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Ollama vs vLLM Complete Comparison - Features, Performance, and Usage Guide

A comprehensive comparison of Ollama and vLLM covering architecture, installation, performance, API, and environment-specific usage. A guide for choosing the right LLM inference engine from local development to production serving.

Data DynamicsApril 16, 202620 min read

Running LLMs on your own infrastructure requires an inference engine. Ollama and vLLM are the two most popular choices, each optimized for local development and production serving respectively. This post systematically compares their architecture, performance, and usage.

1. Overview of Ollama and vLLM

What is Ollama?

Ollama is a tool designed to easily run LLMs in local environments. Like Docker, a single command ollama run llama3 downloads the model and starts a conversation immediately.

Core philosophy: Anyone should be able to run LLMs locally without complex setup.

Developer: Ollama Inc.
License: MIT
Core technology: llama.cpp (GGML/GGUF)
Supported platforms: macOS, Linux, Windows

What is vLLM?

vLLM is an inference engine designed to serve LLMs at high performance in large-scale production environments. Started at UC Berkeley, it achieves high throughput through an innovative memory management technique called PagedAttention.

Core philosophy: Handle maximum requests with minimum GPU resources.

Developer: UC Berkeley (now vLLM project)
License: Apache 2.0
Core technology: PagedAttention, Continuous Batching
Supported platforms: Linux (CUDA GPU required)

Quick Comparison Summary

Comparison	Ollama	vLLM
Primary purpose	Local dev, prototyping	Production serving, large-scale inference
Installation difficulty	Very easy (one-click)	Medium (Python/CUDA environment needed)
Model format	GGUF (quantized)	Hugging Face (FP16/BF16)
CPU inference	Yes (default)	No (GPU required)
GPU usage	Optional (CPU/GPU hybrid)	Required (CUDA optimized)
Concurrent requests	Limited	Excellent (Continuous Batching)
Throughput	Medium	Very high
Memory efficiency	Savings via quantization	Optimized via PagedAttention
OpenAI compatible API	Yes	Yes
LoRA hot-swap	No	Yes
Tensor parallel (Multi-GPU)	Limited	Yes (full support)
Multimodal	Yes (Vision models)	Yes (Vision models)
Target users	Developers, researchers, individuals	ML engineers, infrastructure teams

2. Architecture and Core Technology

Ollama: llama.cpp-Based Local Inference Engine

Ollama internally uses llama.cpp as its inference backend. llama.cpp is a lightweight LLM inference library written in C/C++ that can efficiently run quantized models even on CPUs.

[Ollama Architecture]

User (CLI / API)
      ↓
┌─────────────────┐
│   Ollama Server  │  ← Service layer written in Go
│  (REST API)     │
└────────┬────────┘
         ↓
┌─────────────────┐
│   llama.cpp     │  ← C/C++ inference engine
│  (GGUF models)  │
└────────┬────────┘
         ↓
   CPU / GPU (Metal, CUDA, ROCm)

Key features:

GGUF format: Quantized model files (Q4_K_M, Q5_K_M, Q8_0, etc.)
CPU optimization: Leverages AVX2, AVX-512, ARM NEON
GPU offloading: Accelerate by loading some layers onto GPU
Memory mapping: Load directly from disk via mmap, saving RAM
Model management: Pull, list, and remove models like Docker Hub

vLLM: PagedAttention and High-Performance Serving Engine

vLLM's core innovation is PagedAttention. Inspired by virtual memory paging in operating systems, it manages KV cache in fixed-size blocks.

[vLLM Architecture]

User (HTTP API)
      ↓
┌──────────────────────┐
│  OpenAI Compatible   │  ← FastAPI based
│  API Server          │
└──────────┬───────────┘
           ↓
┌──────────────────────┐
│     vLLM Engine      │
│  ┌────────────────┐  │
│  │ Scheduler      │  │  ← Continuous Batching
│  └───────┬────────┘  │
│  ┌───────┴────────┐  │
│  │ PagedAttention │  │  ← KV Cache block management
│  │ (Block Manager)│  │
│  └───────┬────────┘  │
│  ┌───────┴────────┐  │
│  │ Model Executor │  │  ← GPU kernel execution
│  └────────────────┘  │
└──────────────────────┘
           ↓
      CUDA GPU (required)

PagedAttention core concept:

In traditional LLM serving, each request pre-allocates KV cache memory for the maximum sequence length. This wastes GPU memory regardless of actual usage.

[Traditional: Contiguous memory allocation]
Request 1: [████████░░░░░░░░]  ← Half used, rest wasted
Request 2: [██████░░░░░░░░░░]  ← 1/3 used, rest wasted
Request 3: [Out of memory — queued]

[vLLM PagedAttention: Block-level allocation]
Request 1: [██][██][██][██]     ← Allocate only needed blocks
Request 2: [██][██][██]         ← Allocate only needed blocks
Request 3: [██][██]             ← Remaining blocks available!

Reduced memory waste: 60–80% memory savings vs traditional approach
Increased concurrent throughput: 2–4x more requests on the same GPU
Continuous Batching: New requests immediately added to batch as others complete

Internal Mechanism Comparison

Aspect	Ollama (llama.cpp)	vLLM
Language	C/C++ + Go (service)	Python + C++/CUDA (kernels)
Memory management	mmap + quantization	PagedAttention
Batching	Simple (sequential processing)	Continuous Batching
KV Cache	Static allocation	Dynamic block allocation
Quantization	GGUF (Q4, Q5, Q8, etc.)	AWQ, GPTQ, FP8
Compute backend	CPU (default) + GPU offloading	CUDA GPU (required)

3. Installation and Basic Usage

Ollama Installation and Model Execution

Installation (one-click):

# macOS / Linux
curl -fsSL https://ollama.com/install.sh | sh
 
# Windows
# Download installer from ollama.com
 
# Verify installation
ollama --version

Running models:

# Download and run model (auto-pulls)
ollama run llama3.1
 
# Run specific model size
ollama run llama3.1:8b
ollama run llama3.1:70b
 
# Start conversation
>>> Hello, how do I resolve data skew in Spark?

Model management:

# List available models
ollama list
 
# Download only (without running)
ollama pull llama3.1:8b
 
# Delete model
ollama rm llama3.1:8b
 
# Show model info
ollama show llama3.1:8b

API server (auto-started):

# Background server starts automatically on install (port 11434)
curl http://localhost:11434/api/generate -d '{
  "model": "llama3.1",
  "prompt": "List 3 advantages of Kubernetes.",
  "stream": false
}'

vLLM Installation and API Server

Installation (Python + CUDA required):

# Basic installation
pip install vllm
 
# Install for specific CUDA version
pip install vllm --extra-index-url https://download.pytorch.org/whl/cu121

Start API server:

# Start OpenAI-compatible API server
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --host 0.0.0.0 \
    --port 8000 \
    --max-model-len 4096 \
    --gpu-memory-utilization 0.9

API call:

# Call via OpenAI-compatible API
curl http://localhost:8000/v1/chat/completions -H "Content-Type: application/json" -d '{
  "model": "meta-llama/Llama-3.1-8B-Instruct",
  "messages": [
    {"role": "user", "content": "List 3 advantages of Kubernetes."}
  ],
  "temperature": 0.7,
  "max_tokens": 512
}'

Direct Python usage:

from vllm import LLM, SamplingParams
 
llm = LLM(
    model="meta-llama/Llama-3.1-8B-Instruct",
    gpu_memory_utilization=0.9,
    max_model_len=4096
)
 
sampling_params = SamplingParams(
    temperature=0.7,
    top_p=0.9,
    max_tokens=512
)
 
prompts = [
    "How to resolve data skew in Spark?",
    "What causes Kafka consumer lag?",
    "Airflow DAG design best practices?"
]
 
outputs = llm.generate(prompts, sampling_params)
for output in outputs:
    print(f"Prompt: {output.prompt[:50]}...")
    print(f"Response: {output.outputs[0].text[:200]}...")
    print("---")

Docker-Based Deployment

Ollama Docker:

# With GPU
docker run -d --gpus all \
    -v ollama:/root/.ollama \
    -p 11434:11434 \
    --name ollama \
    ollama/ollama
 
# Run model
docker exec -it ollama ollama run llama3.1:8b
 
# CPU only (no GPU)
docker run -d \
    -v ollama:/root/.ollama \
    -p 11434:11434 \
    --name ollama \
    ollama/ollama

vLLM Docker:

docker run --runtime nvidia --gpus all \
    -v ~/.cache/huggingface:/root/.cache/huggingface \
    -p 8000:8000 \
    --ipc=host \
    vllm/vllm-openai:latest \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --max-model-len 4096

Comparison:

Item	Ollama Docker	vLLM Docker
GPU required	No (CPU possible)	Yes
Image size	~1.5 GB	~8 GB
Model management	Built-in (ollama pull)	External (Hugging Face cache)
Setup complexity	Low	Medium

4. Supported Models and Formats

Ollama: GGUF Format and Modelfile

Ollama uses GGUF (GPT-Generated Unified Format) quantized models.

Available quantization levels:

Quantization	Bits	Model Size (7B)	Quality	Speed
Q2_K	2-bit	~2.7 GB	Low	Very fast
Q4_K_M	4-bit	~4.1 GB	Good	Fast
Q5_K_M	5-bit	~4.8 GB	Very good	Medium
Q6_K	6-bit	~5.5 GB	Excellent	Medium
Q8_0	8-bit	~7.2 GB	Best	Slow
FP16	16-bit	~14.0 GB	Original	Slowest

Note: Generally Q4_K_M is recommended as the optimal balance between quality and size. Q5_K_M and above show diminishing quality improvements while significantly increasing memory usage.

Modelfile (custom model definition):

# Modelfile: Create custom model
FROM llama3.1:8b
 
# System prompt
SYSTEM """You are a big data engineering expert at Data Dynamics.
You provide accurate, practical answers about Apache Spark, Kafka, NiFi, Kudu, etc.
Include code examples."""
 
# Parameters
PARAMETER temperature 0.7
PARAMETER top_p 0.9
PARAMETER num_ctx 4096
PARAMETER stop "<|eot_id|>"
 
# Template (optional)
TEMPLATE """{{ if .System }}<|start_header_id|>system<|end_header_id|>
{{ .System }}<|eot_id|>{{ end }}
<|start_header_id|>user<|end_header_id|>
{{ .Prompt }}<|eot_id|>
<|start_header_id|>assistant<|end_header_id|>
"""

# Create and run custom model
ollama create dd-engineer -f Modelfile
ollama run dd-engineer

vLLM: Hugging Face Models and LoRA Adapters

vLLM directly loads models in Hugging Face format (safetensors, PyTorch).

# Run with Hugging Face model directly
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B-Instruct
 
# Use quantized model (AWQ)
python -m vllm.entrypoints.openai.api_server \
    --model TheBloke/Llama-3.1-8B-Instruct-AWQ \
    --quantization awq
 
# GPTQ quantized model
python -m vllm.entrypoints.openai.api_server \
    --model TheBloke/Llama-3.1-8B-Instruct-GPTQ \
    --quantization gptq

Loading LoRA adapters:

# Dynamically load LoRA adapters
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --enable-lora \
    --lora-modules \
        finance-adapter=./adapters/finance \
        legal-adapter=./adapters/legal \
    --max-lora-rank 16

# Call with specific LoRA adapter via API
import openai
 
client = openai.OpenAI(base_url="http://localhost:8000/v1", api_key="dummy")
 
# Use finance domain adapter
response = client.chat.completions.create(
    model="finance-adapter",  # LoRA adapter name
    messages=[{"role": "user", "content": "How to analyze PER valuation?"}]
)
 
# Use legal domain adapter
response = client.chat.completions.create(
    model="legal-adapter",    # Different LoRA adapter
    messages=[{"role": "user", "content": "Review contract termination conditions."}]
)

Supported Model Range Comparison

Model	Ollama	vLLM
LLaMA 3 / 3.1	Yes	Yes
Mistral / Mixtral	Yes	Yes
Gemma / Gemma 2	Yes	Yes
Qwen 2 / 2.5	Yes	Yes
Phi-3 / Phi-4	Yes	Yes
CodeLlama	Yes	Yes
DeepSeek-V2	Yes	Yes
Command R+	Yes	Yes
Custom Fine-Tuned	GGUF conversion needed	Direct Hugging Face format
LoRA adapter hot-swap	No	Yes
Vision models (multimodal)	Yes (LLaVA, etc.)	Yes (LLaVA, etc.)

5. Performance Comparison

Inference Speed (Throughput, Latency)

Single request performance (LLaMA 3.1 8B):

Metric	Ollama (Q4_K_M, GPU)	Ollama (Q4_K_M, CPU)	vLLM (FP16, GPU)
TTFT (Time to First Token)	~100ms	~500ms	~80ms
Token generation speed	~60 tok/s	~15 tok/s	~80 tok/s
Memory usage	~5 GB	~5 GB (RAM)	~17 GB

Note: The speed difference between Ollama and vLLM for single requests is not significant. vLLM's true strength shows in concurrent request handling.

Concurrent Request Handling (Concurrency)

vLLM's Continuous Batching creates an overwhelming performance difference in concurrent request handling.

[Throughput by concurrent requests (LLaMA 3.1 8B, A100 80GB)]

Concurrent     Ollama           vLLM
  1            ~60 tok/s        ~80 tok/s
  4            ~70 tok/s        ~280 tok/s
  8            ~75 tok/s        ~500 tok/s
  16           ~78 tok/s        ~850 tok/s
  32           ~80 tok/s        ~1,200 tok/s
  64           Requests queue   ~1,500 tok/s

Why is there such a difference?

Aspect	Ollama	vLLM
Batching	Sequential per-request (or limited parallel)	Continuous Batching (dynamic batching)
KV Cache	Independent allocation per request	Shared/reused via PagedAttention
GPU utilization	GPU focused on single request	Multiple requests share GPU compute

GPU Memory Utilization Efficiency

Scenario	Ollama	vLLM
7B model load	~5 GB (Q4)	~17 GB (FP16)
7B model load (quantized)	~5 GB (Q4)	~5 GB (AWQ)
10 concurrent KV Cache	~2 GB additional	~1 GB additional (PagedAttention)
100 concurrent KV Cache	Difficult to support	~8 GB additional
Overall efficiency	Strongest in model size savings	Strongest in concurrent memory efficiency

Batch Processing Performance

Offline batch inference scenario processing large numbers of prompts at once:

# vLLM offline batch inference
from vllm import LLM, SamplingParams
 
llm = LLM(model="meta-llama/Llama-3.1-8B-Instruct")
sampling_params = SamplingParams(temperature=0.0, max_tokens=256)
 
# Batch process 1,000 prompts
prompts = [f"Question {i}: ..." for i in range(1000)]
outputs = llm.generate(prompts, sampling_params)
# vLLM: ~2 min (A100), Ollama: ~15 min (same GPU)

Batch Size	Ollama (A100)	vLLM (A100)	vLLM Speedup
100	~90s	~12s	7.5x
1,000	~900s	~120s	7.5x
10,000	~9,000s	~1,100s	8.2x

6. API and Integration

OpenAI Compatible API

Both tools provide OpenAI API-compatible endpoints, allowing existing OpenAI SDK code to work without modification.

Ollama OpenAI compatible API:

from openai import OpenAI
 
# Ollama (port 11434)
client = OpenAI(
    base_url="http://localhost:11434/v1",
    api_key="ollama"  # Any value works
)
 
response = client.chat.completions.create(
    model="llama3.1",
    messages=[{"role": "user", "content": "Hello"}],
    temperature=0.7
)
print(response.choices[0].message.content)

vLLM OpenAI compatible API:

from openai import OpenAI
 
# vLLM (port 8000)
client = OpenAI(
    base_url="http://localhost:8000/v1",
    api_key="vllm"  # Any value works
)
 
response = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": "Hello"}],
    temperature=0.7
)
print(response.choices[0].message.content)

API endpoint comparison:

Endpoint	Ollama	vLLM
`/v1/chat/completions`	Yes	Yes
`/v1/completions`	Yes	Yes
`/v1/embeddings`	Yes	Yes
`/v1/models`	Yes	Yes
`/api/generate` (native)	Yes	No
`/api/chat` (native)	Yes	No
Streaming (SSE)	Yes	Yes

LangChain / LlamaIndex Integration

LangChain + Ollama:

from langchain_ollama import ChatOllama
 
llm = ChatOllama(
    model="llama3.1",
    base_url="http://localhost:11434",
    temperature=0.7
)
 
response = llm.invoke("How to resolve data skew in Spark?")
print(response.content)

LangChain + vLLM:

from langchain_openai import ChatOpenAI
 
# vLLM is OpenAI-compatible, so use ChatOpenAI
llm = ChatOpenAI(
    model="meta-llama/Llama-3.1-8B-Instruct",
    base_url="http://localhost:8000/v1",
    api_key="vllm",
    temperature=0.7
)
 
response = llm.invoke("How to resolve data skew in Spark?")
print(response.content)

LlamaIndex integration:

# Ollama
from llama_index.llms.ollama import Ollama
llm = Ollama(model="llama3.1", base_url="http://localhost:11434")
 
# vLLM (OpenAI compatible)
from llama_index.llms.openai_like import OpenAILike
llm = OpenAILike(
    model="meta-llama/Llama-3.1-8B-Instruct",
    api_base="http://localhost:8000/v1",
    api_key="vllm"
)

Custom Client Implementation

import requests
 
def query_ollama(prompt: str, model: str = "llama3.1"):
    """Call Ollama native API"""
    response = requests.post(
        "http://localhost:11434/api/generate",
        json={
            "model": model,
            "prompt": prompt,
            "stream": False,
            "options": {
                "temperature": 0.7,
                "num_predict": 512
            }
        }
    )
    return response.json()["response"]
 
def query_vllm(prompt: str, model: str = "meta-llama/Llama-3.1-8B-Instruct"):
    """Call vLLM OpenAI-compatible API"""
    response = requests.post(
        "http://localhost:8000/v1/chat/completions",
        json={
            "model": model,
            "messages": [{"role": "user", "content": prompt}],
            "temperature": 0.7,
            "max_tokens": 512
        }
    )
    return response.json()["choices"][0]["message"]["content"]

7. Environment-Specific Usage Guide

Local Development / Prototyping → Ollama

Ollama is optimal when developers experiment with LLMs on local machines or quickly build prototypes.

Suitable scenarios:

RAG pipeline prototype development
Prompt engineering experiments
LLM-based testing in CI/CD pipelines
LLM usage in offline environments
Running LLMs on machines without GPUs

# Dev environment setup (under 5 minutes)
ollama pull llama3.1:8b
ollama pull nomic-embed-text  # Embedding model
 
# Start RAG prototype development
python rag_prototype.py

Production Serving → vLLM

vLLM is optimal when serving LLM responses to many users in actual services.

Suitable scenarios:

Internal AI chatbot services
API-based LLM service delivery
Large-scale batch inference (document summarization, classification, etc.)
Fine-Tuned model serving
Large model serving in multi-GPU environments

# Production deployment (tensor parallel + auto-scaling)
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 4 \
    --gpu-memory-utilization 0.9 \
    --max-model-len 8192 \
    --max-num-seqs 256 \
    --host 0.0.0.0 \
    --port 8000

Edge / On-Premises Deployment

When LLMs must operate only on internal infrastructure without external network access:

Environment	Recommended	Reason
Server without GPU	Ollama	CPU inference support, lightweight with quantization
Single GPU server	Ollama or vLLM	Ollama for few concurrent requests, vLLM for many
Multi-GPU server	vLLM	Tensor parallel for large model execution
Desktop / Laptop	Ollama	Easy installation, macOS Metal acceleration
Kubernetes cluster	vLLM	Horizontal scaling, load balancing

Hybrid Configuration

Strategy for using Ollama and vLLM together:

[Hybrid Architecture]

Developer Local Machine                Production Server
┌──────────────────┐               ┌──────────────────────┐
│    Ollama        │               │       vLLM           │
│  (Prototyping)   │               │  (Production serving) │
│  llama3.1:8b Q4  │               │  LLaMA 3.1 70B FP16 │
│  Port: 11434     │               │  Port: 8000          │
└──────────────────┘               └──────────────────────┘
        ↓                                    ↓
   Dev/Testing                       Production traffic
   Prompt experiments                User services
   RAG prototypes                    Batch inference

Same code, different backends:

import os
from openai import OpenAI
 
# Switch backend via environment variable
if os.getenv("ENV") == "production":
    client = OpenAI(base_url="http://vllm-server:8000/v1", api_key="vllm")
    model = "meta-llama/Llama-3.1-70B-Instruct"
else:
    client = OpenAI(base_url="http://localhost:11434/v1", api_key="ollama")
    model = "llama3.1:8b"
 
# Same API code
response = client.chat.completions.create(
    model=model,
    messages=[{"role": "user", "content": "Hello"}]
)

8. Advanced Features

Ollama: Multimodal, Custom Models, Embeddings

Multimodal (Vision):

# Run vision model
ollama run llava
 
# Query with image
>>> [image path] What do you see in this image?

# Multimodal API call
import ollama
import base64
 
with open("chart.png", "rb") as f:
    image_data = base64.b64encode(f.read()).decode()
 
response = ollama.chat(
    model="llava",
    messages=[{
        "role": "user",
        "content": "Analyze this chart.",
        "images": [image_data]
    }]
)

Embedding generation:

import ollama
 
# Generate embeddings (for RAG)
response = ollama.embeddings(
    model="nomic-embed-text",
    prompt="Apache Spark is a distributed data processing framework."
)
embedding = response["embedding"]  # 768-dimensional vector

Import custom GGUF model:

# Modelfile: Use external GGUF file
FROM ./my-custom-model-Q4_K_M.gguf
 
SYSTEM "You are a professional assistant."
PARAMETER temperature 0.7

ollama create my-model -f Modelfile
ollama run my-model

vLLM: Tensor Parallel, Speculative Decoding, Structured Output

Tensor parallel (Multi-GPU):

# Run 70B model on 4 GPUs
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --tensor-parallel-size 4
 
# Run 405B model on 8 GPUs
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-405B-Instruct \
    --tensor-parallel-size 8 \
    --pipeline-parallel-size 1

Speculative Decoding:

A technique that uses a small draft model to generate multiple tokens ahead, then verifies with the large model to speed up inference.

python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-70B-Instruct \
    --speculative-model meta-llama/Llama-3.1-8B-Instruct \
    --num-speculative-tokens 5

[Standard inference]
Token 1 → Token 2 → Token 3 → Token 4 → Token 5  (5 steps)

[Speculative decoding]
Draft(8B):  Generate tokens 1,2,3,4,5 at once
Target(70B): Verify all 5 tokens in one pass → Accept 3  (1-2 steps)
→ ~2-3x speed improvement

Structured Output:

from openai import OpenAI
 
client = OpenAI(base_url="http://localhost:8000/v1", api_key="vllm")
 
# Force structured response with JSON Schema
response = client.chat.completions.create(
    model="meta-llama/Llama-3.1-8B-Instruct",
    messages=[{"role": "user", "content": "Tell me the weather in Seoul."}],
    extra_body={
        "guided_json": {
            "type": "object",
            "properties": {
                "city": {"type": "string"},
                "temperature": {"type": "number"},
                "condition": {"type": "string", "enum": ["sunny", "cloudy", "rain", "snow"]},
                "humidity": {"type": "number"}
            },
            "required": ["city", "temperature", "condition"]
        }
    }
)
# Response guaranteed to match specified JSON schema

LoRA Adapter Hot-Swap

vLLM's LoRA hot-swap enables serving multiple Fine-Tuned adapters simultaneously on a single base model.

# Load multiple LoRA adapters simultaneously
python -m vllm.entrypoints.openai.api_server \
    --model meta-llama/Llama-3.1-8B-Instruct \
    --enable-lora \
    --lora-modules \
        finance=./lora/finance \
        legal=./lora/legal \
        medical=./lora/medical \
    --max-lora-rank 32 \
    --max-num-seqs 256

[LoRA Hot-Swap Architecture]

                    ┌─ finance adapter ─→ Financial domain responses
Base model (8B) ───┼─ legal adapter ───→ Legal domain responses
                    └─ medical adapter ─→ Medical domain responses

→ 1 base model + N LoRA adapters = GPU memory savings
→ Select adapter per request (via model parameter)

Ollama vs vLLM advanced feature comparison:

Feature	Ollama	vLLM
Multimodal (Vision)	Yes	Yes
Embedding generation	Yes	Yes
Tensor parallel (Multi-GPU)	Limited	Yes (full support)
Pipeline parallel	No	Yes
Speculative decoding	No	Yes
Structured output (JSON)	No	Yes (guided_json)
LoRA hot-swap	No	Yes
Custom model (Modelfile)	Yes	No (Hugging Face format)
Prefix caching	No	Yes
Quantization methods	GGUF (Q4~Q8)	AWQ, GPTQ, FP8, BitsAndBytes

9. Selection Guide: What to Use When

Decision Flowchart

[Do you have a GPU?]
├─ No → Ollama (CPU inference)
└─ Yes
   ↓
[More than 10 concurrent requests?]
├─ Yes → vLLM (Continuous Batching)
└─ No
   ↓
[Is this a production service?]
├─ Yes
│  ↓
│  [Need LoRA hot-swap or structured output?]
│  ├─ Yes → vLLM
│  └─ No
│     ↓
│     [Is SLA (response time guarantee) important?]
│     ├─ Yes → vLLM
│     └─ No → Ollama also works
└─ No
   ↓
[Quick experimentation / prototyping?]
├─ Yes → Ollama
└─ No → Choose based on requirements

Scenario-Based Recommendations

Scenario	Recommendation	Reason
"I want to try LLM on my laptop"	Ollama	One-click install, CPU capable
"I need a quick RAG prototype"	Ollama	Setup in 5 min, easy LangChain integration
"We're launching an internal AI chatbot"	vLLM	Concurrent handling, stable serving
"Batch summarize 10K documents"	vLLM	7-8x faster batch processing
"Serve 3 Fine-Tuned models simultaneously"	vLLM	Efficient serving via LoRA hot-swap
"Run LLM on-premises without GPU"	Ollama	CPU inference support
"Run 70B model on multiple GPUs"	vLLM	Full tensor parallel support
"Run LLM-based tests in CI/CD"	Ollama	Easy Docker, fast start/stop
"Need responses matching JSON schema"	vLLM	Guaranteed structured output via guided_json
"Run on macOS with Apple Silicon"	Ollama	Metal acceleration built-in

Strategy for Using Both Together

In practice, using Ollama and vLLM together across development stages is most effective.

[Usage Across Development Lifecycle]

Stage 1: Experimentation (Ollama)
   - Model exploration: compare llama3.1, mistral, gemma2
   - Prompt testing: find optimal prompts
   - RAG prototype: local vector DB + LLM integration

Stage 2: Development (Ollama)
   - App development: integrate via OpenAI-compatible API
   - Unit testing: use Ollama Docker in CI/CD
   - Fine-Tuning verification: check results

Stage 3: Staging (vLLM)
   - Performance testing: concurrent request load testing
   - Model optimization: quantization, batch size tuning
   - LoRA adapter validation

Stage 4: Production (vLLM)
   - Service deployment: Kubernetes + vLLM
   - Monitoring: Prometheus + Grafana
   - Auto-scaling: automatic scaling based on traffic

Note: Using OpenAI-compatible APIs means switching from Ollama to vLLM only requires changing base_url and model name. The ability to switch between development and production environments without code changes is the biggest benefit of using both tools together.

References

Kwon, W. et al. (2023). "Efficient Memory Management for Large Language Model Serving with PagedAttention." SOSP
Ollama Documentation — https://ollama.com
vLLM Documentation — https://docs.vllm.ai
llama.cpp GitHub — https://github.com/ggerganov/llama.cpp
vLLM GitHub — https://github.com/vllm-project/vllm
Leviathan, Y. et al. (2023). "Fast Inference from Transformers via Speculative Decoding." ICML

— Data Dynamics Engineering Team