Best Open-Source LLM Inference Servers 2026

Picking a LLM inference server used to be easy. You had vLLM, you had TGI, and you picked based on whether you wanted throughput or HuggingFace integration. That decision tree is now a lot more complicated. SGLang has closed the gap - and in many workloads surpassed - vLLM on raw throughput. TGI quietly entered maintenance mode in December 2025. TensorRT-LLM keeps posting the fastest absolute numbers if you can tolerate a 28-minute compilation step per model. LMDeploy, Aphrodite, and MLC-LLM have carved out real niches. And Ray Serve has grown into a full orchestration layer that sits above all of them.

This comparison covers eleven frameworks, pulls from H100 benchmarks published in early 2026, and tries to give you an honest decision framework rather than another feature-checkbox table.

The Benchmark Baseline

All throughput numbers below come from public H100 SXM5 80GB benchmarks running Meta-Llama 3.3-70B-Instruct in FP8 precision unless noted otherwise. For smaller models (Llama 3.1 8B on H100), the SGLang/LMDeploy lead is wider (roughly 16,200 tok/s vs 12,500 tok/s for vLLM).

Hardware: NVIDIA H100 SXM5 80GB (single GPU) for TRT/SGLang/vLLM rows. LMDeploy row: A100 80GB, Llama 3 70B Int4, 100 concurrent users. Ollama row: RTX 4090. Sources: Spheron benchmarks, Prem AI comparison.

Modern GPU clusters run multiple inference servers simultaneously, making the choice of serving framework a critical infrastructure decision. Source: unsplash.com

vLLM - The Reliable Default

GitHub: vllm-project/vllm | License: Apache 2.0 | Latest: v0.19.0 (Apr 3, 2026)

vLLM started at UC Berkeley and has built up 77k GitHub stars and 2,000+ contributors. It remains the reference implementation for PagedAttention - a KV cache management technique that treats GPU memory like virtual memory, partitioning the cache into fixed-size blocks that can live in non-contiguous memory. The result is less than 4% memory waste under typical workloads, compared to 60-80% fragmentation in naive implementations.

Strengths

• Model breadth: 200+ architectures natively supported from HuggingFace, including decoder-only LLMs, MoE models (DeepSeek-V3, Mixtral), multimodal models (LLaVA, Qwen-VL), and embedding models
• Quantization coverage: FP8, INT4, INT8, GPTQ, AWQ, GGUF, and compressed_tensors - essentially everything
• Hardware portability: NVIDIA GPUs, AMD GPUs, CPUs, TPUs, and specialized accelerators
• Speculative decoding: n-gram, suffix, EAGLE, and DFlash variants supported
• API compatibility: OpenAI-compatible server plus Anthropic Messages API

Weaknesses

• Cold start around 62 seconds - not ideal for auto-scaling fleets
• TTFT p99 is 80% larger than competitors under load in some benchmarks - variance is higher than SGLang
• Slightly behind SGLang and TRT-LLM on raw throughput for larger models

Best for

General-purpose production serving where you need to run multiple model architectures and don't want to maintain separate serving stacks. The ecosystem around vLLM (observability tools, integrations, community support) is unmatched.

SGLang - The Throughput Leader

GitHub: sgl-project/sglang | License: Apache 2.0 | Latest: v0.5.10.post1 (Apr 9, 2026)

SGLang is the inference engine that crept up on vLLM's market share by solving a specific problem better: prefix reuse. RadixAttention uses a radix tree data structure to automatically identify and cache shared KV blocks across requests. When multiple users start with the same system prompt, the same document in a RAG pipeline, or the same few-shot examples, those tokens are computed once and reused.

The cache hit rates are dramatic: 85-95% for few-shot workloads vs 15-25% for vLLM's prefix caching, and 75-90% for multi-turn chat vs 10-20% for vLLM. That compounding reuse is why SGLang delivers 3.1x faster inference than vLLM on DeepSeek V3 specifically.

Strengths

• Structured output: xGrammar backend delivers up to 10x faster JSON decoding than alternatives, with compiled grammars cached in the radix tree for reuse
• Scale: Powers 400,000+ GPUs globally, trillions of tokens daily. Production users include xAI, AMD, NVIDIA, LinkedIn, and Cursor
• Multi-GPU: Tensor, pipeline, expert, and data parallelism
• Quantization: FP4/FP8/INT4/AWQ/GPTQ across NVIDIA GB200/B300/H100 and AMD MI-series
• Prefill-decode disaggregation: Separates the prompt processing phase from token generation for better resource use

Weaknesses

• Smaller community than vLLM (26k stars vs 77k)
• Slightly steeper operational learning curve
• Fewer supported model architectures than vLLM

Best for

Any workload with shared prefixes: RAG pipelines, document Q&A, multi-turn chat agents, structured JSON extraction. For anything involving AI agent frameworks, SGLang's prefix caching compounds into a significant cost advantage.

TensorRT-LLM - Maximum Performance, Maximum Friction

GitHub: NVIDIA/TensorRT-LLM | License: Apache 2.0 | Latest: v1.2.0

NVIDIA TensorRT-LLM is the performance ceiling for LLM serving on NVIDIA hardware. The numbers are real: 13% higher throughput than vLLM at 50 concurrent requests, best TTFT across every concurrency level. On H100 and later GPUs, FP8 quantization doubles performance versus FP16. On B200 GPUs, FP4 inference is natively supported.

The cost is compilation. Every new model requires building a TensorRT engine, a process that takes around 28 minutes. After that first compile, subsequent warm starts drop to ~90 seconds. But any model update, weight change, or configuration tweak triggers a full recompile.

Strengths

• Highest throughput and lowest TTFT in class at high concurrency
• In-flight batching maximizes GPU use
• FlashAttention and paged KV caching built in
• Best-in-class on NVIDIA hardware including GB200 (FP4 native)
• Usually rolled out through the Triton Inference Server backend

Weaknesses

• 28-minute engine compilation per model - painful for experimentation
• NVIDIA-only (no AMD, no CPU, no TPU fallback)
• The "open source" label is technically accurate (Apache 2.0) but NVIDIA controls development and the custom kernels make portability a non-starter

Best for

High-volume production deployments where you're running a fixed model at scale and can amortize the compilation overhead. Data centers serving millions of requests per day on H100/B200 fleets. Not recommended if you need to swap models frequently or experiment with architectures.

HuggingFace TGI - Sunsetted

GitHub: huggingface/text-generation-inference | Status: Maintenance mode since Dec 11, 2025

TGI pioneered the OpenAI-compatible API wrapper for HuggingFace models and was the default choice for most teams from 2023 through 2025. On December 11, 2025, HuggingFace's Lysandre Debut announced that TGI had entered maintenance mode - only minor bug fixes and documentation updates will be accepted. New features are done.

Under production load, TGI achieved 68-74% GPU use compared to vLLM's 85-92%, and saturated at 50-75 concurrent requests vs vLLM's 100-150. For existing deployments, TGI will continue working. For anything new, migrate.

Migration path: Drop-in replacement is vLLM or SGLang - both serve the same OpenAI-compatible /v1/chat/completions endpoint.

llama.cpp and Ollama - Development Tools

llama.cpp GitHub: ggml-org/llama.cpp | License: MIT

llama.cpp runs LLM inference in pure C++ with no GPU required. It supports GGUF quantization from 1.5-bit through 8-bit integer, k-quants (Q4_K_M, Q5_K_S, etc.), and KV cache quantization that cuts memory usage by up to 50% during generation. The built-in llama-server exposes an OpenAI-compatible API with Prometheus metrics, and since late 2025 also supports the Anthropic Messages API.

Ollama wraps llama.cpp in a user-friendly CLI and REST interface, adding model management and a clean API. It's excellent for local development, testing prompt changes, and internal tooling.

The production ceiling is real: at 50 concurrent users, Ollama plateaus at roughly 155 tok/s while vLLM maintains 920 tok/s. Response times degrade from 2 seconds to 45+ seconds with just 10 concurrent users. This isn't a criticism - these tools are optimized for a different use case. See our separate local LLM tools comparison for the desktop-first perspective.

Best for

CPU-only inference, air-gapped environments, developer workstations, model prototyping before launching to a GPU cluster.

LMDeploy - The Hidden Strong Contender

GitHub: InternLM/lmdeploy | License: Apache 2.0

LMDeploy from the InternLM team runs the TurboMind inference engine (C++) and matches SGLang's throughput on Llama 3.1 8B (~16,100 tok/s on H100). Its standout metric is latency on quantized models: for Int4 inference on A100 80GB running Llama 3 70B at 100 concurrent users, LMDeploy delivers 700 tok/s with the lowest time-to-first-token across all tested engines. The Int4 performance is 2.4x faster than FP16.

LMDeploy supports persistent batch inference, blocked KV cache, dynamic split-and-fuse, and tensor parallelism. The caveat is narrower model coverage than vLLM and a smaller Western community - most documentation and issues are in Chinese.

Best for

Teams running InternLM models, or anyone prioritizing Int4 latency on A100/A800 hardware.

MLC-LLM - Cross-Platform Compilation

GitHub: mlc-ai/mlc-llm | License: Apache 2.0

MLC-LLM compiles models via Apache TVM to run on CUDA, Vulkan, Metal, and WebGPU. The same compiled artifact runs on Linux, macOS, Windows, iOS, Android, and in-browser via WebGPU. This is the only framework in this list that can serve a model natively on a mobile device or in a browser.

For server-side production throughput, MLC-LLM is not competitive with vLLM or SGLang. Its value is in edge and cross-platform deployment where the runtime can't be NVIDIA-only. It exposes an OpenAI-compatible REST API and supports FP8/INT4/AWQ/GPTQ quantization.

Best for

Edge inference, mobile deployment, browser-based private AI applications, cross-platform CI/CD pipelines.

Aphrodite Engine - Extended Quantization for Creative AI

GitHub: aphrodite-engine/aphrodite-engine | License: AGPL-3.0

Aphrodite is a vLLM fork maintained by the PygmalionAI team, tuned for creative writing and roleplay use cases. Its unique value is quantization breadth: AQLM, AutoRound, AWQ, BitNet, Bitsandbytes, EETQ, GGUF, GPTQ, QuIP#, SqueezeLLM, Marlin, FP2 through FP12, NVIDIA ModelOpt, TorchAO, VPTQ, and MXFP4. It also adds a KoboldAI-compatible API layer and Mirostat sampling.

For general production serving, stick with upstream vLLM - Aphrodite's advantages are niche. But if you need FP2 quantization or KoboldAI API compatibility, it's the only serious option.

DeepSpeed-MII - Microsoft's Inference Layer

GitHub: deepspeedai/DeepSpeed-MII | License: Apache 2.0

DeepSpeed-MII provides inference optimizations from Microsoft Research: blocked KV caching, continuous batching, Dynamic SplitFuse, tensor parallelism, and INT8/ZeroQuant quantization. It supports 37,000+ models and claims up to 2.5x higher throughput than vLLM in its own benchmarks.

The honest assessment: MII's benchmarks predate vLLM's v0.6+ performance improvements, and independent 2026 comparisons show it behind SGLang and current vLLM. It remains relevant for teams already on the Azure/DeepSpeed stack. For greenfield deployments, vLLM or SGLang is the better default.

Triton Inference Server - The Enterprise Orchestration Layer

GitHub: triton-inference-server/server | License: BSD 3-Clause

NVIDIA Triton is general-purpose multi-framework model serving, not a LLM inference engine itself. Its value in this comparison is as the production deployment layer on top of TensorRT-LLM. The TensorRT-LLM backend for Triton adds in-flight batching, paged attention, LoRA support, and leader/orchestrator multi-GPU modes.

Triton handles the enterprise concerns: multi-model serving, health checks, performance metrics, ensemble pipelines, and gRPC/HTTP APIs. If your production stack already uses Triton for computer vision or NLP models, adding LLM serving via the TRT-LLM backend is the natural path.

Multi-GPU inference deployments rely on high-bandwidth networking between nodes - the infrastructure layer matters as much as the serving framework. Source: unsplash.com

Ray Serve - Orchestration, Not a Server

Docs: docs.ray.io/en/latest/serve/llm/index.html | License: Apache 2.0

Ray Serve isn't an inference engine - it's a programmable orchestration layer that wraps engines like vLLM. The ray-llm repository was archived for the built-in ray.serve.llm API (available since Ray 2.44), which handles auto-scaling, load balancing, streaming responses, and multi-model routing.

Ray Serve's superpower is disaggregated serving: prefill and decode phases can be separated across different actor pools, tuning GPU use. Teams serving multiple models or needing fine-grained traffic control will find it valuable. Teams serving a single model with predictable load should just run vLLM directly.

Full Comparison Table

TP = Tensor Parallelism, PP = Pipeline Parallelism, EP = Expert Parallelism

Use-Case Decision Matrix

You want the safest default for production serving - start with vLLM. Broadest model support, active community, Apache 2.0, and solid documentation. Pick SGLang if you confirm prefix-heavy workloads.

Your workload involves RAG or multi-turn agents - switch to SGLang. The RadixAttention prefix caching compounds across requests in ways that vLLM's implementation doesn't match. For RAG tool integrations, this is often a 20-30% cost reduction in practice.

You're running a fixed high-traffic model on H100/B200 - TensorRT-LLM delivers the best throughput numbers. Accept the 28-minute compilation as a one-time build cost per model version.

You need CPU inference, Apple Silicon, or air-gapped deployments - llama.cpp is the right engine. Ollama adds a friendlier interface. Neither scales to high concurrent load.

You're focused on quantized inference budget - LMDeploy for Int4 on A100/A800, especially for InternLM models or any workload where you want lowest TTFT at low precision.

You need edge, mobile, or browser deployment - MLC-LLM is the only viable open-source option.

You're on the Azure/DeepSpeed ecosystem - DeepSpeed-MII integrates naturally and offers solid performance within that stack.

You need structured JSON outputs at scale - SGLang with xGrammar. The compiled grammar caching is unique in the space.

You're running many models or need routing/autoscaling - Ray Serve as the orchestration layer, wrapping vLLM or SGLang underneath. Check the AI speed and latency leaderboard for how these stack up under real routing conditions.

You want to benchmark your own hardware - LLMFit is a useful starting point for calibrating expected throughput against your available GPU fleet.

FAQ

Is vLLM still the best inference server in 2026?

vLLM is the most flexible default, but SGLang and TensorRT-LLM beat it on raw throughput. The right choice depends on workload: vLLM for broad model support, SGLang for prefix-heavy or structured output workloads, TRT-LLM for maximum single-model throughput.

Should I migrate away from TGI?

Yes, for anything new. TGI entered maintenance mode on December 11, 2025, and no new features will be added. Both vLLM and SGLang are drop-in API replacements serving the same OpenAI-compatible endpoints.

Does SGLang support all the same models as vLLM?

Not yet. vLLM supports 200+ HuggingFace architectures; SGLang covers fewer. For the most recently released or niche models, vLLM is more likely to have support. Check the SGLang docs for the current model list before committing.

When does TensorRT-LLM make sense?

When you are serving a fixed model at sustained high concurrency on NVIDIA H100 or B200 hardware, and the 28-minute compilation cost is amortized over millions of requests. Not suitable for experimentation or multi-model serving.

Is Ollama suitable for production?

At low concurrency (under 4-8 simultaneous users), yes. Under higher load, response times degrade sharply and throughput plateaus. For customer-facing production serving, use vLLM or SGLang.

Sources

• vLLM GitHub (v0.19.0)
• SGLang GitHub (v0.5.10)
• NVIDIA TensorRT-LLM GitHub
• HuggingFace TGI GitHub
• llama.cpp GitHub
• InternLM LMDeploy GitHub
• MLC-LLM GitHub
• Aphrodite Engine GitHub
• DeepSpeed-MII GitHub
• Triton Inference Server GitHub
• Ray Serve LLM Docs
• Spheron vLLM vs TRT-LLM vs SGLang H100 Benchmarks
• Prem AI: vLLM vs SGLang vs LMDeploy 2026
• Prem AI: LLM Inference Servers Compared 2026
• Lysandre Debut - TGI Maintenance Mode Announcement
• vLLM Automatic Prefix Caching Docs