E2E Networks Blaze AI Inference – E2E Networks Ltd. (NSE: E2E; BSE: 544783) officially launched Blaze, a pay-per-token inference service for open AI models. Blaze gives developers and enterprises a single OpenAI-compatible endpoint to leading open models. Powered by NVIDIA GPU infrastructure and open-source components from NVIDIA DSX OS, Blaze turns E2E’s India-hosted AI factory capacity into production inference services, while E2E manages model hosting and scaling. Customers pay only for the tokens they use.
Indian AI companies are moving from pilots to production, making inference cost and latency critical to product viability as AI agents drive up token consumption. Data sovereignty and keeping compute within India are key priorities, additionally the rising AI-assisted development costs are adding pressure on CIOs to improve token efficiency.
“Blaze is where you can run your production inference,” said Tarun Dua, Founder and Managing Director, E2E Networks. “There is customer demand for frontier-class open weight models running on an NVIDIA Cloud Partner (NCP) with NVIDIA reference design clusters as Sovereign Compute. Blaze provides them this on E2E Cloud in India. Blaze provides easy integration with all E2E Cloud platform services”
Built for how AI is used now
Blaze is designed for inference-heavy products where latency and cost per token matter most, including voice, coding agents, document AI and OCR, image and video generation, and language applications.
For latency-sensitive voice and chat applications, Blaze offers fast time to the first sentence, while open weight models benefit from high kv cache reuse to reduce inference cost.
Bringing AI factory capacity into production with NVIDIA DSX OS
Blaze integrates open-source components from NVIDIA DSX OS, the open, modular software layer within the NVIDIA DSX AI Factory Platform. NVIDIA Dynamo provides distributed model serving, with KV cache-aware routing, disaggregated prefill and decode, and fast data transfer between GPUs through NVIDIA NIXL. NVIDIA Grove and KAI Scheduler coordinate inference workloads across E2E’s Kubernetes fleet. NVIDIA BlueField DPUs, NVIDIA DOCA and NVIDIA Infra Controller enforce hardware-level isolation between customers, which matters for banks, healthcare providers and public-sector bodies with strict data rules.
“Access to AI computing and inference services is essential to advancing India’s AI ecosystem,” said Vishal Dhupar, Managing Director for South Asia at NVIDIA. “NVIDIA’s accelerated computing platform provides a foundation for cloud providers like E2E Networks to make AI more accessible to India’s developers and enterprises.”
One platform from training to tokens
Blaze and TIR, E2E’s AI platform, provide a flexible path for customers to scale AI workloads. Blaze delivers API-based, token-efficient inference for open-weight models, with optimized performance and seamless scaling. TIR enables self-hosted inference and model pre-training and post-training on high-performance NVIDIA Blackwell GPU clusters, through managed Kubernetes, offering greater control over infrastructure and model customization. Customers can serve their own or fine-tuned models on TIR’s managed Kubernetes with Integrated NVIDIA Dynamo. E2E’s AI factory capacity is expanding to about 14.6 MW, including a 1,024-GPU NVIDIA Blackwell GPU cluster live since May 2026 and additional 5.4 MW of Blackwell capacity including HGX B300 systems and other Blackwell GPUs.
