What is vLLM? A Deep Dive Into High-Throughput LLM Serving

When I first started deploying large language models, I assumed that choosing the right model was the hardest decision. I spent weeks comparing Llama variants, Mistral builds, quantized checkpoints, and instruction-tuned releases. What I didn’t anticipate was that the real bottleneck would not be model quality, it would be inference efficiency.

The turning point came when I realized my GPUs were not saturated, yet performance was inconsistent. Memory usage was high, throughput was lower than expected, and scaling horizontally meant simply adding more hardware instead of optimizing what I already had. That’s when I began exploring vLLM more seriously through the official documentation at https://docs.vllm.ai/en/latest/ and reviewing the core architecture discussions on https://blog.vllm.ai/.

What I discovered is that vLLM is not “another AI tool.” It is a rethinking of how large language models should be served.

Understanding What vLLM Actually Is

Before evaluating whether vLLM is better than Ollama or whether it’s worth using at all, it’s critical to clarify something foundational: vLLM is not a model. It is an inference engine designed to serve models efficiently.

A large language model (LLM) is the neural network itself, such as Llama 3, Mistral, Mixtral, Falcon, or OPT. Those models contain the weights and the learned knowledge.

vLLM, by contrast, is the runtime system that loads those models into GPU memory and handles the process of generating tokens efficiently across concurrent requests.

This distinction is crucial because many deployment problems attributed to “model performance” are actually infrastructure inefficiencies. The model might be perfectly capable, but the serving stack wastes memory, batches inefficiently, or fails under load.

The project, originally developed by researchers at UC Berkeley and now maintained as an open-source initiative at https://github.com/vllm-project/vllm, was designed to solve that exact systems-level problem.

The Real Problem vLLM Solves: KV Cache Memory Waste

To understand why vLLM matters, it helps to examine the bottleneck in transformer inference: the Key-Value (KV) cache.

During text generation, transformers store intermediate attention states so they do not need to recompute them at every token step. This KV cache grows with sequence length and number of concurrent requests. In traditional implementations, this cache is allocated in large contiguous memory blocks.

That approach sounds simple, but in practice it leads to massive GPU memory fragmentation. When serving multiple users with different prompt lengths, memory becomes partially unused yet still unavailable. In real-world workloads, this waste can exceed 50 percent of allocated memory.

vLLM introduces PagedAttention, which is thoroughly described across its technical documentation at https://docs.vllm.ai/en/latest/ and discussed in architectural breakdowns such as https://www.redhat.com/en/topics/ai/what-is-vllm.

PagedAttention borrows concepts from virtual memory systems used in operating systems. Instead of storing KV cache in rigid contiguous memory regions, it stores cache entries in non-contiguous memory pages and dynamically maps them when computing attention.

The impact of this design is substantial. Memory waste drops dramatically. More sequences can fit into the same GPU memory. That increases effective concurrency without requiring additional hardware.

When I first tested this under concurrent workloads, the improvement wasn’t marginal; it was structural. The GPU utilization curve flattened, meaning hardware stayed consistently active instead of oscillating between peaks and idle periods.

Continuous Batching and Why It Changes Concurrency Economics

Another critical innovation in vLLM is continuous batching. Traditional inference engines often rely on static batching: requests are grouped together, processed, and the system waits until the entire batch completes before accepting new sequences.

This approach creates inefficiencies under uneven workloads. If one request in the batch takes longer, all other requests wait.

vLLM uses iterative scheduling. As soon as one sequence completes, another request can immediately enter the batch. The GPU is never waiting for an entire group to finish before accepting new work.

In performance analyses such as the comparison shared at https://robert-mcdermott.medium.com/performance-vs-practicality-a-comparison-of-vllm-and-ollama-104acad250fd and community discussions like https://www.reddit.com/r/LocalLLaMA/comments/1p9t5c8/is_vllm_worth_it/, users consistently highlight improved throughput under concurrent load.

In my own deployments, this translated to higher tokens-per-second throughput at the same GPU allocation. That directly affects cost per request, which is often the most important metric in production AI systems.

Installation Experience and Infrastructure Requirements

The installation process and the GPU-specific guide make something very clear: vLLM assumes you are working in a CUDA-capable environment.

This is not a lightweight desktop tool. It is optimized primarily for NVIDIA GPUs and requires proper driver and CUDA configuration.

Setting it up felt closer to deploying a backend service than running a local AI experiment. Once installed, the Quickstart guide allows you to launch an OpenAI-compatible API server.

That compatibility is a significant architectural advantage. Applications already built around OpenAI’s API format can integrate with vLLM without rewriting request logic. This reduces adoption friction and simplifies migration from hosted APIs to self-hosted infrastructure.

Model Support and Advanced Capabilities

vLLM is designed to serve a wide range of modern transformer-based models rather than being limited to a single ecosystem. According to its supported models documentation , it works with major architectures such as Llama 2 and Llama 3, Mistral, Mixtral (including Mixture-of-Experts models), Falcon, OPT, and other Hugging Face-compatible decoder-only models. This broad compatibility is important because these architectures differ significantly in attention mechanisms, parameter sizes, routing logic, and tokenizer behavior. vLLM abstracts those differences at the serving layer, allowing teams to swap or upgrade models without redesigning their infrastructure.

Beyond basic text generation, vLLM also supports structured tool calling. This enables models to generate structured outputs, such as JSON-based function calls, which can trigger external APIs, database queries, or backend services. In practice, this makes vLLM suitable for agent-style systems where the model does more than chat, it can decide to call tools, process results, and continue reasoning. Combined with OpenAI-compatible API support and multi-GPU tensor parallelism, these capabilities position vLLM as a scalable inference backbone for production-grade AI systems, not just a chatbot runtime.

vLLM vs Ollama: A Nuanced Comparison

Before comparing them side by side, it’s important to understand that vLLM and Ollama are built with fundamentally different goals in mind.

Ollama is designed for simplicity. It focuses on making large language models easy to download, run, and experiment with locally. You install it, pull a model, and start generating text within minutes. It hides GPU configuration complexity and abstracts infrastructure details. That makes it ideal for individual developers, researchers, and small teams who want quick experimentation without worrying about batching strategies, memory allocation, or distributed serving.

vLLM, on the other hand, is engineered for performance at scale. It is built to maximize GPU utilization, optimize memory through PagedAttention, and handle high levels of concurrency efficiently. Instead of prioritizing ease of setup, it prioritizes throughput, cost efficiency, and production deployment flexibility. It assumes you are building backend infrastructure, not just running a local chatbot.

In simple terms, Ollama optimizes for developer convenience. vLLM optimizes for infrastructure efficiency.

If the goal is quick local testing or personal projects, Ollama feels smoother and more approachable. If the goal is serving many users simultaneously with optimized GPU cost, vLLM is architecturally stronger.

vLLM vs Ollama Comparison Table

In practical terms, the difference comes down to scale and intent. Ollama reduces friction for getting started. vLLM reduces cost and inefficiency when scaling.

Advantages of vLLM in Production Environments

The most significant advantages of vLLM include:

1. Superior GPU memory utilization through PagedAttention.
2. Continuous batching for stable high throughput.
3. OpenAI-compatible API server for integration simplicity.
4. Support for quantization formats such as AWQ and FP8.
5. Multi-GPU tensor parallelism capabilities.

The cumulative effect of these features is reduced cost per generated token at scale. For organizations deploying LLM-backed services, this can materially impact operating expenses.

Limitations and Practical Considerations

While vLLM delivers strong performance benefits, especially under concurrency, it is important to understand where its constraints lie before deciding to adopt it.

The first and most obvious limitation is hardware dependency. vLLM is built and optimized primarily for NVIDIA GPUs using CUDA. Although there is ongoing ecosystem evolution around alternative accelerators, real-world production performance today is most reliable on CUDA-enabled GPUs. Running vLLM purely on CPU is technically possible for testing purposes, but it is not practical for serious workloads. CPU inference with large transformer models is dramatically slower and defeats the purpose of vLLM’s high-throughput design. If your environment does not have GPU infrastructure or budget for it, vLLM may not be the right starting point.

The second major consideration is VRAM management. vLLM achieves its performance through advanced batching and memory optimization mechanisms like PagedAttention, but those gains do not eliminate memory limits. In high-concurrency scenarios, especially when serving long-context prompts or large models (such as 13B, 30B, or 70B parameters), GPU memory can still be exhausted if configuration is not tuned carefully. Batch size, maximum sequence length, tensor parallel settings, and KV cache allocation all influence memory consumption. Without understanding these trade-offs, it is possible to trigger out-of-memory errors under load. In production environments, this requires monitoring, benchmarking, and sometimes iterative tuning.

Another practical limitation is deployment complexity. Unlike plug-and-play tools such as Ollama, which abstract away most system details, vLLM expects you to think about infrastructure. You need to consider GPU drivers, CUDA versions, model quantization formats, API server configuration, and potentially multi-GPU orchestration. If you are deploying at scale, you may also need containerization, load balancing, and monitoring tools. This does not mean vLLM is difficult, but it is infrastructure-oriented rather than beginner-friendly.

Additionally, while the documentation is comprehensive and well-structured, it assumes familiarity with concepts such as tensor parallelism, distributed inference, quantization, and batching strategies. Developers without prior exposure to deep learning infrastructure may find the learning curve steeper compared to more consumer-oriented tools. Understanding how attention caching works, why batching affects latency, or how multi-GPU parallelism distributes weights is often necessary for effective deployment.

These practical constraints mean that vLLM is not ideal for casual experimentation, educational exploration, or early-stage prototyping. It shines in environments where performance efficiency, scalability, and cost optimization matter more than ease of setup. For beginners, local experimentation tools may offer a smoother entry point. For teams operating production AI systems, the added complexity of vLLM is usually justified by its throughput and infrastructure advantages.

Final Perspective

After deploying vLLM and comparing it against simpler serving stacks, I realized that the performance gains are not incremental. They represent a shift in how inference infrastructure should be designed.

vLLM does not improve the intelligence of a model. It improves the efficiency of delivering that intelligence.

In environments where concurrency, cost optimization, and scalability matter, that efficiency becomes the defining factor.

For experimentation, lighter tools remain valuable.

For production AI systems with meaningful load, vLLM feels less like an optional optimization and more like the correct architectural layer.

And that is the key difference.

FAQs

What is vLLM for?
vLLM is an open-source high-performance inference engine designed to serve large language models efficiently, especially in production environments with high concurrency.

What is the difference between vLLM and LLM?
An LLM (Large Language Model) is the model itself (like Llama or Mistral). vLLM is the engine that runs and serves that model efficiently.

Is vLLM better than Ollama?
For high-throughput, production-scale serving, vLLM is generally more efficient. For simple local experimentation and ease of setup, Ollama is often easier.

Who developed vLLM?
vLLM was originally developed by researchers at UC Berkeley and is now maintained by the open-source vLLM community.

Should I use vLLM?
Use vLLM if you’re deploying models in production, serving many users, or optimizing GPU cost.