Introduction
The model serving platform is the software layer between your model weights and your API consumers. It handles request scheduling, memory management, batching, and GPU execution. The choice of serving platform has a larger impact on throughput and cost than almost any other infrastructure decision.
The landscape has evolved rapidly. vLLM emerged as the leading open-source LLM serving framework with its PagedAttention innovation. TensorRT-LLM provides maximum throughput for NVIDIA hardware. Triton Inference Server supports diverse model types. BentoML and Ray Serve provide higher-level abstractions for deployment.
This guide provides a deep technical comparison of production serving platforms, covering architecture, performance characteristics, deployment complexity, and the scenarios where each platform excels.
Leading open-source LLM serving
TensorRT-LLM throughput advantage
Model types supported by Triton
vLLM deployment time with Docker
vLLM
vLLM is the leading open-source LLM serving framework, developed at UC Berkeley and now maintained by a large community. Its core innovation is PagedAttention — virtual memory management for KV cache — which enables near-zero memory waste and high batch sizes.
vLLM supports continuous batching, speculative decoding, tensor parallelism, and a wide range of quantization formats (INT4, INT8, FP8). Its OpenAI-compatible API makes it a drop-in replacement for OpenAI API calls. Deployment is straightforward with Docker and Kubernetes support.
vLLM's primary limitation is NVIDIA-only support (AMD ROCm support is experimental). For maximum throughput on NVIDIA hardware, TensorRT-LLM outperforms vLLM by 20-40% through custom CUDA kernels and more aggressive optimization.
vLLM quick start
TensorRT-LLM
TensorRT-LLM is NVIDIA's production LLM serving framework, built on TensorRT and optimized for NVIDIA hardware. It provides the highest throughput of any serving framework through custom CUDA kernels, in-flight batching, and hardware-specific optimizations.
The tradeoff is complexity. TensorRT-LLM requires model compilation (30-60 minutes per model version), NVIDIA-specific tooling, and deeper expertise to operate. Model updates require recompilation, which slows deployment velocity.
TensorRT-LLM is the right choice for high-volume production deployments where maximum throughput justifies the operational overhead. For organizations with dedicated MLOps teams and stable model portfolios, the 20-40% throughput advantage translates directly to cost savings.
TensorRT-LLM compilation overhead
Triton Inference Server
NVIDIA Triton Inference Server is a general-purpose model serving platform that supports multiple model types (TensorFlow, PyTorch, ONNX, TensorRT, custom backends) and multiple hardware backends (GPU, CPU, FPGA). It is the right choice for organizations serving diverse model types beyond LLMs.
Triton's LLM performance is good but not best-in-class — it typically uses TensorRT-LLM as a backend for LLM serving. Its strength is the unified serving infrastructure for heterogeneous model portfolios, with built-in model versioning, A/B testing, and ensemble pipelines.
Triton is widely used in enterprise environments where standardization on a single serving platform simplifies operations. Its Kubernetes operator and Helm charts make deployment straightforward in cloud-native environments.
Platform comparison
Serving platform comparison
| Platform | LLM Optimized | Throughput | Latency | Multi-Model | K8s Native | Enterprise Support | License |
|---|---|---|---|---|---|---|---|
| vLLM | Yes | Excellent | Good | Limited | Yes | Community | Apache 2.0 |
| TensorRT-LLM | Yes | Best | Best | Limited | Via Triton | NVIDIA | Apache 2.0 |
| TGI | Yes | Good | Good | No | Yes | HuggingFace | Apache 2.0 |
| Triton | Via backend | Good | Good | Yes | Yes | NVIDIA | BSD |
| BentoML | Yes | Good | Good | Yes | Yes | Commercial | Apache 2.0 |
| Ray Serve | Via vLLM | Good | Good | Yes | Yes | Anyscale | Apache 2.0 |
Serving stack architecture
Production Model Serving Stack
Monitoring
Prometheus, Grafana, alerting
Load Balancer
Nginx, Envoy, or cloud LB
API Layer
OpenAI-compatible REST API, gRPC
GPU Runtime
CUDA, cuDNN, NCCL for multi-GPU
Serving Framework
vLLM / TensorRT-LLM / Triton
Model Weights
HuggingFace Hub, S3, NFS — model storage
Kubernetes deployment
All major serving platforms support Kubernetes deployment. Key configuration elements include GPU resource requests, node affinity for GPU nodes, persistent volumes for model weights, and horizontal pod autoscaling.
GPU resource configuration
Horizontal Pod Autoscaling (HPA) for inference should use custom metrics — GPU utilization or request queue depth — rather than CPU/memory. The KEDA (Kubernetes Event-Driven Autoscaling) operator supports custom metrics from Prometheus, enabling GPU-utilization-based scaling.
Model weight storage
Platform ROI calculator
Serving Platform Throughput Value
Estimate the value of throughput improvements from platform selection.
Estimated results
Current cost per 1M tokens
Optimized cost per 1M tokens
Throughput gain
Monthly tokens (optimized)
Monthly GPU cost
Cost reduction
Selection guide
Starting out / most teams
vLLMEasy deployment, OpenAI-compatible API, excellent performance, large community. Start here unless you have specific requirements that demand another platform.
Maximum throughput on NVIDIA hardware
TensorRT-LLM20-40% higher throughput than vLLM. Worth the operational complexity for high-volume deployments where the throughput advantage translates to significant cost savings.
Diverse model portfolio (not just LLMs)
Triton Inference ServerUnified serving for TensorFlow, PyTorch, ONNX, and LLM models. Best for organizations with heterogeneous model portfolios.
Rapid deployment / smaller teams
BentoMLHigher-level abstraction reduces deployment complexity. Accepts some performance overhead in exchange for faster time-to-production.
Frequently asked questions
What is the best LLM serving framework?
vLLM is the recommended starting point for most organizations. It provides excellent throughput, easy deployment, OpenAI-compatible API, and broad model support. TensorRT-LLM offers 20-40% higher throughput for NVIDIA hardware but requires more operational expertise. Choose TensorRT-LLM when you have dedicated MLOps resources and need maximum efficiency at scale.
How does vLLM compare to TensorRT-LLM?
vLLM is easier to deploy and operate, supports more model architectures, and has a larger community. TensorRT-LLM achieves 20-40% higher throughput through custom CUDA kernels and hardware-specific optimizations. vLLM is the better choice for most teams; TensorRT-LLM is worth the complexity for high-volume deployments where the throughput advantage justifies the operational overhead.
What is Triton Inference Server?
NVIDIA Triton Inference Server is a general-purpose model serving platform supporting multiple model types (TensorFlow, PyTorch, ONNX, TensorRT) and hardware backends. It is ideal for organizations serving diverse model portfolios beyond LLMs. For LLM-specific serving, Triton typically uses TensorRT-LLM as a backend.
How do you deploy a serving platform on Kubernetes?
vLLM and Triton both provide official Helm charts and Kubernetes operators. Key considerations: GPU resource requests/limits (nvidia.com/gpu), node affinity for GPU nodes, persistent volume claims for model weights, horizontal pod autoscaling based on GPU utilization or queue depth, and service mesh integration for traffic management.