Cortex Model Server by iAgentic

Intelligent Inference at the Core of Enterprise AI.

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Blazing-Fast Inference Engine for LLM, Speech and Vision Models

In the era of Large Language Models (LLMs), inference is the new bottleneck. From high-frequency chatbot interactions to real-time multimodal processing, today’s AI applications demand more than just accuracy — they demand performance, scalability, and flexibility.

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New
Omni-Modality Scaling
Expanding support for video, audio, image, and sensor data alongside text.
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Coming Soon
Long Context Support
Handle 100K+ tokens efficiently — critical for summarizing entire documents, medical records, or codebases.
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30 Days Free Trial
Reinforcement Learning from Human Feedback
Enhanced support for model tuning workflows that incorporate human preferences for safe and aligned AI.
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Broad Model Compatibility

Deploy the models you need — without rewriting a single line of inference logic.
Support for models like T5, Whisper, BERT, and XLM-RoBERTa opens the door to applications in translation, summarization, and audio transcription.
Seamlessly serve models like LLaVA, Idefics3, and Qwen2-VL to combine vision, language, and audio understanding in a single pipeline.
Leverage advanced hybrids like Jamba (MoE Mamba) or FalconMamba for domain-specific deployments.
Power vector databases and semantic search using embedding models like E5-Mistral.
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Lightning-Fast Inference Performance

Cortex is built from the ground up to deliver millisecond-level latency, even for the largest models.
Predict multiple tokens ahead and validate in parallel to reduce generation time.
Efficiently processes large documents and conversations by intelligently batching context.
Leverages FlashAttention and FlashInfer for GPU-accelerated execution.
Supports GPU, INT4, INT8, FP8, GPTQ, and AWQ to dramatically lower compute costs without compromising accuracy.
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Production-Grade Reliability

Bring enterprise-grade LLM capabilities to production — without compromises.
Streaming Output Support:. deal for conversational AI, customer support agents, and live transcription tools.
OpenAI-Compatible API. Drop vLLM into your existing stack using OpenAI-style endpoints with no changes to your application code.
Prefix Caching and Multi-LoRA. Load and switch between model adapters on the fly, enabling personalized AI services for every user.
Battle-Tested at Scale, Extensively tested for load, latency, and memory footprint across hundreds of deployment scenarios.
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Hardware-Agnostic and Cloud-Native

Run your models anywhere — on your cloud, your edge, or your on-prem data centers.
Optimized for NVIDIA (H100, A100), AMD MI300x, Intel Xeon/GPU, Google TPUs, AWS Inferentia/Trainium, and Intel Gaudi.
Works with KServe, Knative, SageMaker, or custom container-based infrastructure.
Dynamically batch requests from multiple users to maximize throughput.
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