Local LLMs

vLLM vs TGI vs Ollama: choose a local or production LLM serving stack

Compare vLLM, Hugging Face Text Generation Inference, and Ollama for local development, OpenAI-compatible serving, production inference, GPUs, throughput, and operations.

Updated 2026-06-119 min readAdvanced

Read local LLM GPU calculator Read Ollama vs LM Studio

Best for

Developers moving from local models to production serving
Teams comparing Ollama, vLLM, and TGI for private inference
RAG builders choosing local or self-hosted model infrastructure
Readers planning GPU usage, throughput, and OpenAI-compatible APIs

Not for

A complete benchmark for every model and GPU
Managed cloud inference vendor comparison
Production rollout without load testing and security review

Comparison

Choose by workflow, not brand

Option	Best for	Strengths	Tradeoffs	Use when
Ollama	Local development, simple private AI, desktop workflows, and quick model experiments	Easy local setup and local API access.	Not the default choice for high-throughput production serving.	You want a local model running quickly on a workstation.
vLLM	Production serving, higher-throughput inference, OpenAI-compatible APIs, and GPU deployments	Designed for efficient serving and OpenAI-compatible server workflows.	Requires more operations knowledge than local desktop tools.	You need a self-hosted inference service, not just local experimentation.
TGI	Existing Hugging Face Text Generation Inference deployments and teams maintaining previous TGI setups	Established history in Hugging Face inference infrastructure.	Official docs indicate maintenance mode, so new projects should check current recommendations.	You already run TGI or have a specific reason to maintain it.

Local development is not production serving

A local tool can be excellent for learning and private workflows but still be the wrong production runtime. Production serving adds concurrency, monitoring, autoscaling, security, queueing, model updates, and load testing.

Use Ollama to learn and prototype quickly.
Move to vLLM or managed inference when throughput and uptime matter.
Do not expose local APIs without authentication and network controls.

OpenAI-compatible APIs matter

OpenAI-compatible serving lets existing apps and SDKs point at self-hosted models with fewer code changes. But compatibility is not identical behavior, so test tool calling, JSON output, streaming, and error handling.

Verify chat completions, streaming, and structured output behavior.
Keep model-specific prompts versioned.
Measure latency and throughput with your real prompt lengths.

Operational decision checklist

Choose a serving stack only after testing model fit, GPU memory, concurrency, queue behavior, cold starts, metrics, logs, upgrades, and fallback behavior.

Run load tests at p50, p90, and p99 prompt sizes.
Plan model rollout and rollback.
Track tokens per second, time to first token, and error rates.

Decision Rules

A practical checklist

Use Ollama for local experimentation and private workstation workflows.

Use vLLM for self-hosted production inference and OpenAI-compatible serving.

Use TGI mainly when maintaining an existing TGI deployment or after confirming current fit.

Always test on your own model, GPU, context length, and concurrency.

Related Guides

Continue the decision path

Read local LLM GPU calculator

Estimate VRAM before choosing a serving stack.

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Read Ollama vs LM Studio

Choose the local desktop and developer workflow first.

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Local LLM GPU calculator

Estimate memory fit before picking a serving runtime.

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Ollama vs LM Studio

Choose local development tools before production serving.

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LLM gateway comparison

Route between self-hosted and managed model providers.

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Chinese Archive

Aligned deeper reading

Ollama Chinese archive

Chinese local LLM and Ollama learning materials.

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DeepSeek local practice

Chinese open-model and local deployment tutorials.

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Topic Hubs

Explore the wider search cluster

Topic hub

RAG and models

Plan RAG systems, local LLM deployment, model APIs, cloud AI platforms, vector databases, evaluation, observability, rate limits, and cost optimization.

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Industry Pages

See this guide in a buyer workflow

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IT operations AI

Compare AI tools for ITSM, AIOps, SaaS management, LLM observability, gateways, rate limits, fallback routing, enterprise search, knowledge management, and IT governance.

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FAQ

Common questions

Is vLLM better than Ollama?

vLLM is usually a better fit for production serving and throughput. Ollama is usually a better fit for local development and simple private workflows.

Should I start a new project with TGI?

Check the current Hugging Face documentation first. TGI has been important historically, but official docs now indicate maintenance-mode status in some contexts.

Can local LLM serving replace hosted APIs?

Sometimes, but only after testing quality, latency, GPU cost, operations, scaling, monitoring, and fallback behavior.

Source Links

Primary references used for this guide

Reference

vLLM online serving

Official vLLM serving documentation.

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Reference

Hugging Face TGI docs

Official Hugging Face Text Generation Inference documentation.

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Reference

Ollama API docs

Official Ollama API documentation.

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Build your own evaluation note

The strongest decision is always local to your workflow. Save the vendor links, define a representative task, record the exact prompt or command, and compare the final evidence instead of the marketing claim.

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