Apolo AI Ecosystem:
LLM Inference

Performing inference on large language models requires substantial computational resources, making performance optimization a critical aspect of deploying AI-driven applications. Efficient LLM inference leverages techniques such as model quantization, batching, and GPU acceleration to reduce latency and cost. Scalable inference solutions allow organizations to deploy AI models with high throughput while maintaining response quality. By leveraging optimized inference frameworks, businesses can ensure seamless real-time interaction with LLMs in production environments.

Optimized Performance

Reduces latency and maximizes throughput using advanced inference techniques.

Scalability

Supports scaling inference workloads across distributed compute resources.

Cost Efficiency

Minimizes computational costs through quantization, batching, and adaptive scaling.

Deployment Flexibility

Enables serving LLMs on local, cloud, or hybrid infrastructure for diverse use cases.2

Tools & Availability

Tool: vLLM

Available in GUI: Yes

Tool Description: vLLM is an optimized inference engine designed for large-scale language models, enabling efficient serving with high throughput and low latency. It utilizes advanced scheduling techniques, tensor parallelism, and continuous batching to maximize inference performance while reducing compute overhead.

Tool: Ollama

Available in GUI: No

Tool Description: Ollama is a flexible inference solution tailored for running and interacting with large language models efficiently. It supports on-device and cloud-based deployments, integrating seamlessly with various AI applications while ensuring low-latency responses and optimal resource utilization.

Benefits

Leveraging specialized inference frameworks allows organizations to deploy LLMs efficiently, ensuring fast response times, reduced costs, and enhanced scalability for AI applications.

‍

Open-source

All tools are open-source.

Unified environment

All tools are installed in the same cluster.

Python

CV and NLP projects on Python.

Resource agnostic

Deploy on-prem, in any public or private cloud, on Apolo or our partners' resources.

Enhances Responsiveness

Optimizes inference speed for real-time AI interactions.

Reduces Infrastructure Costs

Efficiently manages compute resources, minimizing unnecessary expenditures.

Supports Scalable AI Workloads

Adapts to varying demand with dynamic resource allocation.

Enables Seamless Deployment

Offers flexible deployment options across cloud, edge, and on-premise environments.

Apolo AI Ecosystem:
‍Your AI Infrastructure, Fully Managed

Apolo’s AI Ecosystem is an end-to-end platform designed to simplify AI development, deployment, and management. It unifies data preparation, model training, resource management, security, and governance—ensuring seamless AI operations within your data center. With built-in MLOps, multi-tenancy, and integrations with ERP, CRM, and billing systems, Apolo enables enterprises, startups, and research institutions to scale AI effortlessly.