The LLM Inference Landscape
One of the biggest topics around LLM is when it comes to inference, which means when we actually serve the model to start generating output tokens. And depending on what system you use to serve LLMs, end users will experience different results. For example, when we go to ChatGPT and interact with the chatbot, it might generate its output at a certain speed. Well, when we go to Gemini, for example, it might generate its output at a much different speed. We measure this in what's called tokens per second. And depending on what system you use to actually run your large language model, the same model can be served at varying speed.
And there are tons of systems out there like llama.cpp, vLLM, SGLang, TensorRT-LLM, Hugging Face TGI, LMDeploy. These are all a classification of inference engine that allows a model to be served. vLLM is a system that really stands out as a tool that allows you to run your model with high throughput.
Introducing vLLM & Paged Attention
When you start to serve LLM beyond a single user or a single instance to multiple users across multiple instances, what starts to happen is that the underlying system that runs LLM needs to serve multiple requests at a time. And since during inference your prompt is stored not only in memory in terms of KV cache and auto regressively appends during its decoding phase, having a system that can actually manage this KV cache efficiently is very important.
vLLM is a system that introduced an extremely efficient way to manage the KV cache where at any time it showed a significant improvement over existing systems that often wasted up to 60 to 80% of its memory in fragmentation, over allocation, or both. And vLLM introduced what's called the paged attention in order to actually create virtualized KV cache that was inspired by how paging systems work at the operating system level.
How Paged Attention Boosts Efficiency and Its Trade-offs
So, instead of over allocating your space where you prepare for the worst case in terms of how much the model will generate in output, vLLM, instead of padding the memory used in pages that grew in size, which frees up the memory to be used more efficiently as it processes many requests at a time. And not only was this more efficient in the, let's call it, storage Tetris game, it means that the underlying graphics card was busier.
But, it's important to keep in mind that vLLM isn't necessarily an end-all solution. It means to solve throughput problem on a multi-user and multi-requests, but other inference engines prioritize other metrics like or optimizing to the vendor in the case of TensorRT-LLM for Nvidia, as well as better optimization support for running the model for less RAM requirements.
But, learning how to use vLLM to serve LLM as more and more large language models are innovated, but also being able to serve it beyond a single user instances by sharing them across multiple users from server side is going to be an extremely important skill to monitor and optimize how your hardware is being used to provide services. Learning how to use vLLM is going beyond being impressed by an LLM, but actually trying to productize LLM so that you can monitor its tokens per speed and its outputs and its throughput as the number of sessions grow with various prompt requests in sizes also changes dynamically. And managing this exact stack is what vLLM allows you to work to lower the latency and maximize concurrency using vLLM.
Hands-On Lab: Overview and Environment Setup
In this lab, we're going to learn about how vLLM turns a basic LLM setup into a production-ready inference server. We run small LLM 135M with Hugging Face, then with vLLM and compare the speed. We will visualize the KV cache problem, see how paged attention fixes it, launch an API server, stress test it with concurrent users, tune parameters, and build a monitoring dashboard. This lab takes about 40 to 50 minutes to complete.
When I open the lab, I'm dropped right into the scenario. Access the lab using the link in the description below and follow along with me. The intro page tells us that we are an ML engineer at Inference IO, a startup building an LLM as a service platform. Our CEO just signed a deal to serve small LLM to multiple concurrent user. Our current Hugging Face setup handles one request at a time. Our mission is to use vLLM to build a production-ready inference server. The lab has a setup step, eight tasks, and four knowledge checks. The tasks cover Hugging Face baseline, vLLM inference, KV cache problem, paged attention, API server, multi-user load testing, parameter tuning, and a monitoring dashboard.
The first step is to verify our environment. Run the following command and then python/root/code verify_environment.py. This script checks that all required packages like vLLM, Transformers, and Gradio are all installed, downloads the small LLM 135M model, runs a quick test generation, and confirms everything is ready. Wait until you see the environment verification complete message.
Task 1-2: Benchmarking Hugging Face vs. vLLM
Now, we move to task one, which is about running naive Hugging Face inference to establish a baseline. Open /root/code/task1_huggingface_baseline.py. You have to complete two to-dos. At line 30, replace the blank with Hugging Face TB small LLM 135M to load the model, and at line 49, replace the blank with 50 to set the maximum number of new tokens to generate.
Run the script with python/root/code/task1_huggingface_baseline.py.
The output shows the model loading, generating text, and reporting tokens per second. This number is our baseline that vLLM will beat. Now, we move to task two, which is about running the same model with vLLM. Open /root/code/task2_vllm_inference.py. You need to complete two to-dos. At line 36, replace the blank with Hugging Face TB small LLM 135M to initialize the vLLM engine with the same model. At line 40, replace the blank with 0.7 for temperature and 50 for max tokens to create the sampling parameters. Run the script with python/root/code/task2_vllm_inference.py.
The output shows a side-by-side comparison of Hugging Face versus vLLM tokens per second. Notice that vLLM is faster, even though it is the exact same model and same prompt. This proves that the inference engine matters, not just the model. We have a knowledge check about the inference basics. The question asks, "What is a primary metric used to measure how fast an LLM generates output?" The answer is tokens per second because during inference the model produces tokens one at a time through auto regressive decoding. So, tokens per second directly tells you how fast user sees responses.
Task 3-4: The KV Cache Problem & Paged Attention Solution
Now, we move to task three, which is about understanding the KV cache problem. Open /root/code/task3_kvcache_problem.py. You need to complete two to-dos. At line 26, replace the blank with 512 to set the maximum sequence length. At line 52, replace the blank with allocated minus actual divided by allocated times 100 to calculate the wasted percentage. Run the script with python/root/code/task3_kvcache_problem.py.
The output shows how traditional system pre-allocate worst-case memory for every request. Short prompts waste massive amounts of space because they get the same allocation as long as prompts. You will see that memory utilization is only about 20%, meaning 80% is wasted. This is a KV cache bottleneck.
Now, we move to task four, which is about paged attention, the solution that vLLM uses. Open /root/code/task4_paged_attention.py. You need to complete three to-dos. At line 29, replace the blank with 16 to set the page size. At line 51, replace the blank with page size to calculate how many pages each request needs. At line 66, replace the blank with total pages allocated to calculate the paged memory utilization. Run the script with python/root/code/task4_paged_attention.py.
The output compares contiguous allocation against paged allocation. Instead of pre-allocating worst-case memory, paged attention uses small fixed-size pages allocated on demand, just like how operating system manage virtual memory. Memory utilization jumps from about 20% to about 95%. This means four to five times more concurrent users can be served with the same hardware. We have a knowledge check about paged attention. The question asks, "What operating system concept inspired vLLM paged attention mechanism?" The answer is virtual memory paging because just like an OS divides RAM into 4KB pages allocated on demand, vLLM divides the KV cache into token-size pages allocated on demand. This eliminates fragmentation and over allocation.
Task 5: Launching an OpenAI-Compatible API Server
Now, we move to task five, which is about launching vLLM as an OpenAI compatible API server. Open /root/code/task5_api_server.py. You need to complete two to-dos. At line 99, replace the blank with localhost at port 8000 version one for the base URL and not needed for the API key to configure the OpenAI client. At line 105, replace the blank with Hugging Face TB small LLM 135M to set the model for the completion request.
Run the script with python/root/code/task5_api_server.py. The script starts with vLLM server in the background, waits for it to be ready, and then sends a test request using the standard OpenAI SDK. This means any application that already works with the OpenAI API can switch to your self-hosted vLLM server by just changing the base URL. The server stays running for the remaining tasks.
Task 6: Load Testing with Concurrent Users
Now, we move to task six, which is about stress testing the server with concurrent users. Open /root/code/task6_multiuser_load.py. You need to complete two to-dos. At line 97, replace the blank with 1 5 10 20 to define the list of concurrent user counts to test. At line 117, replace the blank with total tokens divided by total time to calculate the aggregate throughput.
Run the script with python/root/code/task6_multiuser_load.py. The output shows a load test table with results for each concurrency level. As the number of concurrent users increases, total throughput goes up because the hardware is being used more efficiently through continuous batching. Per request latency increases slightly, but the overall system produces more tokens per second.
We have a knowledge check about throughput. The question asked, "What is the main reason vLLM achieves high throughput than naive inference engines when serving multiple users?" The answer is, "vLLM efficiently manages the KV cache with paged attention, reducing memory waste because by using paging instead of contiguous pre-allocation, more concurrent requests fit in memory at once and the hardware stays busier processing tokens.
Task 7: Tuning vLLM for Production Workloads
Now, we move to task seven, which is about tuning vLLM parameters for production. Open /root/code/task7_tuning.py. You need to complete two to-dos. At line 164, replace the blank with 64 to set a shorter context length for the configuration. At line 173, replace the blank with eight to limit the number of concurrent sequences. Run the script with python/root/code/
task7_tuning.py.
The script restarts the vLLM server with different configurations and benchmarks each one. You will see how lowering max model length reduces memory per request while limiting max number steps controls how many requests are processed at once. The right tuning depends on your workload. Short prompts need lower max model length and many users need higher max number of sequences.
Task 8: Building a Gradio Monitoring Dashboard
Now, we move to task eight, which is the capstone. Open /root/code/
task8_dashboard.py. You need to complete three to-dos. At line 85, replace the blank with requests.post to send a test request to the vLLM server for live metrics. At line 114, replace the blank with huggingface_tps vllm_tps to set the tokens per second values in the comparison chart. At line 119, replace the blank with vllm_tps divided by huggingface_tps to calculate the improvement ratio.
Run the script with python/root/code/task8_dashboard.py. Then, click the Gradio UI button in the top right to view the dashboard. The dashboard shows a comparison chart of huggingface versus vLLM performance, the improvement ratio, live inference metrics, load test results, and tuning configuration comparisons. This is a simplified version of the monitoring you would use in production with tools like Prometheus and Grafana.
We have a final knowledge check about the tradeoffs between inference engines. The question asked, "When might you choose llama.cpp over vLLM?" The answer is, "When running inference on CPU/RAM without a GPU and needing optimized CPU performance because llama.cpp is specifically optimized for CPU and RAM inference on consumer hardware, while vLLM excels at high throughput multi-user serving on GPU."
Final Review and Key Takeaways
Before wrapping up, I want to highlight a few things. Inference engine matters more than you think. The same model runs at different speeds depending on the engine. KV cache fragmentation wastes 60 to 80% of memory in traditional systems. Paged attention fixes this by borrowing the virtual memory paging concept from operating systems. vLLM OpenAI compatible API means zero code changes when migrating from OpenAI. Always tune max model length and max number sequences for your specific workload. Monitor tokens per second and latency in production to know when to scale.
That is it. We went from naive huggingface setup that handles one request at a time to a production-ready vLLM server with live monitoring. We measured baseline performance, saw vLLM speed average, understood why the KV cache is the bottleneck, learned how paged attention solves with OS-inspired paging, launched an OpenAI compatible API server, stress-tested it with up to 20 concurrent users, tuned the parameters for production workloads, and built a Gradio monitoring dashboard. You now understand why companies use inference engines like vLLM to serve LLMs efficiently at scale.
