVIDIA CUDA Toolkit and optimized libraries preloaded. These are not shared CPU-backed abstractions - they are full GPU environments engineered for deterministic throughput.

At the serving layer, we use the NVIDIA Dynamo-Triton. Triton standardizes model deployment across frameworks like PyTorch, TensorFlow, ONNX, and NVIDIA TensorRT. It enables dynamic batching, multi-model hosting, concurrent execution, and streaming support. This ensures requests are handled efficiently, even under heavy production load.

On top of Triton, Qubrid applies automatic optimization using TensorRT,- NVIDIA's high-performance inference SDK built on CUDA. TensorRT compiles and optimizes models through precision tuning (FP16 and INT8), layer fusion, and kernel auto-optimization. For large language models, we apply NVIDIA TensorRT-LLM acceleration to unlock significant speedups and memory efficiency.

From Playground to Production

Users can experiment in Qubrid Playground with on-demand NVIDIA compute and then move seamlessly to production endpoints for maximum throughput, while serverless APIs provide autoscaled inference for dynamic workloads.

Unlike many shared environments where performance fluctuates under load, Qubrid runs large models on dedicated NVIDIA AI infrastructure. Latency remains low and throughput scales linearly as additional GPUs are provisioned. There are no cold-start surprises or hidden CPU fallbacks. Billing is equally transparent. Our token-based, pay-as-you-go model ensures customers only pay for the inference they consume - without idle GPU overhead.

Qubrid AI's single API for open-source models such as NVIDIA Nemotron, Qwen 3.5, Kimi K2.5, Deepseek R1, MiniMax, GLM 4.7, Llama 3.3 is live and available now at https://platform.qubrid.com/models.

About Qubrid AI
Qubrid AI is an Open, Inference-First Full Stack AI Platform company delivering one-api for model inferencing for agent development, fine-tuning, and RAG capabilities all running on GPU cloud and on-premise infrastructure. Designed for agent developers, enterprises, and research organizations, Qubrid AI accelerates the journey from models to real outcomes - combining powerful compute, token-based on-demand inferencing, unified APIs, and intelligent orchestration for scalable Agentic AI innovation.

Media Contact:
Shubham Tribedi
shubham.tribedi@qubrid.com
https://www.qubrid.com

SOURCE Qubrid, Inc