LLM-D Explained: How Distributed AI Inference Makes Large Language Models Faster and Cheaper
- Jayant Upadhyaya
- Jan 27
- 6 min read
Large Language Models (LLMs) are now used in many real-world applications. These include chatbots, coding assistants, search systems, and Retrieval-Augmented Generation (RAG) tools. As more people use these systems at the same time, a new challenge appears: how to handle many AI requests efficiently.
This article explains LLM-D, an open-source project designed to solve this problem. LLM-D helps AI systems run faster, reduce delays, and lower costs by intelligently distributing AI workloads across infrastructure like Kubernetes. To make this easy to understand, we will use simple examples, clear explanations, and step-by-step ideas.
1. Understanding the Problem: AI Requests at Scale
Imagine a busy airport. Planes arrive from many places. Some are small local flights, while others are large international planes. Each plane needs to land safely on the correct runway at the right time.
If all planes tried to land randomly, chaos would happen. That is why airports rely on air traffic controllers. These controllers look at each incoming plane and decide:
Where it should land
When it should land
Which runway is best
AI systems face a very similar problem.
2. AI Requests Are Like Airplanes
In AI systems, requests are like airplanes.
A request could be:
A small question answered using RAG
A large coding task using an AI agent
A long conversation with many follow-up steps
Each request needs computing power from an AI model. Some requests are quick and light. Others are heavy and slow.
Without proper control, these requests can slow each other down.
3. What Is AI Inference?
AI inference is the process where a trained AI model:
Receives a prompt
Processes it
Generates a response
Every time you ask an AI a question, inference happens.
In small systems, inference is simple. But in large systems with many users, inference becomes expensive and slow if not handled properly.
4. Why Traditional Load Balancing Fails
Most systems use round-robin load balancing. This means:
Requests are sent one by one to available servers
Each server gets an equal number of requests
This works well when all requests are similar.
However, AI requests are not uniform.
Some requests:
Have many input tokens
Produce small outputs
Others:
Have small inputs
Produce very large outputs
Treating all requests the same causes problems.
5. What Is Inter-Token Latency?
When AI generates text, it produces tokens one by one.
Inter-token latency is the delay between tokens appearing.
If many heavy requests block the system:
Users wait longer to see the first token
Responses feel slow
The experience feels broken
Reducing this delay is very important for real-time AI applications.
6. Introducing LLM-D
LLM-D is an open-source system designed to manage AI inference more intelligently.
The “D” in LLM-D stands for Distributed.
LLM-D distributes AI workloads across many machines so that:
Inference runs faster
Costs are lower
Performance is more stable
LLM-D is commonly used for:
RAG systems
AI agents
Coding assistants
Large-scale AI services
7. Why LLM-D Was Created
As AI usage grew, teams noticed several problems:
Inference costs were rising quickly
Latency was inconsistent
Some users had very slow responses
Infrastructure was underused or overloaded
LLM-D was created to fix these issues by making inference smarter, not just bigger.
8. LLM-D and Kubernetes
LLM-D is designed to work on Kubernetes, a platform used to manage large numbers of servers.
Kubernetes allows:
Scaling resources up or down
Running workloads across clusters
Managing GPUs efficiently
LLM-D uses Kubernetes to distribute AI inference across many machines instead of relying on one large server.
9. Different Types of AI Requests
Not all AI requests are the same.
For example:
A RAG request might have a large input and small output
A coding agent request might generate many tokens over time
These two requests should not be treated equally.
LLM-D understands this difference.
10. The Inference Gateway: The “Air Traffic Controller”
At the center of LLM-D is the inference gateway.
This gateway acts like an air traffic controller:
It inspects incoming requests
It decides where each request should go
It balances the system intelligently
Instead of blindly sending requests to the next server, it makes decisions based on data.
11. Metrics Used for Intelligent Routing
The inference gateway looks at several factors, including:
Current system load
Predicted latency
Request size
Cache availability
By using these metrics, LLM-D avoids congestion and delays.
12. What Is Prefix Caching?
Many AI requests share similar text.
For example:
Similar prompts
Repeated system instructions
Common context
Prefix caching stores these shared parts so they do not need to be processed again.
This saves:
GPU computation
Time
Money
LLM-D routes similar requests to servers that already have cached data.
13. Why Caching Reduces Cost
AI inference is expensive because GPUs must perform many calculations.
If the same text is processed repeatedly:
Costs increase
Performance drops
Caching avoids repeated work, making inference cheaper and faster.
14. Prefill and Decode: Two Phases of Inference
LLM-D splits inference into two phases:
Prefill
The model reads and understands the input
This step uses a lot of memory
Decode
The model generates output tokens
This step is repeated many times
Traditional systems treat these phases as one workload. LLM-D separates them.
15. Disaggregating Prefill and Decode
LM-D assigns:
Prefill to high-memory GPUs
Decode to scalable GPU workers
Both phases share the same KV cache, which stores useful information.
This separation allows:
Better hardware usage
Faster responses
Independent scaling
16. What Is the KV Cache?
The KV (Key-Value) cache stores intermediate data from the model.
Sharing this cache means:
Prefill and decode stay in sync
Similar requests reuse information
Computation is reduced
This is a key part of LLM-D’s efficiency.
17. Endpoint Picker: Choosing the Best Destination
LLM-D uses an endpoint picker to decide where each request goes.
It considers:
Which servers are free
Which servers have relevant cached data
Which path will be fastest
This decision happens automatically for every request.
18. Real Performance Improvements
LLM-D has shown major performance gains.
Examples include:
3× improvement in P90 latency (slowest 10% of requests)
57× improvement in time-to-first-token
These improvements are critical for:
Service-level objectives (SLOs)
Quality of service (QoS)
User satisfaction
19. Why Time to First Token Matters
Users expect AI to respond quickly.
Even if the full answer takes time, seeing the first token quickly:
Builds trust
Improves experience
Feels responsive
LLM-D dramatically improves this metric.
20. Supporting Mission-Critical AI Systems
Many organizations depend on AI for important tasks:
Customer support
Code generation
Decision support
Internal tools
Slow or unreliable inference can cause real problems.
LLM-D helps ensure:
Predictable performance
Lower risk
Better uptime
21. Cost Savings Through Smart Distribution
Instead of adding more GPUs:
LLM-D makes better use of existing ones
By reducing wasted computation:
Hardware costs drop
Cloud bills decrease
Efficiency increases
This makes AI more sustainable at scale.
22. LLM-D vs Simple Scaling
Traditional scaling means:
Add more servers
Spend more money
LLM-D focuses on:
Smarter routing
Better caching
Intelligent scheduling
This leads to better results without endless scaling.
23. Why Open Source Matters
LLM-D is open source.
This means:
Transparency
Community contributions
Flexibility
Organizations can inspect, modify, and adapt LLM-D to their needs.
24. Who Benefits from LLM-D?
LLM-D is useful for:
AI platform teams
Cloud infrastructure teams
Companies running large LLM workloads
Developers building scalable AI products
It is especially helpful where performance and cost matter.
25. Challenges LLM-D Solves
LLM-D helps solve:
Inference bottlenecks
High GPU costs
Latency spikes
Uneven workload distribution
26. Best Practices When Using LLM-D
To get the most value:
Monitor latency metrics
Enable caching
Separate prefill and decode
Tune routing policies
Test under load
Good configuration matters.
27. LLM-D and the Future of AI Infrastructure
As AI usage grows:
Inference efficiency becomes critical
Costs must be controlled
Performance must stay consistent
Systems like LLM-D represent the future of AI infrastructure.
28. Simple Summary
LLM-D treats AI inference like air traffic control:
It understands different request types
It routes them intelligently
It avoids congestion
It improves speed and lowers cost
By distributing workloads across Kubernetes and using smart caching and routing, LLM-D makes large-scale AI systems practical and reliable.
29. Final Thoughts
Building AI models is only part of the challenge. Running them efficiently at scale is just as important.
LLM-D provides a powerful solution for managing AI inference in real-world systems. It improves performance, reduces cost, and ensures a better experience for users.
For teams running large AI workloads, LLM-D is a key building block for the future.
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