Best vLLM Alternatives in 2026: 8 Tools Compared

#1

SGLang

Best for: Production serving for agents

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SGLang is the leading production alternative to vLLM in 2026. Its RadixAttention cache reuses shared prompt prefixes, which pays off for agents and retrieval workflows where many requests share a system prompt. It supports structured output, multi-LoRA serving, and Prometheus metrics, and it runs on NVIDIA, AMD, TPU, and Ascend hardware. Guides report deployment on hundreds of thousands of GPUs worldwide.

The cost is complexity. SGLang setup and tuning take more effort than a local tool, and teams should benchmark it on their own prompts before switching, since the caching advantage shrinks for fully random queries. For production APIs, agentic workloads, and structured output needs, SGLang is the first alternative to test.

#2

TensorRT-LLM

Best for: Maximum NVIDIA throughput

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TensorRT-LLM is NVIDIA's own inference engine, built for maximum throughput on NVIDIA datacenter GPUs. It supports advanced quantization formats such as FP8 and NVFP4, plus cache reuse, and it serves through an OpenAI-compatible endpoint. Teams running latency-sensitive, high-volume production on NVIDIA hardware get the best raw numbers here.

The lock-in is real. TensorRT-LLM runs on NVIDIA only, and tuning takes expert effort. Model support is narrower than vLLM, and new model releases may need a wait for optimized kernels. It fits teams that already standardize on NVIDIA and care more about peak throughput than hardware flexibility.

#3

Text Generation Inference

Best for: Existing Hugging Face deployments

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Text Generation Inference, known as TGI, was Hugging Face's production serving engine and remains easy to set up with an OpenAI-compatible API. It supports quantization formats such as GPTQ and AWQ, and it links deeply with the Hugging Face model hub. Teams already running TGI can keep it going with bug fixes.

The key fact is its status. TGI entered maintenance mode in December 2025, with only bug fixes accepted, and Hugging Face docs point new users toward vLLM, SGLang, llama.cpp, and MLX. New projects should not pick TGI. It stays on this list only for teams that already run it and need a migration plan, not for fresh deployments.

#4

Ollama

Best for: Simple local model use

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Ollama is the simplest way to run models locally. One command pulls a model and starts chatting, and it runs on NVIDIA, AMD, Apple Metal, and CPU hardware. Its model library covers popular open models, and it suits solo developers, prototyping, and private desktop workflows where data must stay on the machine.

Ollama is not a production server. Multi-user serving is limited, peak throughput trails vLLM and SGLang, and production controls such as advanced batching and metrics are thinner. Teams that outgrow Ollama usually move to SGLang or vLLM for serving, while keeping Ollama for development. For local use, nothing here is easier.

#5

llama.cpp

Best for: CPU and edge hardware

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llama.cpp is the portability champion. Written in C++, it runs on NVIDIA, AMD, Metal, Vulkan, and plain CPUs, with GGUF quantization that squeezes large models onto modest hardware. Its server mode offers an OpenAI-compatible API with JSON schema output and grammar controls, plus opt-in Prometheus metrics.

The server mode is basic compared with SGLang or vLLM, and multi-user production needs extra work. Setup also assumes command-line comfort. llama.cpp fits enthusiasts, edge devices, CPU-only servers, and developers building desktop apps on top of local models. When hardware is unusual or weak, it is often the only engine that works well.

#6

LMDeploy

Best for: Long context on NVIDIA GPUs

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LMDeploy, from the InternLM team, targets fast serving on NVIDIA GPUs with low first-token latency and long context windows. It supports vision-language models and quantization formats such as AWQ and W4A16, and it offers an OpenAI-compatible API with Prometheus metrics. Teams serving long documents or multimodal models should test it.

Hardware focus is the limit. LMDeploy centers on NVIDIA, with Huawei Ascend support, and its community is smaller than vLLM or SGLang. Some features, such as multi-LoRA serving, work only on its PyTorch engine. For NVIDIA teams with long-context needs, LMDeploy is a strong second engine to benchmark.

#7

MLC LLM

Best for: Mobile, browser, and edge deployment

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MLC LLM compiles models for on-device use across phones, browsers, smart TVs, and laptops, using Metal, Vulkan, WebGPU, and CUDA backends. That makes it the pick for apps that must run inference without a server, keeping user data on the device. Quantization options such as Q4 and Q8 help models fit mobile memory.

Setup is harder than Ollama, and the range of well-tested mobile models is narrower than server engines. Performance varies widely by device, so teams must test on target phones. MLC LLM fits product teams shipping AI features inside mobile or web apps, where server costs or privacy rules block a cloud API.

#8

Triton Inference Server

Best for: Multi-model serving fleets

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Triton Inference Server, from NVIDIA, serves many model types, not only LLMs, inside one fleet. It supports multiple frameworks, dynamic batching, and model management tools that suit enterprise teams running dozens of models. Combined with TensorRT-LLM as a backend, it can serve LLMs at scale inside a wider inference platform.

For a single LLM, Triton is overkill. Configuration is hard, and its LLM-specific features trail SGLang and vLLM. Teams pick Triton when they already serve vision, speech, and recommendation models alongside LLMs and want one serving layer. Teams serving only LLMs should pick SGLang or vLLM instead.