LLM Serving: Ollama vs vLLM vs TGI

Compare vLLM vs SGLang vs Ollama vs TGI vs LocalAI for LLM serving

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2025 Update: SGLang has emerged as a top-tier framework, often outperforming vLLM in throughput and TTFT benchmarks. Both vLLM v0.7 and SGLang v0.4 are recommended for production workloads.

Quick Decision Guide

Multi-GPU 80GB-class rigs are not listed on the Clore.ai marketplace. The biggest boxes listed today are 4× RTX PRO 6000 Blackwell (96GB each, 380GB total) and 8–11× RTX 5090 (32GB each). A100 / H200 / B200 capacity is sold as bare metal on request. Check GPU Pricing & Availability before sizing a deployment.

Use Case	Best Choice	Why
Quick testing & chat	Ollama	Easiest setup, fastest startup
Production API (max throughput)	SGLang or vLLM	Highest throughput in 2025
Reasoning models (DeepSeek-R1)	SGLang	Best support for reasoning chains
HuggingFace integration	TGI	Native HF support
Local development	Ollama	Works everywhere
High concurrency	SGLang or vLLM	Continuous batching
Multi-modal (TTS, STT, Embeddings)	LocalAI	All-in-one solution
Streaming apps	vLLM or SGLang	Both excellent

Startup Time Comparison

Solution	Typical Startup	Notes
Ollama	30-60 seconds	Fastest, lightweight
SGLang	3-8 minutes	Downloads model from HF
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	SGLang	TGI	LocalAI
Ease of Setup	⭐⭐⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐
Performance	⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐
Model Support	⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐	⭐⭐⭐⭐
API Compatibility	Custom + OpenAI	OpenAI	OpenAI	Custom + OpenAI	OpenAI
Multi-GPU	Limited	Excellent	Excellent	Good	Limited
Memory Efficiency	Good	Excellent	Excellent	Very Good	Good
Multi-Modal	Vision only	Vision only	Vision only	No	TTS, STT, Embed
Startup Time	30 sec	5-15 min	3-8 min	3-10 min	5-10 min
Reasoning Models	Limited	Good	Excellent	Good	Limited
Best For	Development	Production	Production + Reasoning	HF Ecosystem	Multi-modal

---

2025 Benchmarks: DeepSeek-R1-32B

TTFT, TPOT & Throughput (A100 80GB, batch=32, input=512, output=512)

Framework	TTFT (ms)	TPOT (ms/tok)	Throughput (tok/s)	Notes
SGLang v0.4	180	14	2,850	Best overall 2025
vLLM v0.7	240	17	2,400	Excellent, close to SGLang
llama.cpp	420	28	1,100	CPU+GPU, quantized
Ollama	510	35	820	Ease of use priority

TTFT = Time to First Token (latency). TPOT = Time Per Output Token. Lower is better for both.

Throughput Comparison (RTX 4090, Llama 3.1 8B, 10 concurrent users)

Framework	Tokens/sec	Concurrent Users	Notes
SGLang v0.4	920	20-30	Radix attention caching
vLLM v0.7	870	20-30	PagedAttention
TGI	550	10-20
Ollama	160*	—	Sequential by default

*Ollama serves requests sequentially by default

---

SGLang

Overview

SGLang (Structured Generation Language) is a high-throughput LLM serving framework developed by researchers from UC Berkeley and LMSYS. In 2025 benchmarks it frequently matches or exceeds vLLM — especially for reasoning models like DeepSeek-R1.

Pros

• ✅ Often fastest TTFT and throughput in 2025 benchmarks

• ✅ Radix attention for efficient KV-cache reuse

• ✅ Excellent support for reasoning models (DeepSeek-R1, QwQ)

• ✅ OpenAI-compatible API

• ✅ Continuous batching and prefix caching

• ✅ Speculative decoding support

• ✅ Multi-GPU tensor parallelism

Cons

• ❌ Newer ecosystem, fewer community resources than vLLM

• ❌ More complex setup than Ollama

• ❌ Linux-only

Quick Start

pip install sglang[all]

# Serve a model
python -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-8B-Instruct \
    --host 0.0.0.0 \
    --port 8000

DeepSeek-R1 with SGLang

python -m sglang.launch_server \
    --model-path deepseek-ai/DeepSeek-R1-Distill-Qwen-32B \
    --host 0.0.0.0 \
    --port 8000 \
    --tp 2 \
    --reasoning-parser deepseek-r1

API Usage

from openai import OpenAI

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

response = client.chat.completions.create(
    model='meta-llama/Llama-3.1-8B-Instruct',
    messages=[
        {'role': 'user', 'content': 'Explain quantum entanglement'}
    ],
    temperature=0.7,
    max_tokens=512
)
print(response.choices[0].message.content)

Multi-GPU

# 2 GPUs (tensor parallel)
python -m sglang.launch_server \
    --model-path meta-llama/Llama-3.1-70B-Instruct \
    --host 0.0.0.0 \
    --port 8000 \
    --tp 2

Best For

• 🎯 Maximum throughput production APIs

• 🎯 Reasoning models (DeepSeek-R1, QwQ, o1-style)

• 🎯 Low-latency (TTFT) applications

• 🎯 Prefix-heavy workloads (high KV-cache reuse)

---

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 a battle-tested high-throughput LLM inference engine for production. v0.7 (2025) brings improved performance, better quantization support, and new speculative decoding options.

Pros

• ✅ Highest throughput (continuous batching + PagedAttention)

• ✅ PagedAttention for efficient memory

• ✅ Excellent multi-GPU support

• ✅ OpenAI-compatible API

• ✅ Production-ready, large community

• ✅ Supports many quantization formats (AWQ, GPTQ, FP8)

• ✅ Speculative decoding in v0.7

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 (vLLM v0.7)
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:v0.7.0 \
    --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 with large community

• 🎯 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/SGLang

• ❌ 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

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/SGLang

• ❌ 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

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 (2025)

Throughput (tokens/second) — Single User

Model	Ollama	vLLM v0.7	SGLang v0.4	TGI
Llama 3.1 8B (RTX 3090)	40	90	100	70
Llama 3.1 8B (RTX 4090)	65	140	160	110
Llama 3.1 70B (A100 40GB)	18	30	35	25

Throughput — Multiple Users (10 concurrent)

Model	Ollama	vLLM v0.7	SGLang v0.4	TGI
Llama 3.1 8B (RTX 4090)	150*	800	920	500
Llama 3.1 70B (A100 40GB)	50*	200	240	150

*Ollama serves sequentially by default

Memory Usage

Model	Ollama	vLLM v0.7	SGLang v0.4	TGI
Llama 3.1 8B	5GB	6GB	6GB	7GB
Llama 3.1 70B (Q4)	38GB	40GB	39GB	42GB

Time to First Token (TTFT) — DeepSeek-R1-32B

Framework	TTFT (A100 80GB)	TPOT (ms/tok)
SGLang v0.4	180ms	14ms
vLLM v0.7	240ms	17ms
llama.cpp	420ms	28ms
Ollama	510ms	35ms

---

Feature Comparison

Feature	Ollama	vLLM v0.7	SGLang v0.4	TGI	LocalAI
OpenAI API	✅	✅	✅	✅	✅
Streaming	✅	✅	✅	✅	✅
Batching	Basic	Continuous	Continuous	Dynamic	Basic
Multi-GPU	Limited	Excellent	Excellent	Good	Limited
Quantization	GGUF	AWQ, GPTQ, FP8	AWQ, GPTQ, FP8	bitsandbytes, AWQ	GGUF
LoRA	✅	✅	✅	✅	✅
Speculative Decoding	❌	✅	✅	✅	❌
Prefix Caching	❌	✅	✅ (Radix)	✅	❌
Reasoning Models	Limited	Good	Excellent	Good	Limited
Metrics	Basic	Prometheus	Prometheus	Prometheus	Prometheus
Function Calling	✅	✅	✅	✅	✅
Vision Models	✅	✅	✅	✅	Limited
TTS	❌	❌	❌	❌	✅
STT	❌	❌	❌	❌	✅
Embeddings	✅	Limited	Limited	Limited	✅

---

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 SGLang When:

• You need the absolute lowest latency (TTFT)

• You're serving reasoning models (DeepSeek-R1, QwQ, o1-style)

• You have workloads with heavy prefix sharing (RAG, system prompts)

• You need top-tier throughput in 2025 benchmarks

• You want cutting-edge optimizations (Radix attention)

Use vLLM When:

• You need maximum throughput with a mature, well-supported framework

• You're serving many users at scale

• You need production reliability with a large community

• You want OpenAI drop-in replacement

• You have multi-GPU setups

• You need broad model format support (AWQ, GPTQ, FP8)

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 SGLang

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

# SGLang - 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 vLLM to SGLang

Both support OpenAI API - just change the endpoint URL. APIs are fully compatible.

# vLLM
python -m vllm.entrypoints.openai.api_server --model ... --port 8000

# SGLang (equivalent)
python -m sglang.launch_server --model-path ... --port 8000

---

Recommendations by GPU

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

---

Next Steps

• Ollama Guide - Easiest setup

• vLLM Guide - Highest throughput

• LocalAI Guide - Multi-modal support

• DeepSeek-R1 Guide - Reasoning models

• Multi-GPU Setup - Scale to larger models

• API Integration - Build applications