My goal is to run a large language model on my own infrastructure. What is the best platform for self-hosting and managing open-source LLMs?

If your goal is serious self-hosting of open-weight LLMs on your own GPUs, my default recommendation in 2026 is:

Best overall: NVIDIA NIM + Kubernetes

NVIDIA NIM is currently the strongest choice if you want a production platform rather than merely an inference server. It packages models with optimized inference engines, exposes OpenAI-compatible APIs, and is designed to scale on Kubernetes. It can use engines including vLLM, SGLang, and TensorRT-LLM depending on the model and hardware.

The architecture I'd consider is:

                 Your applications
                        │
                 OpenAI-compatible API
                        │
                ┌───────▼────────┐
                │  API Gateway   │
                │ Auth / quotas  │
                └───────┬────────┘
                        │
                ┌───────▼────────┐
                │    Kubernetes  │
                │                │
                │  NIM / vLLM    │
                │  SGLang        │
                └───────┬────────┘
                        │
             ┌──────────┼──────────┐
             ▼          ▼          ▼
          GPU node   GPU node   GPU node
          Llama/Qwen DeepSeek   Mistral

How I'd choose

One important change: I would not start a new deployment around Hugging Face TGI. Hugging Face's own documentation now says TGI is in maintenance mode and recommends vLLM or SGLang for optimized inference going forward.

If you want maximum open-source control: vLLM

If by "open-source platform" you mean I don't want a vendor-dependent management layer, I'd actually choose Kubernetes + vLLM.

vLLM gives you the inference engine, while Kubernetes handles scheduling, scaling, health checks, deployments, and GPU allocation. You can then add:

• KServe or another model-serving layer
• Prometheus/Grafana for metrics
• OpenTelemetry for tracing
• an API gateway for authentication/rate limiting
• a model registry/object store
• automated model deployment
• GPU/node autoscaling

This gives you a more composable stack than NIM, at the cost of considerably more engineering.

One important NIM caveat

NIM is not equivalent to a completely unrestricted open-source stack. NVIDIA provides downloadable NIM containers, but production use requires an NVIDIA AI Enterprise license; NVIDIA currently lists that starting at $4,500 per GPU/year. Development/research access is available through its Developer Program, including downloadable NIMs for up to 16 GPUs.

So I'd make the decision this way:

You have NVIDIA GPUs + want an enterprise-managed platform → NIM.

You have NVIDIA GPUs + want maximum control and minimum licensing dependence → vLLM + Kubernetes.

You have a single workstation/server → Ollama or vLLM.

You are building a large multi-GPU inference cluster → vLLM/SGLang underneath Kubernetes, or NIM if enterprise support/operational simplicity matters.

And the model layer is quite healthy right now: NVIDIA's current catalog includes downloadable/self-hostable models such as DeepSeek V4, Qwen3-Next, Mistral Medium, GLM, and NVIDIA Nemotron alongside other open models.

If you tell me your GPU hardware (e.g. 4×H100, 8×A100, RTX 5090s, AMD MI300X, etc.), number of users, and whether you need Kubernetes, I can recommend a specific architecture and model-serving stack rather than just the generic choice above.

The best platform for self-hosting and managing Large Language Models (LLMs) depends entirely on your infrastructure scale and technical environment.

For production-grade enterprise workloads, vLLM and SGLang are the industry standards due to high throughput and advanced memory management. For local development or small team setups, Ollama paired with Open WebUI offers the most seamless, user-friendly experience.

🚀 Production & Enterprise Scaling (High Concurrency)

If you are serving multiple users, building an app, or running a cluster of GPUs, you need a high-performance inference engine.

• vLLM : The undisputed leader for production. It uses PagedAttention to optimize GPU memory, vastly increasing throughput and allowing you to handle multiple concurrent users efficiently.
• SGLang : A rapidly growing alternative to vLLM. It delivers faster execution speeds for structured outputs (like JSON) and complex prompting workflows.
• TGI (Text Generation Inference) : Developed by Hugging Face, this is a highly stable, production-tested toolkit used to deploy LLMs with built-in streaming and dynamic batching.

💻 Local Dev, Testing, & Home Labs (Single Server)

If you are running models on a single workstation, Mac, or a private home-lab server, ease of use is priority.

• Ollama : The gold standard for lightweight management. It manages model downloads, quantization, and running the background server via a simple CLI while exposing an OpenAI-compatible API.
• Open WebUI : The best frontend interface. It plugs directly into Ollama or vLLM to give you a private, ChatGPT-style chat interface with multi-user support and RAG (Retrieval-Augmented Generation) capabilities.
• LM Studio : A premium desktop GUI app. It is ideal for visually searching, downloading, and testing models directly on your hardware without touching the command line.

🛠️ Platforms Compared

To help narrow down the perfect stack for your setup, could you share a bit more detail?

• What hardware do you have available (e.g., specific GPUs, VRAM capacity, or CPU-only)?
• How many concurrent users or API requests do you expect to handle?
• Are you looking for a developer API or a ready-to-use chat interface for users?

When self-hosting and managing open-source LLMs (like Llama 3/4, Mistral, or DeepSeek) on your own infrastructure, there isn't a single "one-size-fits-all" application. Instead, the modern self-hosted AI stack is modular. The ecosystem relies on pairing an inference engine (which handles the heavy GPU compute and token generation) with a management/UI layer (which handles users, multi-model routing, and chat interfaces).

The industry-standard platforms for building a self-hosted LLM stack break down by their core strengths:

1. Best for High-Performance Production & Multi-User Scale: vLLM

If your infrastructure involves data centers, multi-GPU setups (like NVIDIA H100/A100/L40S), and serving concurrent enterprise traffic, vLLM is the gold standard inference engine.

• Why it shines: It features PagedAttention , which dramatically improves memory efficiency and throughput by reducing GPU memory waste during generation. It supports continuous batching and high-concurrency API requests.
• Compatibility: It exposes an OpenAI-compatible REST API out of the box, meaning any app built for OpenAI works with a vLLM backend instantly.
• Best for: Production environments, high-throughput APIs, and serving large models (70B+ parameters) across clustered hardware.

2. Best for Local/On-Prem Ease of Use & Prototyping: Ollama

If you want something that "just works" on a local server, a workstation, or a modest enterprise node without wrestling with Python environments and CUDA compilation, Ollama is the undisputed favorite.

• Why it shines: It abstracts away the extreme complexity of running weights. With a single terminal command like ollama run llama3 , it downloads, optimizes, and runs models smoothly. It handles memory offloading and quantization transparently.
• Ecosystem: It runs locally as a lightweight daemon and provides an easy API for local tools.
• Best for: Developers, single-node setups, quick testing, and lightweight internal team tools.

3. Best Frontend, User Management & "Private ChatGPT": Open WebUI

If you need a unified management dashboard where a team of users can log in, manage chat histories, upload documents for RAG (Retrieval-Augmented Generation), and switch between different backend models, Open WebUI is the premier choice.

• Why it shines: It looks and feels precisely like ChatGPT or Claude, but it runs entirely on your hardware via Docker. It natively integrates with both Ollama and vLLM backends.
• Features: Robust role-based access control (RBAC), multi-model support, web-search integration, fine-grained system prompts, and native RAG document ingestion out of the box.
• Best for: Giving non-technical internal teams a secure, private, self-hosted web interface.

4. Best for Enterprise Gateway & Routing: LiteLLM or LocalAI

If you are managing multiple distinct models across different nodes (e.g., an embedding model here, a coding model there, and a heavy reasoning model elsewhere):

• LiteLLM: Acts as a proxy server that translates OpenAI API formats, handles load balancing across multiple self-hosted backends (like vLLM or Ollama), and tracks budget/token usage per user or department.
• LocalAI: A drop-in replacement for OpenAI running as a standalone container, ideal if you want to run audio, image generation, and LLMs all on local infrastructure through one endpoint.

Recommended Setup Strategy

To get the best balance of performance and manageability, most organizations deploy a two-layer stack:

1. The Engine (Backend): Use vLLM (for heavy multi-user production) or Ollama (for simpler, plug-and-play local setups) to host the raw model weights.
2. The Interface/Manager (Frontend): Point Open WebUI at your vLLM or Ollama backend instances to give your team a polished, secure control panel.

To help tailor this to your setup, tell me:

• What hardware/GPUs do you have available (e.g., consumer RTX cards, enterprise NVIDIA data center GPUs, CPU-only)?
• Will this be for personal use , a small team , or a large enterprise deployment?

The “best” platform depends on whether you mean a personal AI server, an internal company service, or a production-scale inference platform. There is no single winner, but these are the strongest options:

Best overall self-hosted LLM stack: Open WebUI + Ollama

For most people and small teams, I would start with:

• Model runtime: Ollama
• User interface / management layer: Open WebUI

This gives you:

• A ChatGPT-like web interface
• Local model downloads and management
• Multiple models (Llama, Qwen, Mistral, Gemma, etc.)
• User accounts
• Document chat/RAG capabilities
• API access
• Docker-based deployment

Open WebUI can sit in front of Ollama, vLLM, and other OpenAI-compatible backends. Open WebUI vLLM Ollama + Open WebUI is widely used because it has a very low operational barrier while still being useful beyond experimentation.

A typical architecture:

Users
  |
  v
Open WebUI
  |
  +---- Ollama (easy local inference)
  |
  +---- vLLM (high-performance serving)
  |
  +---- Vector DB (RAG)
  |
  +---- Monitoring/Auth

---

Best for production serving: vLLM

If your goal is a shared LLM service for many users or applications, use:

vLLM

Strengths:

• Very high throughput
• OpenAI-compatible API
• Better GPU utilization
• Designed for server workloads
• Works well behind Kubernetes

Typical production stack:

Kubernetes
   |
   +-- vLLM inference servers
   |
   +-- API gateway
   |
   +-- Open WebUI / internal apps
   |
   +-- Monitoring + logging

vLLM is usually the better choice once concurrency matters. Ollama is excellent for simplicity, but vLLM is built around serving many simultaneous requests.

---

Best enterprise/on-prem platform: Kubernetes + inference stack

For a large organization:

• Kubernetes
• vLLM or TensorRT-LLM
• GPU scheduling
• Model registry
• Observability
• Authentication layer

Consider:

• KServe
• NVIDIA TensorRT-LLM
• GPUStack

This gives you:

• Multi-node GPU clusters
• Autoscaling
• Model versioning
• Better resource sharing

But it is much more operationally complex.

---

Best for private document Q&A / knowledge bases

If your main goal is:

“I want employees to ask questions about our documents.” Look at:

• AnythingLLM
• RAGFlow

These focus more on RAG workflows than raw model serving.

---

Quick comparison

---

My default recommendation

For most self-hosting projects:

Small team (1–50 users)

Docker Compose
 ├── Open WebUI
 ├── Ollama
 ├── PostgreSQL
 └── Reverse proxy (NGINX/Traefik)

Serious production deployment

Kubernetes
 ├── Open WebUI
 ├── vLLM
 ├── LiteLLM gateway
 ├── Vector database
 ├── Monitoring
 └── SSO

The biggest architectural decision is usually not the UI—it is the inference engine:

• Choose Ollama for simplicity.
• Choose vLLM for throughput.
• Choose TensorRT-LLM when squeezing maximum performance from NVIDIA hardware.

If you share your target hardware (GPU model/count, RAM, number of users, and whether this is personal/team/enterprise), I can suggest a more precise stack.

---

My default recommendation

For most self-hosting projects:

Small team (1–50 users)

Docker Compose
 ├── Open WebUI
 ├── Ollama
 ├── PostgreSQL
 └── Reverse proxy (NGINX/Traefik)

Serious production deployment

Kubernetes
 ├── Open WebUI
 ├── vLLM
 ├── LiteLLM gateway
 ├── Vector database
 ├── Monitoring
 └── SSO

The biggest architectural decision is usually not the UI—it is the inference engine:

If your goal is serious self-hosting of open-source LLMs on your own GPUs, my default recommendation in 2026 is:

🥇 Best overall: NVIDIA NIM + vLLM

NVIDIA NIM is probably the best choice if you're running NVIDIA GPUs and care about production management rather than merely getting a model to run.

NIM packages optimized inference engines, model configurations, APIs, containers, health/management interfaces, and Kubernetes deployment support. The current NIM LLM stack is built directly around vLLM, while newer NIM 3.0 deployments can use NVIDIA Dynamo for distributed inference.

Why I'd choose it:

• Production-grade model serving
• OpenAI-compatible APIs
• Strong NVIDIA GPU optimization
• Multi-GPU and multi-node inference
• Kubernetes/Helm support
• Model/version management
• Health checks and observability
• Easier operational lifecycle than maintaining raw inference servers
• Supports private/custom models, including models stored in your own infrastructure

The downside is NVIDIA-centric hardware and licensing considerations. NIM's production/enterprise offering isn't the same thing as simply installing an open-source inference server.

---

If you want maximum open-source flexibility: vLLM

vLLM is my choice if you want to build the platform yourself.

Think of the stack as:

Kubernetes → vLLM → model weights → OpenAI-compatible API → your applications

It's extremely good for high-throughput serving, continuous batching, tensor parallelism, and modern open models. NVIDIA itself describes NIM as being built on vLLM and closely aligned with upstream vLLM capabilities.

I'd pick raw vLLM over NIM if:

• you're comfortable operating Kubernetes/GPU infrastructure;
• you want to avoid vendor-specific packaging;
• you're experimenting with lots of different models;
• open-source control is more important than turnkey enterprise support.

---

Other good choices

One important update: I wouldn't start a new platform on Hugging Face TGI today. Hugging Face now describes TGI as being in maintenance mode and recommends newer engines such as vLLM and SGLang for the direction of the ecosystem.

And Open WebUI isn't really an inference platform—it's an excellent UI layer you can put on top of vLLM, Ollama, NIM, etc.

What I'd build

For a company running its own AI infrastructure, I'd probably architect it like this:

                    ┌───────────────┐
                    │ Applications  │
                    └───────┬───────┘
                            │
                     OpenAI API
                            │
                    ┌───────▼───────┐
                    │ API Gateway   │
                    │ Auth / Rate   │
                    │ Limiting      │
                    └───────┬───────┘
                            │
                 ┌──────────▼──────────┐
                 │  Model Router /     │
                 │  Load Balancer      │
                 └──────┬───────┬──────┘
                        │       │
                 ┌──────▼─┐ ┌──▼──────┐
                 │ vLLM / │ │ vLLM /  │
                 │ NIM    │ │ NIM     │
                 └────┬───┘ └───┬─────┘
                      │           │
                  ┌───▼───────────▼───┐
                  │ NVIDIA GPU Cluster │
                  └────────────────────┘

Then add Kubernetes + Prometheus/Grafana + centralized model storage + an API gateway around it.

If you're expecting multiple models, multiple teams, dozens of GPUs, or hundreds/thousands of concurrent users, I'd strongly favor NIM/vLLM + Kubernetes over Ollama-style tooling.

If you tell me your GPU hardware (e.g. 4× H100, 8× A100, RTX 6000 Ada, etc.), number of GPUs, expected concurrent users, and whether this is personal or enterprise, I can recommend the exact stack—including model server, Kubernetes setup, model registry, UI, monitoring, and model-routing layer.

Running a large language model on your own infrastructure requires a stack rather than a single tool. A complete self-hosted setup typically consists of three layers: the inference engine (to run the model on your GPUs), the gateway/proxy (to manage traffic, API keys, and routing), and the user interface/application layer (for team chats or RAG workflows).

The best platforms and frameworks for self-hosting depend heavily on your exact scale and use case:

1. Best Inference Engines (The Model Runtime)

These tools handle the heavy lifting of GPU memory management, KV caching, and token generation.

• vLLM: The industry standard for high-concurrency production applications. It utilizes PagedAttention to drastically reduce GPU memory fragmentation, supports continuous batching, and exposes a native OpenAI-compatible API out of the box.
• SGLang: The top choice for high-performance and agentic workloads. It introduces RadixAttention for advanced prefix caching (ideal for multi-turn chats and RAG) and features an ultra-fast structured generation backend for JSON/function calling.
• Ollama: The absolute best for local development, testing, and single-server/edge deployments. It handles model management and downloading with a single CLI command, though it is less optimized for heavy multi-tenant production traffic than vLLM.
• llama.cpp: The premier option if you are constrained to CPU inference, running on consumer hardware, or deploying lightweight models to edge devices.

2. Best Gateways & Proxy Layers

If you plan to route multiple models, track usage, or fallback to cloud APIs, you need a gateway.

• LiteLLM: A widely adopted open-source proxy that translates your requests into a unified OpenAI-compatible format across 100+ backends. It handles budget tracking, virtual key generation, and rate limiting seamlessly.

3. Best User Interfaces & Knowledge Platforms

If you need a ready-to-use frontend or want to hook your local models into company data (RAG):

• Open WebUI: Often described as the "Self-hosted ChatGPT." It provides a polished, feature-rich web interface with multi-user management, authentication, and direct integration with Ollama or vLLM backends.
• Onyx AI (formerly Danswer): The leading open-source enterprise platform for connecting self-hosted models to workplace tools (Slack, Jira, Google Drive, Confluence) while strictly preserving internal user access controls.
• AnythingLLM: An all-in-one workspace application tailored heavily for document-heavy RAG workflows and private file interrogation.

To help narrow down the ideal stack, could you share:

• Your hardware setup (e.g., local consumer GPUs, an on-premise server cluster, or rented cloud GPUs)
• Your primary use case (e.g., a private personal assistant, an API for internal developers, or a company-wide RAG knowledge base)

If your goal is serious self-hosting on your own servers, my top choice today is NVIDIA NIM, especially if you're running NVIDIA GPUs.

My ranking

Why I'd choose NIM

NIM is essentially a production layer around open inference technologies. Its current LLM architecture uses vLLM, provides OpenAI-compatible APIs, handles model/runtime configuration, and offers validated configurations for NVIDIA hardware. It can run in your data center, cloud, or air-gapped environment.

That's particularly attractive if you want:

• multiple models running simultaneously
• GPU-aware deployment
• standardized APIs
• Kubernetes integration
• monitoring/health management
• easier upgrades
• production support
• private/air-gapped deployment
• less time wrestling with CUDA, quantization and inference-engine configuration

NIM also supports model-free containers, so you aren't restricted to a tiny set of NVIDIA-packaged models; you can deploy custom/fine-tuned models from repositories or your own storage.

But there's an important distinction

If by "open-source platform" you mean you want the stack itself to be as open as possible, I'd build around vLLM instead.

vLLM is the inference engine I'd recommend as the core of a self-hosted LLM platform. You can then layer on:

Hugging Face Hub → model registry/storage → vLLM → API gateway → Kubernetes → observability

Hugging Face explicitly supports connecting its ecosystem to local vLLM, Ollama, llama.cpp, TGI and other local inference servers.

For a larger organization, I'd probably architect it roughly like this:

                    ┌─────────────────────┐
                    │   Applications      │
                    └──────────┬──────────┘
                               │ OpenAI API
                    ┌──────────▼──────────┐
                    │ API Gateway / Auth  │
                    └──────────┬──────────┘
                               │
              ┌────────────────▼────────────────┐
              │      LLM Serving Platform       │
              │                                 │
              │  vLLM / NVIDIA NIM / SGLang    │
              └───────┬───────────┬─────────────┘
                      │           │
                 ┌────▼───┐  ┌────▼───┐
                 │ Model A│  │ Model B│
                 └────────┘  └────────┘
                      │           │
              ┌───────▼───────────▼───────┐
              │     GPU Kubernetes        │
              └──────────────┬────────────┘
                             │
                  ┌──────────▼──────────┐
                  │ NVIDIA GPUs / H100  │
                  │ H200 / B200 / etc.  │
                  └─────────────────────┘

What I would actually deploy

For a new enterprise self-hosting project, I'd choose:

NVIDIA GPUs + Kubernetes + NVIDIA NIM + Hugging Face models + an OpenAI-compatible gateway

NIM is particularly compelling because it gives you the operational packaging while retaining the underlying open inference ecosystem; NVIDIA says its current NIM LLM architecture is directly aligned with upstream vLLM.

For a smaller team that wants maximum control, I'd drop NIM and use:

Kubernetes + vLLM + Hugging Face + LiteLLM

For a single workstation/server, don't over-engineer it: Ollama or vLLM is much simpler.

One caveat: Hugging Face Inference Endpoints isn't actually self-hosting—Hugging Face manages the infrastructure for you. It's excellent if you want managed deployment, but it doesn't meet your "my own infrastructure" requirement.

If you tell me how many GPUs you have (and which ones), whether you're using Kubernetes, and roughly how many concurrent users/requests you expect, I can recommend a concrete stack and architecture—including which inference engine, model sizes, GPU topology, and management layer I'd use.

When self-hosting and managing open-source (open-weight) LLMs on your own infrastructure, the "best" platform depends heavily on your scale, your hardware setup, and whether you are building for personal use, a small team, or enterprise production.

The self-hosted AI stack is typically divided into three layers: the Inference Engine (running the model), the Management/Interface Layer (user access and app wiring), and the Enterprise/Orchestration Layer (governance and RAG).

1. The Best Model Runtimes & Inference Engines

These platforms run directly on your GPU/CPU hardware and expose an API.

• Ollama : Best for simplicity and local/small-team development . It allows you to pull and run models (like Llama, Qwen, and GLM families) using a single command line. It manages GGUF formats smoothly and provides a local OpenAI-compatible API with minimal configuration.
• vLLM : Best for high-throughput production serving . If you are serving multiple concurrent users on dedicated infrastructure (like NVIDIA A100/H100 or RTX clusters), vLLM's PagedAttention technology drastically increases token throughput and reduces memory waste.
• llama.cpp / llama-server : Best for deep, lightweight control and resource-constrained hardware . It is the underlying engine for GGUF quantization, allowing you to fine-tune memory footprints precisely across CPU and GPU.

2. The Best User Interfaces & Management Dashboards

If you need a ChatGPT-style web UI that you can self-host for a group of users to interact with your models:

• Open WebUI : Best overall self-hosted chat interface . It acts as a feature-rich, self-hosted web UI that connects natively to Ollama or vLLM backends. It supports multi-user management, role-based access control (RBAC), RAG document integration, and a fully offline-capable Docker container deployment.
• Onyx (formerly Danswer) : Best for connecting self-hosted LLMs to internal company knowledge . It goes beyond a simple chat interface, providing built-in connectors to tools like Slack, Confluence, Google Drive, and GitHub while honoring source-system user permissions.

3. All-in-One Enterprise Infrastructure

• Prem AI : Best for zero-data-retention managed self-hosting . If you want a turnkey production environment that runs entirely on your private cloud or on-prem servers—complete with integrated fine-tuning pipelines and zero external telemetry—Prem bridges the gap without requiring a dedicated MLOps team to stitch together raw Docker components.

Recommended Starter Stacks

• For a single user or small dev team: Deploy Ollama as your backend engine combined with Open WebUI via Docker Compose on your local server or mini PC.
• For production enterprise scale: Deploy vLLM for optimized GPU inference tied into an enterprise gateway like Onyx for document search, access control, and user management.

To help narrow down the ideal architecture, tell me:

• What kind of hardware or GPU resources do you have available (e.g., consumer RTX cards, enterprise A100/H100, Apple Silicon)?
• Is this for personal use, a small team, or a public production app?

There is no single "best" platform because the ideal tool depends entirely on your scale and concurrency needs . Self-hosting platforms generally split into three distinct categories based on whether you are prototyping, serving a high-traffic production API, or looking for an all-in-one business application.

The leading platforms for self-hosting and managing open-source/open-weight LLMs break down as follows:

1. For High-Throughput Production & Multi-User APIs: vLLM

If you are serving applications to multiple concurrent users, managing heavy traffic, or running multi-GPU infrastructure, vLLM is the gold standard.

• How it works: It uses PagedAttention to manage KV-cache memory efficiently and supports continuous batching, packing far more requests onto your GPUs.
• Best for: Production APIs, high concurrency, and getting the maximum throughput per GPU dollar.
• Pros: Hugging Face-backed momentum, massive ecosystem, drops right into code expecting an OpenAI-compatible endpoint.
• Cons: Steeper learning curve and larger configuration surface than local developer tools. (Note: SGLang is also emerging as a high-throughput competitor, especially for complex agentic workflows with multi-turn caching).

2. For Local Development, Prototyping & Single-User Use: Ollama

If your goal is to spin up models quickly on local hardware or internal low-volume dev tools, Ollama is the fastest and friendlier path.

• How it works: It acts as a local appliance—one command downloads, quantizes (GGUF-native), and serves a model.
• Best for: Local dev environments, personal workstations, edge/Mac Silicon setups, and prototyping before a production push.
• Pros: Installs in minutes, supports every major OS, and exposes a clean local API endpoint.
• Cons: Throughput and performance degrade heavily under high concurrent multi-user loads.

3. For Complete Enterprise Workspaces & RAG: Open WebUI or Onyx AI

If you don't just want an inference engine, but a full governed interface (like ChatGPT) connected to your company knowledge base:

• Open WebUI: The most popular open-source web interface that plugs right into an underlying backend like Ollama or vLLM. It gives you user management, authentication, and RAG capabilities out of the box.
• Onyx AI: A robust open-source platform specifically designed to connect self-hosted LLMs to enterprise workflows (Slack, Google Drive, Confluence, etc.) while preserving strict access controls.

Suggested Deployment Strategy

Most mature self-hosted infrastructures don't pick just one—they layer them:

1. Use Ollama on developer laptops or staging environments for rapid testing.
2. Use vLLM on your production GPU cluster (or cloud infrastructure) to handle real user traffic.
3. Layer Open WebUI or a custom frontend on top for user-facing applications.

To help narrow down the ideal stack, tell me:

• What kind of hardware or GPU infrastructure do you have available?
• Are you planning for single-user/internal tools or a public-facing production API with high concurrency?

If your goal is running open-source LLMs on infrastructure you control, my recommendation in 2026 is:

Best overall: vLLM + Kubernetes

I would use vLLM as the inference layer and Kubernetes as the orchestration layer, rather than looking for one monolithic “LLM platform.” vLLM has become one of the most widely adopted open-source LLM serving frameworks, particularly for production workloads and high-throughput batching. A recent empirical study found it to be the most visible/adopted of the major LLM-serving frameworks.

A practical production stack would look like:

If you want the easiest enterprise option

Consider NVIDIA NIM. It packages models, optimized inference engines, dependencies, and APIs into deployable containers, and can run on your own data center/workstations. It is particularly attractive if you're already standardized on NVIDIA hardware and want less operational work.

The tradeoff is that vLLM is the more genuinely open-source/flexible foundation, whereas NIM is more of an enterprise product layer around optimized inference. NIM itself is built substantially on open-source engines including vLLM.

What I'd choose by situation

One server / experimentation → Ollama or vLLM

A few GPU servers / production → Kubernetes + vLLM + LiteLLM

Large enterprise GPU cluster → Kubernetes + vLLM/SGLang + NVIDIA NIM, with the surrounding LLMOps stack

Very large multi-node inference → Look at NVIDIA Dynamo in addition to vLLM/SGLang. Dynamo is specifically designed for distributed, multi-node inference and can coordinate inference across GPUs/nodes.

Maximum simplicity → NVIDIA NIM

Maximum control and avoidance of vendor lock-in → Kubernetes + vLLM

One important distinction: vLLM isn't really an LLM management platform. It's the serving engine. Once you have multiple models, teams, GPUs, versions, quotas, deployments, evaluations, and observability, you need the surrounding platform. That's why I'd build a small composable stack rather than searching for a single product.

If you tell me your GPU hardware (e.g. 2×A100, 8×H100, RTX 6000 Ada, etc.), number of users/requests, and whether you're using Kubernetes, I can recommend a concrete architecture—including which LLMs to run, GPU allocation, model quantization, and the software stack.