vLLM vs LMDeploy vs Triton: A Technical Comparison of LLM Inference Backends in 2026

The rapid evolution of large language models (LLMs) has placed unprecedented demands on inference infrastructure. As models grow from 7B to 405B parameters, the choice of inference backend can mean the difference between a responsive API and a latency nightmare. In a recent in-depth analysis published on Habr, developers from Selectel compared three leading backends: vLLM, LMDeploy, and NVIDIA Triton Inference Server. This article distills that technical report into a structured, data-driven comparison, examining architecture, throughput, memory efficiency, and deployment complexity.

The Problem: Scaling LLM Inference Under Real-World Constraints

The original article, titled "vLLM vs LMDeploy vs Triton: обзор бэкендов для инференса LLM" (available here), addresses a core challenge: how to serve LLMs efficiently when hardware resources are finite. The authors tested each backend with popular open-weight models including Llama 3.1 70B and Qwen 2.5 32B on NVIDIA A100 GPUs (80 GB). Key metrics included tokens per second (throughput), time-to-first-token (TTFT), memory usage, and stability under concurrent requests.

Backend Architectures: How They Work

vLLM

vLLM is an open-source inference engine developed at UC Berkeley. Its standout feature is PagedAttention, which manages KV-cache memory in fixed-size blocks rather than contiguous chunks. This eliminates fragmentation and allows near-optimal memory utilization. The authors report that vLLM achieved up to 95% GPU memory utilization during their tests.

• Strengths: SOTA continuous batching, automatic prefix caching, support for speculative decoding.
• Limitations: Limited native support for non-transformer architectures; requires Python runtime for custom operations.

LMDeploy

LMDeploy, developed by Shanghai AI Laboratory, focuses on production deployment with integrated quantization (W4A16, W8A16). It uses TurboMind, a custom high-performance inference engine written in C++ and CUDA. The article notes that LMDeploy’s persistent batch mechanism reduces overhead by reusing GPU kernel launches across requests.

• Strengths: Built-in quantization (AWQ, GPTQ), low-latency for small batches, efficient memory pooling.
• Limitations: Smaller community than vLLM; documentation can lag behind releases.

NVIDIA Triton Inference Server

Triton is a mature, multi-framework inference server supporting TensorRT, TensorFlow, PyTorch, ONNX, and custom backends. The authors tested Triton with the TensorRT-LLM backend, which compiles model graphs into optimized engines.

• Strengths: Enterprise-grade features (model ensembles, dynamic batching, concurrent model execution), GPU scheduling, and robust monitoring.
• Limitations: Higher setup complexity; performance heavily depends on TensorRT engine tuning.

Benchmark Results: Head-to-Head Comparison

The article provides detailed benchmarks. Below is a summary of key findings for Llama 3.1 70B with 4× A100 GPUs using tensor parallelism:

Interpretation: Triton + TensorRT-LLM leads in raw throughput and concurrency, but requires extensive engine compilation (often hours for large models). vLLM offers the best memory efficiency and fastest deployment. LMDeploy strikes a balance with good latency and built-in quantization.

Real-World Use Cases and Case Studies

Case 1: High-Throughput Chat Service

A team at a mid-sized AI startup deployed a Llama 3.1 70B-based chatbot using vLLM. Their requirement was to handle 50+ concurrent users with sub-300 ms TTFT. According to the article, vLLM’s PagedAttention allowed them to serve requests with only 4× A100 GPUs, whereas a naive implementation would have required 8×. The team reported a 40% reduction in hardware costs compared to their previous PyTorch-based solution.

Case 2: Enterprise Multi-Model Pipeline

An enterprise customer used Triton Inference Server to deploy a pipeline combining a small embedding model (BGE-M3) and a large generation model (Qwen 2.5 72B). Triton’s ensemble scheduler and concurrent model execution enabled them to share GPU memory between models, reducing total GPU count from 6 to 4. The trade-off was a two-day setup period for TensorRT engine optimization.

Case 3: Quantized Deployment on a Budget

A research lab with limited GPU resources (2× A100) used LMDeploy to serve a quantized Llama 3.1 70B with W4A16 (4-bit weights, 16-bit activations). The backend’s persistent batch mechanism kept latency under 350 ms even under load. The article notes that LMDeploy’s quantization pipeline was easier to integrate than Triton’s, requiring only a single command-line conversion.

Technical Deep Dive: Memory Management Strategies

Memory management is the critical bottleneck for LLM inference. The article explains three approaches:

• vLLM’s PagedAttention: Inspired by OS virtual memory, it divides KV-cache into pages. This allows fine-grained sharing of cache blocks across requests, reducing memory waste by up to 60% in high-concurrency scenarios.
• LMDeploy’s Block Manager: Uses a custom memory pool that pre-allocates contiguous blocks for KV-cache. It trades some flexibility for lower allocation overhead.
• Triton’s TensorRT-LLM: Compiles the entire model graph, including KV-cache management, into a static engine. This yields deterministic performance but requires recompilation for model or hardware changes.

Deployment Complexity and Ecosystem

The authors note that vLLM is the easiest to get started with, while Triton is best suited for teams with dedicated MLOps engineers. LMDeploy fits well for teams that need quantization without the overhead of TensorRT.

Compatibility with Industry Tools

All three backends support OpenAI-compatible APIs, making them easy to integrate with existing applications. For example, ASI Biont supports connecting to these inference backends via API for custom LLM workflows — more details at asibiont.com/courses.

Conclusion and Recommendations

The article from Selectel concludes that no single backend is universally superior. The choice depends on specific requirements:

• Choose vLLM if you prioritize rapid prototyping, memory efficiency, and ease of use. It is ideal for startups and research teams.
• Choose LMDeploy if you need built-in quantization and low-latency responses for moderate concurrency. It is a strong middle-ground option.
• Choose Triton + TensorRT-LLM if you operate at enterprise scale, require maximum throughput, and have the engineering resources for setup and tuning.

As of mid-2026, all three backends are actively maintained and have their own dedicated communities. The most successful deployments often combine them: for example, using vLLM for development and Triton for production, or LMDeploy for quantized edge models. The key is to benchmark with your specific model and workload before committing.

For the full technical analysis, including detailed latency distributions and memory breakdowns, refer to the original Habr article here.