LLM Serving: Ollama vs vLLM vs TGI

Choose the right LLM serving solution for your needs on CLORE.AI.

All options available on CLORE.AI Marketplace.

Quick Decision Guide

Use Case	Best Choice	Why
Quick testing & chat	Ollama	Easiest setup, fastest startup
Production API	vLLM	Highest throughput
HuggingFace integration	TGI	Native HF support
Local development	Ollama	Works everywhere
High concurrency	vLLM	Continuous batching
Multi-modal (TTS, STT, Embeddings)	LocalAI	All-in-one solution
Streaming apps	vLLM or TGI	Both excellent

Startup Time Comparison

Solution	Typical Startup	Notes
Ollama	30-60 seconds	Fastest, lightweight
vLLM	5-15 minutes	Downloads model from HF
TGI	3-10 minutes	Downloads model from HF
LocalAI	5-10 minutes	Pre-loads multiple models

HTTP 502 errors during startup are normal - the service is still initializing.

---

Overview Comparison

Feature	Ollama	vLLM	TGI	LocalAI
Ease of Setup	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Performance	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐
Model Support	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
API Compatibility	Custom + OpenAI	OpenAI	Custom + OpenAI	OpenAI
Multi-GPU	Limited	Excellent	Good	Limited
Memory Efficiency	Good	Excellent	Very Good	Good
Multi-Modal	Vision only	Vision only	No	TTS, STT, Embed
Startup Time	30 sec	5-15 min	3-10 min	5-10 min
Best For	Development	Production	HF Ecosystem	Multi-modal

---

Ollama

Overview

Ollama is the easiest way to run LLMs locally. Perfect for development, testing, and personal use.

Pros

• ✅ One-command install and run

• ✅ Built-in model library

• ✅ Great CLI experience

• ✅ Works on Mac, Linux, Windows

• ✅ Automatic quantization

• ✅ Low resource overhead

Cons

• ❌ Lower throughput than alternatives

• ❌ Limited multi-GPU support

• ❌ Less production-ready

• ❌ Fewer optimization options

Quick Start

# Install
curl -fsSL https://ollama.com/install.sh | sh

# Run any model
ollama run llama3.2
ollama run mistral
ollama run codellama

# Serve API
ollama serve

API Usage

import requests

# Generate
response = requests.post('http://localhost:11434/api/generate', json={
    'model': 'llama3.2',
    'prompt': 'Explain quantum computing',
    'stream': False
})
print(response.json()['response'])

# Chat
response = requests.post('http://localhost:11434/api/chat', json={
    'model': 'llama3.2',
    'messages': [
        {'role': 'user', 'content': 'Hello!'}
    ]
})

OpenAI Compatibility

from openai import OpenAI

client = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')

response = client.chat.completions.create(
    model='llama3.2',
    messages=[{'role': 'user', 'content': 'Hello!'}]
)

Performance

Model	GPU	Tokens/sec
Llama 3.2 3B	RTX 3060	45-55
Llama 3.1 8B	RTX 3090	35-45
Llama 3.1 70B	A100 40GB	15-20

Best For

• 🎯 Quick prototyping

• 🎯 Personal AI assistant

• 🎯 Learning and experimentation

• 🎯 Simple deployments

---

vLLM

Overview

vLLM is the fastest LLM inference engine, designed for production deployments with maximum throughput.

Pros

• ✅ Highest throughput (continuous batching)

• ✅ PagedAttention for efficient memory

• ✅ Excellent multi-GPU support

• ✅ OpenAI-compatible API

• ✅ Production-ready

• ✅ Supports many quantization formats

Cons

• ❌ More complex setup

• ❌ Higher memory overhead at start

• ❌ Linux-only (no native Windows/Mac)

• ❌ Requires more configuration

Quick Start

pip install vllm

# Serve model
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --host 0.0.0.0 \
    --port 8000

Docker Deploy

docker run --gpus all -p 8000:8000 \
    vllm/vllm-openai:latest \
    --model meta-llama/Llama-3.1-8B-Instruct

API Usage

from openai import OpenAI

client = OpenAI(base_url='http://localhost:8000/v1', api_key='dummy')

# Chat completion
response = client.chat.completions.create(
    model='meta-llama/Llama-3.1-8B-Instruct',
    messages=[
        {'role': 'system', 'content': 'You are helpful.'},
        {'role': 'user', 'content': 'Write a haiku about coding'}
    ],
    temperature=0.7,
    max_tokens=100
)

# Streaming
stream = client.chat.completions.create(
    model='meta-llama/Llama-3.1-8B-Instruct',
    messages=[{'role': 'user', 'content': 'Tell me a story'}],
    stream=True
)
for chunk in stream:
    print(chunk.choices[0].delta.content, end='')

Multi-GPU

# 2 GPUs
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 2

# 4 GPUs
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 4

Performance

Model	GPU	Tokens/sec	Concurrent Users
Llama 3.1 8B	RTX 3090	80-100	10-20
Llama 3.1 8B	RTX 4090	120-150	20-30
Llama 3.1 70B	A100 40GB	25-35	5-10
Llama 3.1 70B	2x A100	50-70	15-25

Best For

• 🎯 Production APIs

• 🎯 High-traffic applications

• 🎯 Multi-user chat services

• 🎯 Maximum throughput needs

---

Text Generation Inference (TGI)

Overview

HuggingFace's production server, tightly integrated with the HF ecosystem.

Pros

• ✅ Native HuggingFace integration

• ✅ Great for HF models

• ✅ Good multi-GPU support

• ✅ Built-in safety features

• ✅ Prometheus metrics

• ✅ Well-documented

Cons

• ❌ Slightly lower throughput than vLLM

• ❌ More resource intensive

• ❌ Complex configuration

• ❌ Longer startup times

Quick Start

# Docker (recommended)
docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-3.1-8B-Instruct

# With HF token for gated models
docker run --gpus all -p 8080:80 \
    -e HUGGING_FACE_HUB_TOKEN=$HF_TOKEN \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-3.1-8B-Instruct

API Usage

import requests

# Generate
response = requests.post('http://localhost:8080/generate', json={
    'inputs': 'What is machine learning?',
    'parameters': {
        'max_new_tokens': 200,
        'temperature': 0.7
    }
})
print(response.json()['generated_text'])

# Streaming
response = requests.post('http://localhost:8080/generate_stream', json={
    'inputs': 'Tell me a story',
    'parameters': {'max_new_tokens': 500}
}, stream=True)

for line in response.iter_lines():
    print(line.decode())

OpenAI Compatibility

from openai import OpenAI

client = OpenAI(base_url='http://localhost:8080/v1', api_key='dummy')

response = client.chat.completions.create(
    model='tgi',
    messages=[{'role': 'user', 'content': 'Hello!'}]
)

Configuration Options

docker run --gpus all -p 8080:80 \
    ghcr.io/huggingface/text-generation-inference:latest \
    --model-id meta-llama/Llama-3.1-8B-Instruct \
    --max-input-length 4096 \
    --max-total-tokens 8192 \
    --max-batch-prefill-tokens 4096 \
    --quantize bitsandbytes-nf4

Performance

Model	GPU	Tokens/sec	Concurrent Users
Llama 3.1 8B	RTX 3090	60-80	8-15
Llama 3.1 8B	RTX 4090	90-120	15-25
Llama 3.1 70B	A100 40GB	20-30	3-8

Best For

• 🎯 HuggingFace model users

• 🎯 Research environments

• 🎯 Need built-in safety features

• 🎯 Prometheus monitoring needs

---

LocalAI

Overview

LocalAI is an OpenAI-compatible API that supports multiple modalities: LLMs, TTS, STT, embeddings, and image generation.

Pros

• ✅ Multi-modal support (LLM, TTS, STT, embeddings)

• ✅ Drop-in OpenAI replacement

• ✅ Pre-built models available

• ✅ Supports GGUF models

• ✅ Reranking support

• ✅ Swagger UI documentation

Cons

• ❌ Longer startup time (5-10 minutes)

• ❌ Lower LLM throughput than vLLM

• ❌ Image generation may have CUDA issues

• ❌ More complex for pure LLM use

Quick Start

docker run --gpus all -p 8080:8080 localai/localai:master-aio-gpu-nvidia-cuda-12

Pre-Built Models

LocalAI comes with several models ready to use:

Model	Type
gpt-4, gpt-4o	Chat
whisper-1	Speech-to-text
tts-1	Text-to-speech
text-embedding-ada-002	Embeddings
jina-reranker-v1-base-en	Reranking

API Usage

from openai import OpenAI

client = OpenAI(base_url='http://localhost:8080/v1', api_key='dummy')

# Chat
response = client.chat.completions.create(
    model='gpt-4',
    messages=[{'role': 'user', 'content': 'Hello!'}]
)

# TTS
audio = client.audio.speech.create(model='tts-1', input='Hello world', voice='alloy')

# STT
transcript = client.audio.transcriptions.create(model='whisper-1', file=open('audio.mp3', 'rb'))

# Embeddings
embeddings = client.embeddings.create(model='text-embedding-ada-002', input='Hello world')

Best For

• 🎯 Need multiple modalities (TTS, STT, LLM)

• 🎯 Want OpenAI API compatibility

• 🎯 Running GGUF models

• 🎯 Document reranking workflows

---

Performance Comparison

Throughput (tokens/second) - Single User

Model	Ollama	vLLM	TGI
Llama 3.1 8B (RTX 3090)	40	90	70
Llama 3.1 8B (RTX 4090)	65	140	110
Llama 3.1 70B (A100 40GB)	18	30	25

Throughput - Multiple Users (10 concurrent)

Model	Ollama	vLLM	TGI
Llama 3.1 8B (RTX 4090)	150*	800	500
Llama 3.1 70B (A100 40GB)	50*	200	150

*Ollama serves sequentially by default

Memory Usage

Model	Ollama	vLLM	TGI
Llama 3.1 8B	5GB	6GB	7GB
Llama 3.1 70B (Q4)	38GB	40GB	42GB

Time to First Token (TTFT)

Model	Ollama	vLLM	TGI
Llama 3.1 8B	0.2s	0.1s	0.15s
Llama 3.1 70B	1.5s	0.8s	1.0s

---

Feature Comparison

Feature	Ollama	vLLM	TGI	LocalAI
OpenAI API	✅	✅	✅	✅
Streaming	✅	✅	✅	✅
Batching	Basic	Continuous	Dynamic	Basic
Multi-GPU	Limited	Excellent	Good	Limited
Quantization	GGUF	AWQ, GPTQ	bitsandbytes, AWQ	GGUF
LoRA	✅	✅	✅	✅
Speculative Decoding	❌	✅	✅	❌
Prefix Caching	❌	✅	✅	❌
Metrics	Basic	Prometheus	Prometheus	Prometheus
Function Calling	✅	✅	✅	✅
Vision Models	✅	✅	✅	Limited
TTS	❌	❌	❌	✅
STT	❌	❌	❌	✅
Embeddings	✅	Limited	Limited	✅
Reranking	❌	❌	❌	✅

---

When to Use What

Use Ollama When:

• You want to get started in 5 minutes

• You're prototyping or learning

• You need a personal AI assistant

• You're on Mac or Windows

• Simplicity matters more than speed

Use vLLM When:

• You need maximum throughput

• You're serving many users

• You need production reliability

• You want OpenAI drop-in replacement

• You have multi-GPU setups

Use TGI When:

• You're in the HuggingFace ecosystem

• You need built-in safety features

• You want detailed Prometheus metrics

• You need to serve HF models directly

• You're in a research environment

Use LocalAI When:

• You need TTS and STT alongside LLM

• You want embeddings for RAG

• You need document reranking

• You want a single all-in-one solution

• You're building voice-enabled apps

---

Migration Guide

From Ollama to vLLM

# Ollama
client = OpenAI(base_url='http://localhost:11434/v1', api_key='ollama')
response = client.chat.completions.create(model='llama3.2', ...)

# vLLM - just change URL and model name
client = OpenAI(base_url='http://localhost:8000/v1', api_key='dummy')
response = client.chat.completions.create(model='meta-llama/Llama-3.2-3B-Instruct', ...)

From TGI to vLLM

Both support OpenAI API - just change the endpoint URL.

---

Recommendations by GPU

GPU	Single User	Multi User
RTX 3060 12GB	Ollama	Ollama
RTX 3090 24GB	Ollama	vLLM
RTX 4090 24GB	vLLM	vLLM
A100 40GB+	vLLM	vLLM

---

Next Steps

• Ollama Guide - Easiest setup

• vLLM Guide - Highest throughput

• LocalAI Guide - Multi-modal support

• Multi-GPU Setup - Scale to larger models

• API Integration - Build applications