vLLM
Deep dive: vLLM + what it brings to OpenShift AI / Hugging Face / AI Labs
Nice question — vLLM is one of the most practically important open-source inference engines for modern LLMs. Below I’ll explain what vLLM is, the core technical tricks that give it big gains, how Red Hat is using it in OpenShift AI, and why you’d want to put vLLM in front of Hugging Face / ai-labs base models (and important caveats).
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tl;dr • vLLM is a high-throughput, memory-efficient inference and serving engine for autoregressive LLM workloads that implements smart KV-cache management, continuous/dynamic batching, efficient scheduling and production server features.  • Red Hat has integrated vLLM into its OpenShift AI / RHEL AI stack as a supported inference engine (OpenShift model optimization / inference layer), enabling autoscaling, profiling and GPU acceleration workflows on enterprise platforms.  • Why put vLLM in front of base LLMs (Hugging Face / ai Labs): you typically get much higher throughput, lower time-to-first-token (TTFT), better GPU memory utilization (serve larger models or more concurrent requests), and production features (quantization, monitoring, OpenAI-compatible endpoints). 
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What vLLM actually is (short)
vLLM started at UC Berkeley’s Sky Lab and is now community-driven: a Python/C++ production server that replaces naïve transformer generation loops with a set of runtime systems and memory/data structures tuned for real-world serving. It supports many HF models and exposes features needed for production (token streaming, OpenAI-compatible REST/WS modes, logging, metrics). 
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Core technical tricks that make the difference 1. Paged / Memory-efficient KV cache (paged attention) Instead of keeping a monolithic, per-request KV cache for every token generated, vLLM pages and shares KV memory across requests, reducing fragmentation and waste. That lets you host much larger effective context windows and/or serve many more concurrent sessions per GPU. The claimed memory savings are large (orders-of-magnitude in some workloads) in practice.  2. Continuous / dynamic batching vLLM uses a continuous batching scheduler that accumulates partial requests (prefill + generate phases) and forms efficient GPU batches without inflating latency for short requests. This reduces idle GPU cycles and dramatically improves throughput while keeping p50 latency reasonable. It excels when requests are short or variable in length.  3. Smart scheduling & prefill/generation separation It separates the heavy “prefill” (processing prompt tokens) from the incremental generation steps, scheduling work to maximize throughput and reduce time-to-first-token for interactive apps.  4. Tensor / model parallel support + quantization hooks vLLM plugs into common parallelism mechanisms (tensor parallelism) and supports quantized weights and model formats used by Hugging Face / other open models, allowing larger models across multiple GPUs or lower precision to cut memory/use.  5. Production server features OpenAI-compatible endpoints, streaming, batching knobs, profiling tooling and observability integrations make vLLM ready for real deployments. Hugging Face and other platforms document vLLM as an engine option because of these features. 
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How Red Hat/OpenShift is using vLLM
Red Hat has incorporated vLLM as a first-class inference engine inside the OpenShift AI / RHEL AI stack and the OpenShift model optimization layer. That integration focuses on: • Managed deployment on OpenShift (KServe / RawDeployments / Serverless modes), with GPU profiling and autoscaling patterns.  • Enterprise operationalization (packaging, best-practice config, profiling guides for RHEL + NVIDIA GPUs). 
Put simply: OpenShift AI gives you cluster/CI/CD/operations + K8s autoscaling and vLLM gives you efficient model serving on the GPUs inside that platform.
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Concrete advantages of using vLLM before calling a base model (Hugging Face / ai Labs) 1. Higher throughput (requests per GPU per second) Continuous batching + memory efficiency yields much larger effective batch sizes and throughput, often many× improvement versus naive per-request serving. This reduces cost per query on cloud GPUs.  2. Lower time-to-first-token (TTFT) for interactive UX Scheduling optimizations reduce the latency before the first token is returned (important for chatbots and streaming apps).  3. Serve bigger models on given hardware (or more concurrent sessions) Because of KV cache paging and memory sharing, you can host models that previously needed more GPU memory, or you can serve more connections on the same GPUs. That’s a real dollar-saver.  4. Better cost predictability and autoscaling on OpenShift With OpenShift AI + vLLM you can integrate GPU metrics, KServe autoscaling patterns, and enterprise schedulers to scale model servers sensibly.  5. Easier integration and tooling vLLM plugs into Hugging Face toolchains (docs and TRL show direct integration examples) and can expose OpenAI-like APIs so clients and higher-level orchestration don’t need to change. 
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Practical how-to / deployment notes (high level) • If you run Hugging Face weights: run a vLLM server (it natively reads HF model formats) and configure continuous batching, quantization (if desired), and token streaming. Use the HF docs for vLLM engine specifics.  • On OpenShift: use Red Hat’s OpenShift AI/KServe integration recipe for vLLM; configure autoscaling rules suited to GPU workloads (OpenShift docs show Serverless mode + KServe patterns).  • Tuning knobs: prefill batch size, max generation batch, latency vs throughput targets, KV cache eviction/page sizes, quantization level. The vLLM docs have optimization guidance and profiling tools. 
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When vLLM is not the right tool (caveats) • Non-autoregressive or large multimodal pipelines: vLLM is optimized for autoregressive/text generation; some multimodal or encoder-only workloads may be better served by specialized servers.  • Model/format compatibility: check version compatibility (some TRL / HF integrations require specific vLLM versions). Always verify the model architecture is supported.  • Edge or tiny-device use: on-device tiny runtimes may still prefer smaller optimized runtimes rather than a full GPU server. 
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Example quick architecture (text)
Client apps → API gateway → OpenShift ingress → vLLM server pods (continuous batching, KV cache paging, tensor parallel across GPUs) → model weights (Hugging Face format stored in PVC / S3) → results streamed back. OpenShift provides autoscaling, monitoring and lifecycle. 
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Some recommendations 1. If you’re running generative chat or streaming apps at scale, run vLLM as the inference engine — you’ll almost always reduce GPU cost and improve UX vs a naïve HF/transformers server.  2. On OpenShift, use the OpenShift AI / RHEL AI vLLM integration to get enterprise operational features (profiling, autoscale patterns).  3. Benchmark your workload (time-to-first-token, p50/p95 latency, throughput, GPU memory) — real gains depend on prompt lengths, concurrency profile, and hardware. Use the vLLM profiling guidance from Red Hat / vLLM docs. 
⸻ Absolutely. Here is a deep dive into the vLLM inference engine, its role in Red Hat OpenShift, and the powerful advantages it offers. First, let's clarify a key point from your question: vLLM isn't a tool you use before calling an LLM from Hugging Face. Instead, vLLM is a high-performance engine that runs the LLM. Think of it this way:
A Hugging Face model (like Llama 3 or Mistral) is like a complex, highly-trained brain (the "weights").
A standard inference script (like the default Hugging Face transformers library) is a basic way to ask that brain a question. It's functional but not very fast or efficient.
vLLM is a custom-built, high-performance operating system for that brain. It's designed to run the model at maximum speed and serve thousands of simultaneous requests, which the basic script simply can't handle. 🚀 Deep Dive: How vLLM Works vLLM is an open-source library from UC Berkeley designed to solve the biggest bottleneck in LLM inference: memory. The core problem isn't just the math (matrix multiplications); it's managing the Key-Value Cache (KV Cache).
What is the KV Cache? When an LLM generates text, it's a step-by-step process. To generate the next word, the model must "look back" at all the words (tokens) that came before it. The KV Cache is the model's short-term memory, storing the mathematical representations (Keys and Values) of all previous tokens.
The Problem: This cache grows with every single token generated. In traditional systems (like the base Hugging Face transformers), this creates massive memory problems:
Massive Waste (Internal Fragmentation): You have to pre-allocate a huge, contiguous block of GPU memory for every single request, big enough for the maximum possible text length (e.g., 8,192 tokens). If a user's request only ends up using 500 tokens, the other 7,692 tokens' worth of memory is completely wasted.
Scheduling Nightmares (External Fragmentation): Even if you have 10GB of free GPU memory, you might not be able to fit a new 8GB request if that free memory is in small, non-contiguous chunks. The "Secret Sauce": PagedAttention 🧠 vLLM's core innovation is PagedAttention. It borrows a brilliant concept that's been used in operating systems for decades: virtual memory and paging. Instead of allocating one giant, contiguous block of memory, PagedAttention does this:
Divides Memory into Blocks: It divides the GPU's KV cache memory into small, fixed-size "blocks" (or "pages").
Allocates On-Demand: As a sequence gets longer, vLLM gives it new blocks, one at a time. These blocks do not need to be next to each other in memory.
Uses a "Page Table": A central "page table" keeps track of which blocks (in physical memory) belong to which request (in logical memory). This elegant solution has incredible benefits:
Near-Zero Waste: Memory is allocated as needed. A 500-token sequence uses memory for ~500 tokens, not 8,192. This means you can fit far more concurrent users on the same GPU.
Solves Fragmentation: Since blocks can be anywhere, the "external fragmentation" problem disappears.
Efficient Sharing (Copy-on-Write): This is a huge one. If you have 10 users all starting with a similar prompt (like a RAG system feeding the same document), vLLM can have all 10 requests share the exact same memory blocks for that prompt. It only copies blocks when one user's generation diverges. This saves massive amounts of memory and compute. The Second Pillar: Continuous Batching 🔄 Traditional "static batching" is inefficient. You group 8 requests, send them to the GPU, and then must wait for all 8 to finish before starting the next batch. If 7 finish in 1 second but 1 takes 10 seconds, your GPU sits 87% idle. vLLM uses continuous batching (or "dynamic batching").
A request queue feeds the GPU.
As soon as any single request in the batch finishes, vLLM immediately evicts it from the GPU and swaps in a new request from the queue.
This keeps the GPU busy at nearly 100% utilization, dramatically increasing throughput (the number of requests you can serve per second). 🔴 vLLM on Red Hat OpenShift AI Red Hat has integrated vLLM as a core part of its OpenShift AI platform, specifically as the Red Hat AI Inference Server. In an enterprise environment like OpenShift, you're not just running a script; you're deploying scalable, secure, and manageable services on a Kubernetes cluster. Here’s vLLM's role:
Optimized Serving Runtime: When you deploy an LLM (like one from Hugging Face) on OpenShift AI, you can select vLLM as the "serving runtime." This means OpenShift automatically packages your model to be run by the vLLM engine instead of a basic Python server.
Hardware Acceleration: It's designed to get the most out of the expensive GPUs (like NVIDIA A100s or H100s) in your cluster.
Enterprise-Ready: It gives you all the performance benefits of vLLM (high throughput, low cost) within the secure, scalable, and monitored framework of OpenShift. Businesses can self-host models with performance that rivals proprietary APIs, but with full data control. ✅ Advantages of Using vLLM As clarified, this is a comparison between using vLLM as your server vs. using the standard Hugging Face transformers library for inference. | Feature | Standard Hugging Face transformers | vLLM (with PagedAttention) | |---|---|---| | Throughput | Low. Bottlenecked by static batching and memory. | Extremely High. Up to 24x higher throughput. | | Memory | Very Inefficient. Wastes huge amounts of memory. | Highly Efficient. Near-zero fragmentation. | | Batching | Static. Waits for the slowest request in a batch. | Continuous. Keeps the GPU at ~100% utilization. | | Cost | High. You need many GPUs to serve few users. | Low. You can serve many more users on a single GPU. | | Use Case | Good for research, experimentation, and single-user tasks. | Built for production. Ideal for high-traffic applications. |
In short, using vLLM allows you to serve LLMs faster, cheaper, and to more users on the same hardware. Its integration into Red Hat OpenShift makes this production-grade performance accessible within an enterprise-grade, cloud-native platform.
Here is a step-by-step guide. You've hit on a key term: "Agent API." This is the most common and powerful way to use vLLM. The idea is to run vLLM as a dedicated, high-performance web server, which then exposes an OpenAI-compatible API. Your "agent" (or any application) then makes standard API calls to your vLLM server, just as it would to OpenAI's GPT-4. This lets you swap proprietary models for open-source models with zero code changes in your agent. Let's walk through this pattern. 📦 Step 1: Installation First, you'll need to install vllm and the openai client library. We use the openai library because vLLM's server mimics the OpenAI API.
This installs the vLLM core engine
pip install vllmThis installs the client library we'll use to "call" our agent's API
pip install openai🚀 Step 2: Run the vLLM Server (The "API") This is the main step. We will start the vLLM server, which will load our model and expose it at a local API endpoint. For this example, we'll use mistralai/Mistral-7B-Instruct-v0.1, a popular model that doesn't require any special access tokens. Open your first terminal and run this command:
python -m vllm.entrypoints.api_server \
--model "mistralai/Mistral-7B-Instruct-v0.1" \
--host "0.0.0.0" \
--port 8000Let's break that down:
python -m vllm.entrypoints.api_server: This is the command to start vLLM's built-in server.
--model "...": This tells vLLM which model to load from the Hugging Face Hub.
--host "0.0.0.0": This allows the server to be accessible from other machines on your network (e.g., your agent).
--port 8000: The port to run on. You will see a lot of output as vLLM downloads the model and starts the server. Once it's ready, you'll see a message like Uvicorn running on http://0.0.0.0:8000. This terminal is now your live server. Leave it running.
🤖 Step 3: Call the Server (The "Agent" Script) Now, your "agent" can use this API. Open a second terminal and create a Python script (e.g., agent.py). This script will be our "agent" that needs to call an LLM. Here is the code for agent.py:
from openai import OpenAI
# 1. Point our client to the local vLLM server
client = OpenAI(
base_url="http://localhost:8000/v1", # Connects to our vLLM server
api_key="not-needed" # API key is not required for local server
)
# 2. Define the messages for the chat
messages = [
{"role": "user", "content": "What is the PagedAttention mechanism in vLLM?"}
]
# 3. Call the vLLM server just like the OpenAI API
print("Calling the vLLM API...")
completion = client.chat.completions.create(
model="mistralai/Mistral-7B-Instruct-v0.1", # Must match the model on the server
messages=messages,
temperature=0.7,
max_tokens=500
)
# 4. Print the response
print("\nResponse from vLLM:")
print(completion.choices[0].message.content)Now, run this script from your second terminal: python agent.py ✨ What Happens
Your agent.py script will send an HTTP request to http://localhost:8000.
Your vLLM server (running in the first terminal) will receive this request.
vLLM will use its PagedAttention engine to process the request extremely fast.
It will send the completed text back to agent.py.
Your agent script will print a detailed explanation of PagedAttention, generated 100% locally on your machine. Alternative: Using vLLM as a Library (for simple scripts) If you aren't building an "agent API" and just want to run a simple Python script, you can use vLLM as a library. This is good for offline batch processing. Create a single file batch_job.py: from vllm import LLM, SamplingParams
1. Define the prompts you want to process
prompts = [ "The capital of France is", "The opposite of hot is", "What is the PagedAttention mechanism in vLLM?" ]
2. Set sampling parameters
(e.g., temperature=0 for deterministic output, max_tokens)
sampling_params = SamplingParams(temperature=0.0, max_tokens=50)
3. Load the LLM engine
This will download the model on first run
print("Loading model...") llm = LLM(model="mistralai/Mistral-7B-Instruct-v0.1") print("Model loaded.")
4. Generate the outputs (this is batched automatically)
print("Generating outputs...") outputs = llm.generate(prompts, sampling_params) print("Generation complete.")
5. Print the results
for output in outputs: prompt = output.prompt generated_text = output.outputs[0].text print(f"---") print(f"Prompt: {prompt}") print(f"Generated: {generated_text.strip()}")
When you run python batch_job.py, vLLM will load the model, process all three prompts in a single optimized batch, and print the results. For 99% of production "agent" use cases, the API Server (Option 1) is the pattern you want. It separates your application logic from the model inference, which is a much more scalable and robust architecture. Would you like to explore how to stream the response from the vLLM server, so your agent can get tokens back one by one?
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