LLM Inference Explained: Runtime, Infrastructure, and Tooling
When we hear about Generative AI, we often hear about model training, GPUs, LLMs, RAG, agents, and AI applications.
But there is another important part that is becoming increasingly important as AI applications move into production:
Inference.
As someone coming from a DevOps background and learning more about MLOps and AI infrastructure, I found inference interesting because it connects machine learning with many concepts we already know from cloud and DevOps: compute, Kubernetes, scaling, monitoring, networking, cost optimization, and reliability.
In this article, let's understand LLM inference from a beginner's perspective.
What is Inference?
In simple terms:
Training teaches the model. Inference uses the trained model.
During training, a model learns patterns from huge amounts of data and its weights are updated.
During inference, we take those trained model weights and use them to generate an output for a new request.
For example:
User
↓
"What is Kubernetes?"
↓
LLM
↓
Generated response
The process of generating that response is inference.
Training vs Inference
It is useful to understand the difference.
Training
Large Dataset
↓
GPU Cluster
↓
Training
↓
Model Weights
↓
Trained Model
The goal is to teach the model.
Training can require a huge amount of compute, storage, GPUs, and time.
Inference
User Request
↓
Inference Server
↓
GPU
↓
Trained Model
↓
Generated Response
The goal is to serve the model to users.
And this is where things become interesting.
Why is LLM Inference Difficult?
For a traditional machine learning model such as an XGBoost model, inference can be relatively simple.
For example:
Input Data
↓
XGBoost Model
↓
Prediction
The model might run comfortably on a CPU.
But modern LLMs can contain billions of parameters and require significant GPU memory and compute.
And there is another important difference.
An LLM doesn't simply calculate one answer and return it.
It generates the response token by token.
For example:
Kubernetes
↓
is
↓
a
↓
container
↓
orchestration
↓
platform
Now imagine thousands of users doing this at the same time.
The problem becomes much bigger:
1 request
↓
Easy
1,000 requests
↓
Need efficient GPU utilization
100,000 requests
↓
Need serious inference infrastructure
So simply taking a model, putting it on a GPU, and exposing an API isn't enough for large-scale production.
We need to optimize the entire inference system.
The Three Layers of Production Inference
A useful way to understand production inference is through three layers:
LLM INFERENCE
│
┌──────────┼──────────┐
↓ ↓ ↓
Runtime Infrastructure Tooling
Let's understand each one.
1. Runtime
Runtime is about:
How efficiently can we run a model on a GPU?
Suppose we have:
Qwen / Llama
↓
GPU
↓
Inference
We want the GPU to be used as efficiently as possible.
This is where techniques such as:
Batching
KV Cache
Quantization
Speculative Decoding
Efficient attention
GPU memory optimization
become important.
Batching
Imagine four users send requests at almost the same time.
Without efficient batching:
Request A → GPU
Request B → GPU
Request C → GPU
Request D → GPU
The inference engine may process requests less efficiently.
With batching, multiple requests can be processed together:
┌─ Request A
├─ Request B
GPU ←───────┼─ Request C
└─ Request D
Modern inference engines can dynamically combine requests and process them efficiently.
This improves GPU utilization and throughput.
KV Cache
LLMs use an attention mechanism while processing tokens.
During inference, the model generates tokens sequentially.
Some intermediate information from previous tokens can be stored in a KV cache.
Instead of repeatedly calculating the same information, the model can reuse cached information.
Conceptually:
First request
↓
Calculate
↓
KV Cache
↓
Reuse cached information
↓
Faster processing
This becomes especially important when handling long prompts and multiple requests.
Quantization
Large models require a lot of GPU memory.
Quantization reduces the precision used to represent model weights.
For example:
FP16
↓
INT8
↓
INT4
The exact memory savings depend on the model and implementation, but the general idea is:
Use fewer bits to represent the model so that it requires less memory.
This can allow a model to run on smaller or fewer GPUs.
The trade-off is that aggressive quantization can affect model quality.
So there is always a balance:
Memory ↓
Cost ↓
Potential speed ↑
vs.
Model quality may ↓
Speculative Decoding
This is another technique for improving generation speed.
Instead of asking the large model to generate every token one by one, a smaller model can first guess several tokens.
The larger model then validates those guesses.
Conceptually:
Small Model
↓
Generate several draft tokens
↓
Large Model
↓
Validate
↓
Accept valid tokens
If many guesses are correct, the large model can generate multiple tokens more efficiently.
The result can be lower inference latency.
2. Infrastructure
Runtime optimization is only one part of the problem.
Now imagine your application becomes popular.
You have:
1 GPU
But suddenly:
10,000 users
Now you need infrastructure that can scale.
A simplified architecture could look like:
Users
↓
Load Balancer
↓
Inference Service
↓
┌─────────────┼─────────────┐
↓ ↓ ↓
GPU 1 GPU 2 GPU 3
↓ ↓ ↓
Model Model Model
Infrastructure is responsible for things such as:
GPU provisioning
Kubernetes
Autoscaling
Load balancing
Networking
Multi-GPU deployments
Multi-node deployments
Health checks
High availability
Monitoring
Cost management
This is where traditional DevOps knowledge becomes extremely useful.
Multi-GPU and Parallelism
Some models are too large to fit on a single GPU.
For example:
Large Model
↓
┌───┼───┐
↓ ↓ ↓
GPU1 GPU2 GPU3
The model can be distributed across multiple GPUs using different parallelism techniques.
The goal is to make multiple GPUs work together efficiently.
However, more GPUs also introduce challenges such as:
GPU-to-GPU communication
Network bandwidth
Synchronization
Increased cost
So simply adding GPUs does not automatically mean inference becomes faster.
The system has to use them efficiently.
Disaggregated Inference
LLM inference can be broadly divided into two important phases:
User Prompt
↓
PREFILL
↓
DECODE
↓
Generated Response
Prefill
The model processes the input prompt.
Think:
Reading and understanding the user's request.
Decode
The model generates the response token by token.
Think:
Writing the answer.
These two phases have different compute characteristics.
Instead of putting both on the same workers, modern inference architectures can separate them:
User Requests
↓
┌────────┴────────┐
↓ ↓
Prefill Workers Decode Workers
↓ ↓
GPUs GPUs
Now each side can scale independently.
For example, if decode workload increases:
Prefill GPUs: 10
Decode GPUs: 20
we could scale the decode side without scaling the prefill side equally.
This architecture is more complex, but can become useful at large scale.
3. Tooling
The third layer is tooling.
Imagine an ML engineer wants to deploy a model.
Without a good platform, they might need to understand:
Docker
CUDA
GPU types
Kubernetes
Helm
Networking
Autoscaling
Monitoring
Secrets
Load balancing
Model configuration
That's a lot of infrastructure knowledge.
Good tooling provides a higher-level interface.
For example:
Deploy Model
Model: Qwen
GPU: H100
Replicas: 3
Quantization: INT8
↓
Deploy
Behind the scenes, the platform can handle:
Kubernetes
GPU scheduling
Networking
Autoscaling
Monitoring
Model serving
The goal is to give engineers the right balance between:
Control + Productivity
They shouldn't have to manually manage every infrastructure detail just to deploy a model.
How Everything Fits Together
Now we can connect the three layers.
USERS
↓
API / Gateway
↓
Inference Platform
↓
┌──────────────────┼──────────────────┐
↓ ↓ ↓
RUNTIME INFRASTRUCTURE TOOLING
│ │ │
vLLM Kubernetes APIs
Batching GPU Nodes CLI
KV Cache Autoscaling Dashboard
Quantization Load Balancing Deployment
CUDA Multi-GPU Monitoring
│ │ │
└──────────────────┼──────────────────┘
↓
LLM Model
↓
Generated Output
These three layers need to work together.
A highly optimized runtime isn't enough if the infrastructure can't scale.
A powerful infrastructure platform isn't enough if the model runtime is inefficient.
And even a technically excellent system isn't enough if engineers can't use it easily.
Where Does MLOps Fit?
This is where things get interesting for DevOps engineers.
Traditional DevOps already deals with:
Infrastructure
CI/CD
Containers
Kubernetes
Networking
Monitoring
Scaling
Security
Cost
Reliability
MLOps adds additional concerns:
Models
Model versions
Datasets
Model deployment
GPU infrastructure
Inference
Model monitoring
Experiment tracking
Feature/data pipelines
And GenAI adds another layer:
LLMs
Embeddings
RAG
Vector databases
Token usage
Inference optimization
AI agents
LLM observability
So the overlap looks something like:
DevOps
│
↓
MLOps
│
↓
GenAI Infrastructure
│
┌────────┼────────┐
↓ ↓ ↓
GPU LLM Inference
Platform Serving Optimization
A Practical GenAI Infrastructure Stack
A production system could look like this:
Developer
↓
GitHub
↓
CI/CD
↓
Docker
↓
Container Registry
↓
Kubernetes / EKS
↓
GPU Nodes
↓
Inference Runtime
↓
vLLM
↓
Qwen / Llama
↓
API
↓
Users
And around it:
Terraform
Helm
Prometheus
Grafana
OpenTelemetry
CloudWatch
Autoscaling
Security
Cost Optimization
MLOps / GenAI InfThis is where AI Platform Engineering / rastructure starts to overlap heavily with DevOps.
What Should a Beginner Learn First?
If you're starting with GenAI infrastructure, don't try to learn everything at once.
I would recommend:
Step 1 — GenAI basics
LLM
Tokens
Parameters
Model weights
Context window
Training
Inference
Step 2 — GPU basics
GPU
VRAM
CUDA
GPU utilization
GPU memory
Step 3 — Model serving
vLLM
Triton
Model APIs
OpenAI-compatible APIs
Step 4 — Inference optimization
Batching
KV Cache
Quantization
Speculative Decoding
Prefill
Decode
Step 5 — Infrastructure
Kubernetes
GPU nodes
Autoscaling
Multi-GPU
Load balancing
Observability
Cost optimization
Step 6 — GenAI applications
Embeddings
RAG
Vector databases
Agents
Tool calling
MCP
Final Takeaway
The easiest way to remember all of this is:
Training teaches the model. Inference serves the model.
And production inference has three major areas:
Runtime
↓
Make one model run efficiently
Infrastructure
↓
Scale that model reliably
Tooling
↓
Make it easy for engineers to deploy and manage
The optimization techniques we discussed earlier fit mainly into these areas:
Batching → Runtime
KV Cache → Runtime
Quantization → Runtime
Speculative Decoding → Runtime
Parallelism → Runtime + Infrastructure
Disaggregation → Runtime + Infrastructure
Autoscaling → Infrastructure
Kubernetes → Infrastructure
Deployment Platform → Tooling
For someone coming from a DevOps background, this is an interesting area because GenAI infrastructure doesn't replace DevOps — it extends it into GPU, model serving, inference, and AI-specific workloads.
The goal isn't only to make an AI model work.
The real challenge is:
How do we make it fast, reliable, scalable, observable, secure, and cost-efficient in production?
That's where MLOps and AI infrastructure engineering become important.
Conclusion
Generative AI is moving from experimentation to production.
As that happens, inference becomes just as important as the model itself.
The future isn't only about building bigger models.
It's also about building better systems around those models.
And that's where skills in cloud, Kubernetes, GPUs, automation, observability, scaling, and cost optimization become extremely valuable.
