Introduction
Production large‑language‑model (LLM) inference has evolved from a simple API call that internally runs a generate() loop into a complex systems problem that spans hardware, software, and operations. In the early days of LLM deployment, the focus was on the model itself: how many layers, what tokenizer, and which attention mechanism would deliver the best perplexity. Today, however, the bottleneck is almost always the inference stack that sits between the GPU and the end‑user request. The choice of stack determines how many tokens can be processed per second, the tail latency that users experience, and ultimately the cost per million tokens (CPM) on a given GPU fleet.
Four stacks have emerged as the most widely adopted in production environments: vLLM, TensorRT‑LLM, Hugging Face Text Generation Inference (HF TGI), and LMDeploy. Each of these stacks implements a different set of optimizations—ranging from paged attention and kernel fusion to dynamic batching and model partitioning—to squeeze out performance from modern GPUs. The goal of this article is to dissect these stacks at a technical level, comparing their architectural choices, performance characteristics, and operational trade‑offs. By the end, you should be able to match the right stack to your workload profile, whether you prioritize raw throughput, low tail latency, or ease of deployment.
Main Content
vLLM: Paged Attention and Dynamic Batching
vLLM has quickly become the de‑facto baseline for high‑throughput inference. Its core innovation is the use of paged attention, a memory‑efficient mechanism that stores key‑value tensors on the GPU in a compressed format and only loads the pages required for the current token. This approach eliminates the quadratic memory overhead that plagues traditional attention, allowing vLLM to run models with billions of parameters on a single GPU without swapping to host memory.
Beyond memory efficiency, vLLM implements a sophisticated dynamic batching engine. Incoming requests are grouped by token length and scheduled in a way that maximizes GPU occupancy while respecting latency budgets. The scheduler uses a cost‑based model to decide whether to wait for more requests or to fire a batch immediately. This fine‑grained control is what gives vLLM its reputation for high tokens‑per‑second (TPS) on workloads such as chat‑style interactions or batch inference jobs.
However, the dynamic batching logic introduces a small amount of overhead, especially for workloads with highly variable request sizes. In practice, a well‑tuned vLLM deployment can achieve 2–3× higher TPS than a naive single‑request pipeline, but the tail latency can suffer if the scheduler mis‑predicts the arrival pattern.
TensorRT‑LLM: NVIDIA‑Optimized Kernel Fusion
TensorRT‑LLM takes a different approach by leveraging NVIDIA’s TensorRT runtime to fuse multiple operations into a single CUDA kernel. The stack is tightly coupled to the CUDA ecosystem, which means it can exploit the latest GPU features such as Tensor Cores, dynamic parallelism, and memory‑coalescing optimizations.
At the heart of TensorRT‑LLM is a custom kernel that performs the matrix multiplications for self‑attention and the feed‑forward network in one pass. By eliminating intermediate memory writes, the kernel reduces memory traffic and latency. Additionally, TensorRT‑LLM supports mixed‑precision inference out of the box, allowing developers to trade a few bits of accuracy for a significant speedup.
One of the key strengths of TensorRT‑LLM is its ability to handle model partitioning across multiple GPUs. The stack can split a large model into shards, each residing on a different device, and orchestrate cross‑GPU communication via NCCL. This makes it suitable for inference on clusters where a single GPU cannot hold the entire model.
The trade‑off is that TensorRT‑LLM requires a more involved build process. Developers must compile the model into a TensorRT engine, which can be time‑consuming and may not support every custom layer that a research model might include. Nevertheless, for production workloads that prioritize deterministic latency and maximum throughput, TensorRT‑LLM offers a compelling solution.
HF TGI: Simplicity and Flexibility
Hugging Face Text Generation Inference (HF TGI) focuses on providing a lightweight, Python‑centric interface that can run on a variety of hardware. Unlike vLLM and TensorRT‑LLM, HF TGI does not rely on heavy kernel fusion; instead, it uses the Hugging Face Transformers library as a backend and wraps it in a FastAPI server.
The simplicity of HF TGI is its biggest advantage. Developers can spin up a container in minutes, load any model from the Hugging Face Hub, and start serving requests with minimal configuration. The stack also supports dynamic batching, but its implementation is more straightforward and less aggressive than vLLM’s scheduler.
Because HF TGI uses the standard PyTorch or TensorFlow execution paths, it can run models that include custom layers or non‑standard attention mechanisms without modification. This flexibility makes it a popular choice for research teams that need to prototype new architectures quickly.
On the downside, HF TGI’s performance is typically lower than that of vLLM or TensorRT‑LLM. The lack of kernel fusion and the overhead of the Python runtime mean that TPS can be 30–50 % lower on the same hardware. However, for workloads where development speed and model compatibility outweigh raw throughput, HF TGI remains a viable option.
LMDeploy: Cloud‑Native Deployment with Kubernetes
LMDeploy is a relatively new entrant that targets cloud‑native deployments. It is built on top of Kubernetes and offers a declarative configuration model that abstracts away the underlying inference engine. Under the hood, LMDeploy can plug in any of the aforementioned stacks, but it adds a layer of orchestration, autoscaling, and monitoring.
The key feature of LMDeploy is its ability to automatically scale inference pods based on request latency and queue depth. By integrating with Prometheus and Grafana, it provides real‑time dashboards that show TPS, tail latency, and GPU utilization. This observability is critical for production environments where SLA guarantees are required.
Because LMDeploy is agnostic to the inference engine, it can run a vLLM cluster on one set of nodes and a TensorRT‑LLM cluster on another, all managed through the same Kubernetes manifests. This flexibility allows organizations to experiment with different stacks without redeploying the entire infrastructure.
The trade‑off is that LMDeploy introduces additional operational complexity. Kubernetes operators, Helm charts, and custom resource definitions (CRDs) require a learning curve. Moreover, the overhead of containerization and the Kubernetes control plane can slightly increase latency compared to a bare‑metal deployment.
Comparative Analysis
When comparing these stacks, several dimensions emerge: memory efficiency, throughput, latency, ease of deployment, and ecosystem support. vLLM shines in raw throughput for chat‑style workloads thanks to its paged attention and dynamic batching. TensorRT‑LLM offers the lowest latency for workloads that can afford the build time and are tightly coupled to NVIDIA hardware. HF TGI provides the fastest time‑to‑market for research prototypes, while LMDeploy excels at operational scalability and observability.
Cost per million tokens is a function of both TPS and tail latency. A stack that achieves higher TPS but suffers from higher tail latency may end up costing more if the SLA requires low latency. Conversely, a stack with slightly lower TPS but consistently low tail latency can be more economical in a user‑centric service where every millisecond matters.
Ultimately, the choice of stack should be driven by the specific workload profile. If your service is heavily chat‑centric with unpredictable request sizes, vLLM’s dynamic batching is likely the best fit. If you are deploying a large model on a GPU cluster and need deterministic latency, TensorRT‑LLM’s kernel fusion and model partitioning are advantageous. For rapid prototyping or models with custom layers, HF TGI offers unmatched flexibility. And if you need to manage hundreds of inference pods across a cloud cluster, LMDeploy’s Kubernetes integration is indispensable.
Conclusion
The landscape of production LLM inference has matured to the point where the inference stack is a critical determinant of performance, cost, and operational reliability. vLLM, TensorRT‑LLM, HF TGI, and LMDeploy each bring a unique set of optimizations and trade‑offs to the table. By understanding the underlying mechanisms—paged attention, kernel fusion, dynamic batching, and cloud‑native orchestration—engineers can make informed decisions that align with their business goals and technical constraints.
In practice, many organizations adopt a hybrid approach: using vLLM for high‑throughput batch jobs, TensorRT‑LLM for latency‑sensitive endpoints, HF TGI for research prototypes, and LMDeploy to glue everything together in a scalable Kubernetes environment. This multi‑stack strategy allows teams to leverage the strengths of each solution while mitigating their weaknesses.
Call to Action
If you’re ready to elevate your LLM deployment, start by profiling your workload: measure token distribution, request arrival patterns, and latency requirements. From there, experiment with the stacks that best match your profile—run a quick benchmark with vLLM on a single GPU, try TensorRT‑LLM’s engine builder for a larger model, or spin up an HF TGI container to prototype a new architecture. Finally, consider wrapping your chosen stack in LMDeploy or a similar orchestration layer to gain real‑time visibility and autoscaling. By taking these steps, you’ll transform your LLM inference from a bottleneck into a competitive advantage.