Llm-D

Visit Tool

llm-d optimizes AI inference on Kubernetes, delivering state-of-the-art performance. It offers features like reproducible benchmarks, KV offloading, and LoRA routing for efficient and scalable AI deployments.

Claim this tool

No Views Yet

At a glance

Pricing

—

Free tier

—

API

—

Skill level

Technical

About

What is llm-d?

llm-d is a tool designed to enhance the inference performance of AI models when deployed on modern accelerators within a Kubernetes environment. It provides several key features to achieve this, including reproducible benchmark workflows that allow for consistent performance evaluation. The tool also incorporates hierarchical KV offloading and cache-aware LoRA routing, which are crucial for optimizing memory usage and data access during inference. Furthermore, llm-d supports active-active High Availability (HA) and scale-to-zero autoscaling, ensuring both reliability and cost-efficiency for AI inference workloads.

Best used for

Optimizing and scaling AI model inference performance on Kubernetes clusters with modern accelerators.

Common actions

Optimize AI inference

Deploy AI on Kubernetes

Improve model performance

Manage AI infrastructure

Benchmark AI models

low-code/no-codeautomated workflowopen-sourcecollaborationdeepfakeworkflowsgithub copilot"AI Agents"face swapping

Capabilities

Key features

State-of-art inference performance
Reproducible benchmark workflows
Hierarchical KV offloading
Cache-aware LoRA routing
Active-active HA
Scale-to-zero autoscaling

Target Audience

ML EngineersDevOps EngineersCloud ArchitectsData Scientists

Integrations

Not yet documented

Pricing & Plans

unknown

Free

FAQs

How does llm-d's hierarchical KV offloading improve performance on modern accelerators?

Hierarchical KV offloading in llm-d optimizes memory usage by intelligently moving less frequently accessed key-value pairs to slower, larger memory tiers, freeing up faster on-chip memory for active data. This reduces memory bottlenecks and allows for larger models or higher throughput on modern GPUs.

Can llm-d be used with any AI model, or is it specific to certain architectures?

llm-d is designed to enhance inference performance for large language models (LLMs) and other AI models, particularly those benefiting from optimizations like LoRA routing and efficient KV caching. While not strictly limited, its features are most impactful for models deployed on accelerators within a Kubernetes environment.

What are the benefits of llm-d's active-active HA and scale-to-zero autoscaling for production environments?

Active-active HA ensures continuous availability and load balancing, preventing downtime during failures. Scale-to-zero autoscaling optimizes cost by automatically scaling down resources to zero when not in use, and scaling up rapidly during demand spikes, making it highly efficient for fluctuating inference workloads.

Trending

Subcategories trending in Coding & Development

Code Assistants DevOps & Infrastructure No-Code / Low-Code Testing & QA Backend & APIs Prompt Engineering

Trending

Explore

Browse AI tools by category

Content & Design Productivity & Business Coding & Development AI Agents & Automation Research & Education Wellness & Lifestyle Career Development Marketing & Growth Data & Analytics Customer Support & CX Finance E-commerce