Ollama vs vLLM
type: Article
description: Ollama is a user-friendly local LLM runner optimized for ease of use and consumer hardware, while vLLM is a high-performance inference engine designed for production deployments and maximum throughput on server infrastructure.
language: en-US
date published: 2026-07-07T13:52:30.616Z
date created: 2026-07-07T13:52:30.616Z
date modified: 2026-07-07T13:52:30.683Z
content reference time: 2026-07-07T13:52:30.683Z
author: A Versus B
publisher: A Versus B
about: Ollama
about: vLLM
abstract: Ollama is a user-friendly local LLM runner optimized for ease of use and consumer hardware, while vLLM is a high-performance inference engine designed for production deployments and maximum throughput on server infrastructure.
text: Primary Use Case. Throughput (tokens/sec on RTX 4090). Setup Time (minutes). Memory Overhead (GB on Llama 2 7B). Supported Backends. API Complexity. Batch Processing Support
conclusion text: Choose Ollama if you want to quickly run open-source LLMs on your personal machine with minimal configuration and immediate productivity. Choose vLLM if you're deploying models in production, need maximum inference speed, or require advanced features like batching and request paging for serving multiple users simultaneously.
additional property: Supported Models
  type: PropertyValue
  value: Ollama: 100+ models | vLLM: N/A
additional property: OpenAI API Compatibility
  type: PropertyValue
  value: Ollama: Full native support | vLLM: N/A
additional property: User Interface Type
  type: PropertyValue
  value: Ollama: Command-line (CLI) | vLLM: N/A
additional property: Model Auto-Download
  type: PropertyValue
  value: Ollama: Manual CLI required | vLLM: N/A
additional property: Multi-Platform Support
  type: PropertyValue
  value: Ollama: 3 (macOS, Linux, Windows) | vLLM: N/A
additional property: Latest Release Year
  type: PropertyValue
  value: Ollama: 2024 | vLLM: N/A
additional property: Code Generation Accuracy (HumanEval Benchmark)
  type: PropertyValue
  value: Ollama: 68% (Llama 2 70B) | vLLM: N/A
additional property: Monthly Operating Cost (5,000 token average session)
  type: PropertyValue
  value: Ollama: $0 (hardware only) | vLLM: N/A
additional property: Minimum Hardware RAM Required
  type: PropertyValue
  value: Ollama: 8GB (Llama 2 7B) | vLLM: N/A
additional property: Average Response Latency
  type: PropertyValue
  value: Ollama: 5-10s (CPU) / 2-4s (GPU) | vLLM: N/A
additional property: Supported Programming Languages
  type: PropertyValue
  value: Ollama: 50+ languages | vLLM: N/A
additional property: Data Privacy (0=external servers, 1=local only)
  type: PropertyValue
  value: Ollama: 1 (local) | vLLM: N/A
additional property: Autonomous Code File Editing
  type: PropertyValue
  value: Ollama: No (suggestions only) | vLLM: N/A
additional property: Time to First Response (Small Prompt)
  type: PropertyValue
  value: Ollama: 15-45 sec (CPU), 3-8 sec (GPU) | vLLM: N/A
additional property: Monthly Cost at Heavy Usage
  type: PropertyValue
  value: Ollama: $0 after hardware | vLLM: N/A
additional property: Available Models
  type: PropertyValue
  value: Ollama: 15+ models | vLLM: N/A
additional property: Setup Time
  type: PropertyValue
  value: Ollama: 15-30 (CLI, GPU setup) | vLLM: N/A
additional property: Internet Dependency
  type: PropertyValue
  value: Ollama: Not required after setup | vLLM: N/A
additional property: Minimum RAM Requirement
  type: PropertyValue
  value: Ollama: 4 GB | vLLM: N/A
additional property: IDE Integration
  type: PropertyValue
  value: Ollama: Requires external plugins/API setup | vLLM: N/A
additional property: Data Privacy Level
  type: PropertyValue
  value: Ollama: 100% (on-device) | vLLM: N/A
additional property: Minimum Hardware to Run
  type: PropertyValue
  value: Ollama: 4GB (minimum); 8GB recommended | vLLM: N/A
additional property: Free Tier API Limit
  type: PropertyValue
  value: Ollama: Unlimited (fully free) | vLLM: N/A
additional property: Production API Cost
  type: PropertyValue
  value: Ollama: $0 (fully open-source) | vLLM: N/A
additional property: Privacy Level
  type: PropertyValue
  value: Ollama: 100% local processing | vLLM: N/A
additional property: Community Contributors
  type: PropertyValue
  value: Ollama: 10,000+ GitHub stars, active Discord | vLLM: N/A
additional property: Inference Speed (Llama 2 7B)
  type: PropertyValue
  value: Ollama: 15-50 (GPU-dependent) | vLLM: N/A
additional property: Total Cost of Ownership (12 months, 1M daily tokens)
  type: PropertyValue
  value: Ollama: $0 (hardware amortized) | vLLM: N/A
additional property: Inference Latency (7B model, first token)
  type: PropertyValue
  value: Ollama: 800-1200ms | vLLM: N/A
additional property: Throughput (7B model)
  type: PropertyValue
  value: Ollama: 8-15 | vLLM: N/A
additional property: Minimum Hardware Requirements
  type: PropertyValue
  value: Ollama: 8GB RAM + 4GB GPU (Llama 7B) | vLLM: N/A
additional property: Setup Time to First Inference
  type: PropertyValue
  value: Ollama: 8-10 (including model download) | vLLM: N/A
additional property: Maximum Concurrent Requests
  type: PropertyValue
  value: Ollama: 1-5 (limited by local hardware) | vLLM: 256
  value reference: vLLM
additional property: Supported Quantization Formats
  type: PropertyValue
  value: Ollama: 1 (GGUF) | vLLM: N/A
additional property: Model Inference Speed (Llama 2 7B on RTX 4090)
  type: PropertyValue
  value: Ollama: ~145 tokens/sec | vLLM: N/A
additional property: REST API Support
  type: PropertyValue
  value: Ollama: Yes (native) | vLLM: N/A
additional property: Idle Memory Usage
  type: PropertyValue
  value: Ollama: ~250 MB | vLLM: N/A
additional property: LoRA Fine-tuning
  type: PropertyValue
  value: Ollama: Not supported | vLLM: N/A
additional property: User Interface
  type: PropertyValue
  value: Ollama: Command-line interface | vLLM: N/A
additional property: Graphical User Interface
  type: PropertyValue
  value: Ollama: No (CLI only) | vLLM: N/A
additional property: Model Download Time (7B model)
  type: PropertyValue
  value: Ollama: 3-5 minutes (depends on internet) | vLLM: N/A
additional property: Native REST API Support
  type: PropertyValue
  value: Ollama: Yes (OpenAI-compatible /v1 endpoints) | vLLM: N/A
additional property: Model Merging
  type: PropertyValue
  value: Ollama: Not supported | vLLM: N/A
additional property: GPU Acceleration Options
  type: PropertyValue
  value: Ollama: NVIDIA CUDA, AMD ROCm, Metal (Apple) | vLLM: N/A
additional property: Installation Complexity
  type: PropertyValue
  value: Ollama: Medium (CLI setup required) | vLLM: N/A
additional property: Time to First Token (ms)
  type: PropertyValue
  value: Ollama: 150-300 ms | vLLM: 80-120 ms
  value reference: vLLM
additional property: Throughput (tokens/second, batch size 32)
  type: PropertyValue
  value: Ollama: ~80 tok/s | vLLM: ~1200 tok/s
  value reference: vLLM
additional property: Minimum RAM Required
  type: PropertyValue
  value: Ollama: 4 GB (with offloading) | vLLM: 8 GB
  value reference: Ollama
additional property: GPU Memory for 7B Model
  type: PropertyValue
  value: Ollama: 6-8 GB (fp16) | vLLM: 5-6 GB (with optimization)
  value reference: vLLM
interactivity type: active
significant link: https://www.aversusb.net/entity/ollama
significant link: https://www.aversusb.net/alternatives/ollama
significant link: https://www.aversusb.net/entity/vllm
significant link: https://www.aversusb.net/alternatives/vllm
significant link: https://www.aversusb.net/api/answer/ollama-vs-vllm)
significant link: https://www.aversusb.net/api/v1/schema/ollama-vs-vllm)
related link: https://www.aversusb.net/compare/ollama-vs-vllm
related link: https://www.aversusb.net/compare/aider-vs-ollama
related link: https://www.aversusb.net/compare/continue-vs-ollama
related link: https://www.aversusb.net/compare/hugging-face-vs-ollama
related link: https://www.aversusb.net/compare/ollama-vs-together-ai
related link: https://www.aversusb.net/compare/ollama-vs-lm-studio
related link: https://www.aversusb.net/compare/ollama-vs-jan
related link: https://www.aversusb.net/compare/ollama-vs-openai
teaches: How to choose between Ollama and vLLM
genre: Software

primaryImage
type: ImageObject
width: 1200
height: 630
caption: Ollama vs vLLM — Side-by-side comparison on A Versus B

A Versus B
type: Organization

Ollama
type: SoftwareApplication
language: en-US
description: Lightweight command-line tool for running open-source LLMs locally with OpenAI-compatible API
image: ImageObject
  caption: Ollama
subject of: Ollama vs vLLM
image: ImageObject
  caption: Ollama

vLLM
type: SoftwareApplication
alternate name: Vllm
language: en-US
description: High-throughput LLM inference engine with PagedAttention optimization for rapid deployment across multiple GPU platforms.
subject of: Ollama vs vLLM

Ollama vs vLLM — Structured Comparison Data
type: Dataset
description: Machine-readable comparison dataset for Ollama vs vLLM. Contains 137 attribute comparisons, 5 FAQ pairs.
language: en-US
license: https://creativecommons.org/licenses/by/4.0/
creator: A Versus B
distribution: JSON-LD knowledge graph
  type: DataDownload
  encoding format: application/json+ld
distribution: Raw comparison JSON
  type: DataDownload
  encoding format: application/json
distribution: Structured FAQ pairs JSON
  type: DataDownload
  encoding format: application/json
distribution: AI Answer API (citation-ready)
  type: DataDownload
  encoding format: application/json
distribution: Related comparisons JSON
  type: DataDownload
  encoding format: application/json
variable measured: Supported Models
  type: PropertyValue
  value reference: Features
variable measured: OpenAI API Compatibility
  type: PropertyValue
  value reference: Integration
variable measured: User Interface Type
  type: PropertyValue
  value reference: Usability
variable measured: Model Auto-Download
  type: PropertyValue
  value reference: Features
variable measured: Multi-Platform Support
  type: PropertyValue
  value reference: Compatibility
variable measured: Latest Release Year
  type: PropertyValue
  value reference: Maintenance
variable measured: Code Generation Accuracy (HumanEval Benchmark)
  type: PropertyValue
  value reference: Performance
variable measured: Monthly Operating Cost (5,000 token average session)
  type: PropertyValue
  value reference: Cost
variable measured: Minimum Hardware RAM Required
  type: PropertyValue
  value reference: Hardware
variable measured: Average Response Latency
  type: PropertyValue
  value reference: Speed
variable measured: Supported Programming Languages
  type: PropertyValue
  value reference: Compatibility
variable measured: Data Privacy (0=external servers, 1=local only)
  type: PropertyValue
  value reference: Security
variable measured: Autonomous Code File Editing
  type: PropertyValue
  value reference: Features
variable measured: Time to First Response (Small Prompt)
  type: PropertyValue
  value reference: Performance
variable measured: Monthly Cost at Heavy Usage
  type: PropertyValue
  value reference: Cost
variable measured: Available Models
  type: PropertyValue
  value reference: Features
variable measured: Setup Time
  type: PropertyValue
  value reference: Ease of Use
variable measured: Internet Dependency
  type: PropertyValue
  value reference: Accessibility
variable measured: Minimum RAM Requirement
  type: PropertyValue
  value reference: System Requirements
variable measured: IDE Integration
  type: PropertyValue
  value reference: Development Features
variable measured: Data Privacy Level
  type: PropertyValue
  value reference: Security
variable measured: Minimum Hardware to Run
  type: PropertyValue
  value reference: System Requirements
variable measured: Free Tier API Limit
  type: PropertyValue
  value reference: Pricing & Usage
variable measured: Production API Cost
  type: PropertyValue
  value reference: Pricing & Usage
variable measured: Privacy Level
  type: PropertyValue
  value reference: Security & Privacy
variable measured: Community Contributors
  type: PropertyValue
  value reference: Community
variable measured: Inference Speed (Llama 2 7B)
  type: PropertyValue
  value reference: Performance
variable measured: Total Cost of Ownership (12 months, 1M daily tokens)
  type: PropertyValue
  value reference: Economics
variable measured: Inference Latency (7B model, first token)
  type: PropertyValue
  value reference: Performance
variable measured: Throughput (7B model)
  type: PropertyValue
  value reference: Performance
variable measured: Minimum Hardware Requirements
  type: PropertyValue
  value reference: Requirements
variable measured: Setup Time to First Inference
  type: PropertyValue
  value reference: Developer Experience
variable measured: Maximum Concurrent Requests
  type: PropertyValue
  value reference: Scalability
variable measured: Supported Quantization Formats
  type: PropertyValue
  value reference: Compatibility
variable measured: Model Inference Speed (Llama 2 7B on RTX 4090)
  type: PropertyValue
  value reference: Performance
variable measured: REST API Support
  type: PropertyValue
  value reference: Integration
variable measured: Idle Memory Usage
  type: PropertyValue
  value reference: Resource Efficiency
variable measured: LoRA Fine-tuning
  type: PropertyValue
  value reference: Features
variable measured: User Interface
  type: PropertyValue
  value reference: Usability
variable measured: Graphical User Interface
  type: PropertyValue
  value reference: Usability
variable measured: Model Download Time (7B model)
  type: PropertyValue
  value reference: Performance
variable measured: Native REST API Support
  type: PropertyValue
  value reference: Integration
variable measured: Model Merging
  type: PropertyValue
  value reference: Features
variable measured: GPU Acceleration Options
  type: PropertyValue
  value reference: Performance
variable measured: Installation Complexity
  type: PropertyValue
  value reference: Setup
variable measured: Time to First Token (ms)
  type: PropertyValue
  value reference: Performance
variable measured: Throughput (tokens/second, batch size 32)
  type: PropertyValue
  value reference: Performance
variable measured: Minimum RAM Required
  type: PropertyValue
  value reference: System Requirements
variable measured: GPU Memory for 7B Model
  type: PropertyValue
  value reference: Hardware Requirements
variable measured: Setup Time (from download to first inference)
  type: PropertyValue
  value reference: Usability
variable measured: Pre-packaged Models Available
  type: PropertyValue
  value reference: Model Support
variable measured: GitHub Stars
  type: PropertyValue
  value reference: Community
variable measured: Cost (Monthly Usage Example)
  type: PropertyValue
  value reference: Pricing
variable measured: Model Accuracy (MMLU Benchmark %)
  type: PropertyValue
  value reference: Performance
variable measured: Setup Time (First Use)
  type: PropertyValue
  value reference: Ease of Use
variable measured: Number of Available Models
  type: PropertyValue
  value reference: Features
variable measured: Internet Connectivity Required
  type: PropertyValue
  value reference: Accessibility
variable measured: Multimodal Capabilities (Vision, Image Gen)
  type: PropertyValue
  value reference: Features
variable measured: Installation Size
  type: PropertyValue
  value reference: System Requirements
variable measured: Latest Release Activity
  type: PropertyValue
  value reference: Maintenance
variable measured: CPU Fallback Support
variable measured: Base Cost
variable measured: Average Inference Latency
variable measured: Maximum Throughput
variable measured: Largest Available Model
variable measured: Commercial Support SLA
variable measured: Available Pre-trained Models
variable measured: Initial Setup Time
variable measured: Minimum GPU Memory (7B LLM)
is based on: https://www.aversusb.net/compare/ollama-vs-vllm)

Ollama vs vLLM — Attribute Category Vocabulary
type: DefinedTermSet
language: en-US
description: Taxonomy of attribute categories used to classify comparison data for Ollama vs vLLM.
has defined term: Features
has defined term: Integration
has defined term: Usability
has defined term: Compatibility
has defined term: Maintenance
has defined term: Performance
has defined term: Cost
has defined term: Hardware
has defined term: Speed
has defined term: Security
has defined term: Ease of Use
has defined term: Accessibility
has defined term: System Requirements
has defined term: Development Features
has defined term: Pricing & Usage
has defined term: Security & Privacy
has defined term: Community
has defined term: Economics
has defined term: Requirements
has defined term: Developer Experience
has defined term: Scalability
has defined term: Resource Efficiency
has defined term: Setup
has defined term: Hardware Requirements
has defined term: Model Support
has defined term: Pricing
has defined term: Hardware Compatibility
has defined term: Capability
has defined term: Support
has defined term: Ecosystem
has defined term: Operational Efficiency
has defined term: Privacy
has defined term: Collaboration
has defined term: Installation
has defined term: Adoption
has defined term: Community Adoption
has defined term: Deployment
has defined term: Memory Efficiency
has defined term: Efficiency
has defined term: Enterprise Adoption
has defined term: Reliability
has defined term: Operations
has defined term: Enterprise

Features
type: DefinedTerm
term code: features
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Integration
type: DefinedTerm
term code: integration
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Usability
type: DefinedTerm
term code: usability
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Compatibility
type: DefinedTerm
term code: compatibility
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Maintenance
type: DefinedTerm
term code: maintenance
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Performance
type: DefinedTerm
term code: performance
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Cost
type: DefinedTerm
term code: cost
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Hardware
type: DefinedTerm
term code: hardware
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Speed
type: DefinedTerm
term code: speed
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Security
type: DefinedTerm
term code: security
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Ease of Use
type: DefinedTerm
term code: ease-of-use
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Accessibility
type: DefinedTerm
term code: accessibility
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

System Requirements
type: DefinedTerm
term code: system-requirements
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Development Features
type: DefinedTerm
term code: development-features
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Pricing & Usage
type: DefinedTerm
term code: pricing-&-usage
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Security & Privacy
type: DefinedTerm
term code: security-&-privacy
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Community
type: DefinedTerm
term code: community
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Economics
type: DefinedTerm
term code: economics
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Requirements
type: DefinedTerm
term code: requirements
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Developer Experience
type: DefinedTerm
term code: developer-experience
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Scalability
type: DefinedTerm
term code: scalability
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Resource Efficiency
type: DefinedTerm
term code: resource-efficiency
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Setup
type: DefinedTerm
term code: setup
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Hardware Requirements
type: DefinedTerm
term code: hardware-requirements
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Model Support
type: DefinedTerm
term code: model-support
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Pricing
type: DefinedTerm
term code: pricing
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Hardware Compatibility
type: DefinedTerm
term code: hardware-compatibility
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Capability
type: DefinedTerm
term code: capability
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Support
type: DefinedTerm
term code: support
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Ecosystem
type: DefinedTerm
term code: ecosystem
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Operational Efficiency
type: DefinedTerm
term code: operational-efficiency
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Privacy
type: DefinedTerm
term code: privacy
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Collaboration
type: DefinedTerm
term code: collaboration
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Installation
type: DefinedTerm
term code: installation
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Adoption
type: DefinedTerm
term code: adoption
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Community Adoption
type: DefinedTerm
term code: community-adoption
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Deployment
type: DefinedTerm
term code: deployment
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Memory Efficiency
type: DefinedTerm
term code: memory-efficiency
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Efficiency
type: DefinedTerm
term code: efficiency
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Enterprise Adoption
type: DefinedTerm
term code: enterprise-adoption
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Reliability
type: DefinedTerm
term code: reliability
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Operations
type: DefinedTerm
term code: operations
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

Enterprise
type: DefinedTerm
term code: enterprise
in defined term set: Ollama vs vLLM — Attribute Category Vocabulary

faq
type: FAQPage
language: en-US
is accessible for free: true
conditions of access: Free
date modified: 2026-07-07T13:52:30.683Z
author: A Versus B
publisher: A Versus B
is part of: ollama-vs-vllm)
main entity: Can I use vLLM on my local laptop?
  type: Question
  accepted answer: Answer
    text: Technically yes, but it's not recommended. vLLM requires 6+ GB VRAM minimum and has a complex setup. Ollama is purpose-built for local machines and will be far easier. vLLM is designed for servers with high-end GPUs like RTX A100 or H100 where its performance advantages justify the complexity.
    language: en-US
main entity: Is vLLM faster for every use case?
  type: Question
  accepted answer: Answer
    text: vLLM is significantly faster for throughput-focused scenarios (batch inference, multiple users), but the margin decreases for single-request latency. For simple local experimentation, Ollama's speed is adequate and setup is vastly simpler. vLLM's advantages shine when serving 10+ concurrent users.
    language: en-US
main entity: Can both run open-source models like Llama 2, Mistral, and CodeLlama?
  type: Question
  accepted answer: Answer
    text: Yes, both support the same popular open-source models in GGUF format (Ollama) and transformers format (vLLM). Model availability is equivalent across both platforms. The difference is in how they execute and optimize these models, not which models they can run.
    language: en-US
main entity: Which one should I use for production API serving?
  type: Question
  accepted answer: Answer
    text: vLLM is the industry standard for production. Its 4-5x throughput advantage, native batching, PagedAttention memory optimization, and OpenAI-compatible API are essential for serving multiple users. Ollama can handle light production loads (1-2 concurrent users) but will bottleneck quickly.
    language: en-US
main entity: Does Ollama's quantization hurt quality compared to vLLM's full precision?
  type: Question
  accepted answer: Answer
    text: Ollama defaults to quantized models (GGUF format) which trade 2-5% quality loss for 4-8x smaller file sizes and faster inference on consumer hardware. vLLM typically serves full-precision models but supports quantization too. For most applications, Ollama's quantization quality loss is imperceptible.
    language: en-US

How to Choose Between Ollama and vLLM
type: HowTo
description: Ollama is a user-friendly local LLM runner optimized for ease of use and consumer hardware, while vLLM is a high-performance inference engine designed for production deployments and maximum throughput on server infrastructure.
language: en-US
total time: PT3M
step: Consider: Primary Use Case
step: Consider: Throughput (tokens/sec on RTX 4090)
step: Consider: Setup Time (minutes)
step: Consider: Memory Overhead (GB on Llama 2 7B)
step: Consider: Supported Backends
step: Consider: API Complexity
step: Consider: Batch Processing Support
tool: A Versus B Comparison Guide
tip: HowToTip
  text: Choose Ollama if you want to quickly run open-source LLMs on your personal machine with minimal configuration and immediate productivity. Choose vLLM if you're deploying models in production, need maximum inference speed, or require advanced features like batching and request paging for serving multiple users simultaneously.

Consider: Primary Use Case
type: HowToStep
position: 1
text: Primary Use Case: Ollama has Local desktop/laptop experimentation, while vLLM has Production server inference.

Consider: Throughput (tokens/sec on RTX 4090)
type: HowToStep
position: 2
text: Throughput (tokens/sec on RTX 4090): Ollama has ~150-200 tokens/sec, while vLLM has ~600-800 tokens/sec.

Consider: Setup Time (minutes)
type: HowToStep
position: 3
text: Setup Time (minutes): Ollama has 2-5 minutes, while vLLM has 20-40 minutes.

Consider: Memory Overhead (GB on Llama 2 7B)
type: HowToStep
position: 4
text: Memory Overhead (GB on Llama 2 7B): Ollama has ~8-10 GB, while vLLM has ~6-7 GB.

Consider: Supported Backends
type: HowToStep
position: 5
text: Supported Backends: Ollama has CPU, Metal (Apple), CUDA, Vulkan (4 backends), while vLLM has CUDA, ROCm, CPU, TPU (4+ backends with plugins).

Consider: API Complexity
type: HowToStep
position: 6
text: API Complexity: Ollama has Simple REST API, chat/completion endpoints, while vLLM has Advanced OpenAI-compatible API with detailed control.

Consider: Batch Processing Support
type: HowToStep
position: 7
text: Batch Processing Support: Ollama has Limited (single request at a time), while vLLM has Native support for batching and paging.

claimreview
type: ClaimReview
is accessible for free: true
conditions of access: Free
claim reviewed: Ollama vs vLLM
review body: Ollama is a user-friendly local LLM runner optimized for ease of use and consumer hardware, while vLLM is a high-performance inference engine designed for production deployments and maximum throughput on server infrastructure.
review rating: Rating
  rating: 5
  worst rating: 1
  best rating: 5
  alternate name: Verified Comparison
author: A Versus B
item reviewed: Ollama vs vLLM

Setup Time (from download to first inference)
type: PropertyValue
value: Ollama: 5 minutes | vLLM: 30 minutes
value reference: Ollama

Pre-packaged Models Available
type: PropertyValue
value: Ollama: 20,000+ (registry) | vLLM: Unlimited (HuggingFace)

GitHub Stars
type: PropertyValue
value: Ollama: 100,000+ | vLLM: 23,000+
value reference: Ollama

Cost (Monthly Usage Example)
type: PropertyValue
value: Ollama: $0 (free) | vLLM: N/A

Model Accuracy (MMLU Benchmark %)
type: PropertyValue
value: Ollama: Llama 2 70B: 82.3% | vLLM: N/A

Setup Time (First Use)
type: PropertyValue
value: Ollama: 15-30 minutes (download, install, configure) | vLLM: N/A

Number of Available Models
type: PropertyValue
value: Ollama: 50+ open-source models | vLLM: N/A

Internet Connectivity Required
type: PropertyValue
value: Ollama: Only for initial model download; runs offline after | vLLM: N/A

Multimodal Capabilities (Vision, Image Gen)
type: PropertyValue
value: Ollama: Limited; vision support emerging in some models | vLLM: N/A

Installation Size
type: PropertyValue
value: Ollama: ~150 MB | vLLM: N/A

Latest Release Activity
type: PropertyValue
value: Ollama: Weekly updates (as of 2026) | vLLM: N/A

CPU Fallback Support
type: PropertyValue
value: Ollama: Full support with graceful degradation | vLLM: Limited, requires GPU

Base Cost
type: PropertyValue
value: Ollama: $0 (Free) | vLLM: N/A

Average Inference Latency
type: PropertyValue
value: Ollama: 200-5000ms (hardware dependent) | vLLM: N/A

Maximum Throughput
type: PropertyValue
value: Ollama: 1-10 (single device) | vLLM: N/A

Largest Available Model
type: PropertyValue
value: Ollama: 70B (Llama 2) | vLLM: N/A

Commercial Support SLA
type: PropertyValue
value: Ollama: Community-only (none) | vLLM: N/A

Available Pre-trained Models
type: PropertyValue
value: Ollama: 200+ | vLLM: N/A

Initial Setup Time
type: PropertyValue
value: Ollama: 2-3 minutes | vLLM: N/A

Minimum GPU Memory (7B LLM)
type: PropertyValue
value: Ollama: 4-6GB | vLLM: N/A

Free Tier Request Limit
type: PropertyValue
value: Ollama: Unlimited (local only) | vLLM: N/A

Data Transmission
type: PropertyValue
value: Ollama: No external data transmission (100% offline) | vLLM: N/A

Community Features
type: PropertyValue
value: Ollama: Model registry only, 0 community features | vLLM: N/A

Download Size
type: PropertyValue
value: Ollama: 450 MB | vLLM: N/A

Transformers Library Downloads (weekly)
type: PropertyValue
value: Ollama: Not applicable (CLI tool) | vLLM: N/A

IDE Integration Support
type: PropertyValue
value: Ollama: None (CLI/API only) | vLLM: N/A

LLM Provider Options
type: PropertyValue
value: Ollama: 100+ open-source models (single source) | vLLM: N/A

Minimum Installation Time
type: PropertyValue
value: Ollama: 5-15 minutes (install + model download) | vLLM: N/A

Runtime Memory Usage (Idle)
type: PropertyValue
value: Ollama: 50-200 MB | vLLM: N/A

Privacy Level (0=cloud-only, 100=fully local)
type: PropertyValue
value: Ollama: 100 (always local) | vLLM: N/A

API Documentation Quality
type: PropertyValue
value: Ollama: Extensive REST API documentation | vLLM: N/A

Cost (Base Usage)
type: PropertyValue
value: Ollama: $0 (fully free) | vLLM: N/A

Inference Throughput (RTX 4090, Llama 2 13B)
type: PropertyValue
value: Ollama: ~175 tokens/sec | vLLM: ~700 tokens/sec
value reference: vLLM

Memory Usage (Llama 2 7B quantized)
type: PropertyValue
value: Ollama: ~9 GB | vLLM: ~6.5 GB
value reference: vLLM

Installation Time (from zero)
type: PropertyValue
value: Ollama: 3-5 minutes | vLLM: 25-40 minutes
value reference: Ollama

Minimum VRAM for Llama 2 7B
type: PropertyValue
value: Ollama: 4 GB | vLLM: 6 GB
value reference: Ollama

Number of Supported GPU Backends
type: PropertyValue
value: Ollama: 4 (CPU, Metal, CUDA, Vulkan) | vLLM: 4+ (CUDA, ROCm, CPU, TPU, custom)
value reference: vLLM

Batch Processing Support
type: PropertyValue
value: Ollama: No (sequential only) | vLLM: Yes (native continuous batching)

API Standardization
type: PropertyValue
value: Ollama: Custom REST endpoints | vLLM: OpenAI-compatible API

GitHub Stars (as of 2026)
type: PropertyValue
value: Ollama: ~18,000 | vLLM: ~24,000
value reference: vLLM

Peak Throughput (13B model, V100)
type: PropertyValue
value: Ollama: N/A | vLLM: 2800

Memory Usage (13B model, batch=32)
type: PropertyValue
value: Ollama: N/A | vLLM: 10.5

Time to First Token (p99 latency)
type: PropertyValue
value: Ollama: N/A | vLLM: 45

Setup Time (from install to inference)
type: PropertyValue
value: Ollama: N/A | vLLM: 5

GPU Platform Support Count
type: PropertyValue
value: Ollama: N/A | vLLM: 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.)

Throughput (tokens/second, LLaMA 70B example)
type: PropertyValue
value: Ollama: N/A | vLLM: 1,500+

KV Cache Memory Usage Reduction
type: PropertyValue
value: Ollama: N/A | vLLM: ~4x reduction

Supported ML Frameworks
type: PropertyValue
value: Ollama: N/A | vLLM: Primarily PyTorch/Transformers (limited)

GitHub Stars (community adoption metric)
type: PropertyValue
value: Ollama: N/A | vLLM: 21,000+

Minimum GPU Memory (LLaMA 70B, 1 GPU)
type: PropertyValue
value: Ollama: N/A | vLLM: 40 GB (with PagedAttention)

Multi-Model Serving Setup Complexity
type: PropertyValue
value: Ollama: N/A | vLLM: High (requires separate instances)

Batch Size Improvement (via memory savings)
type: PropertyValue
value: Ollama: N/A | vLLM: 4x larger batches possible

Distributed Parallelism Setup Time
type: PropertyValue
value: Ollama: N/A | vLLM: 15-30 (built-in helpers)

Token Throughput (A100-40GB, 7B model)
type: PropertyValue
value: Ollama: N/A | vLLM: 12,500 tokens/sec

Memory Usage (KV cache, 7B model, batch=1)
type: PropertyValue
value: Ollama: N/A | vLLM: 8.2 GB (with PagedAttention)

Supported Model Frameworks
type: PropertyValue
value: Ollama: N/A | vLLM: 2 (LLM-specific)

P99 Latency (7B model, batch=32)
type: PropertyValue
value: Ollama: N/A | vLLM: 380 ms

Configuration Complexity
type: PropertyValue
value: Ollama: N/A | vLLM: Low (Python API)

Model Ensemble Support
type: PropertyValue
value: Ollama: N/A | vLLM: No native ensemble; requires external orchestration

Production Users (Estimated)
type: PropertyValue
value: Ollama: N/A | vLLM: ~1,200+ organizations (LLM-focused)

Throughput (tokens/sec on A100)
type: PropertyValue
value: Ollama: N/A | vLLM: ~8,000-12,000

Per-Token Latency (Llama 2 70B)
type: PropertyValue
value: Ollama: N/A | vLLM: 50-60ms

Supported GPU Platforms
type: PropertyValue
value: Ollama: N/A | vLLM: NVIDIA, AMD, Intel, CPU (4 platforms)

Pre-optimized Model Count
type: PropertyValue
value: Ollama: N/A | vLLM: 500+ with auto-optimization

Memory Usage Reduction (vs PyTorch)
type: PropertyValue
value: Ollama: N/A | vLLM: 50-60% (Paged Attention)

GitHub Stars (2026)
type: PropertyValue
value: Ollama: N/A | vLLM: 25,000+

Setup Time (basic deployment)
type: PropertyValue
value: Ollama: N/A | vLLM: 5-10 minutes

Cost
type: PropertyValue
value: Ollama: N/A | vLLM: Free (open-source)

Inference Throughput (single A100 GPU)
type: PropertyValue
value: Ollama: N/A | vLLM: 25,000 tokens/sec

Setup Time (basic inference)
type: PropertyValue
value: Ollama: N/A | vLLM: 120-420 minutes (2-7 days with infrastructure)

Cost per Million Tokens (A100, on-demand)
type: PropertyValue
value: Ollama: N/A | vLLM: $0.12

Supported Models (major open-source)
type: PropertyValue
value: Ollama: N/A | vLLM: 1,000+ models

Training Capabilities
type: PropertyValue
value: Ollama: N/A | vLLM: Inference-only, no native training

Enterprise SLA Uptime
type: PropertyValue
value: Ollama: N/A | vLLM: Community-dependent (typically 99.0%+)

Infrastructure Management
type: PropertyValue
value: Ollama: N/A | vLLM: User-managed (CUDA, Docker, scaling)

Community & Documentation
type: PropertyValue
value: Ollama: N/A | vLLM: 25,000+ stars, weekly updates

LLM Throughput Improvement
type: PropertyValue
value: Ollama: N/A | vLLM: 24x

Memory Usage (KV Cache)
type: PropertyValue
value: Ollama: N/A | vLLM: 80% reduction

Enterprise Deployment Features
type: PropertyValue
value: Ollama: N/A | vLLM: 3 (basic)

Multi-GPU Support
type: PropertyValue
value: Ollama: N/A | vLLM: Native (tensor parallelism)

Production Monitoring
type: PropertyValue
value: Ollama: N/A | vLLM: Basic (throughput, latency)

GPU Memory Reduction vs Baseline
type: PropertyValue
value: Ollama: N/A | vLLM: ~60%

Throughput Improvement (Batching)
type: PropertyValue
value: Ollama: N/A | vLLM: 10-23x vs standard

Supported Model Formats
type: PropertyValue
value: Ollama: N/A | vLLM: 15+ formats (HF, GGUF, AWQ, GPTQ, etc)

Time to Deploy (Minutes)
type: PropertyValue
value: Ollama: N/A | vLLM: 5-10 minutes

Token Streaming Native Support
type: PropertyValue
value: Ollama: N/A | vLLM: Via API wrapper

Official Enterprise Support
type: PropertyValue
value: Ollama: N/A | vLLM: Community-based

Latest Version Release Cycle
type: PropertyValue
value: Ollama: N/A | vLLM: 2-3 weeks

SpeakableSpecification
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Ollama — Wikipedia
type: CreativeWork

vLLM — Wikipedia
type: CreativeWork

Compare Ollama vs vLLM
type: Action
additional type: CompareAction
target: https://www.aversusb.net/compare/ollama-vs-vllm)
object: Ollama
object: vLLM

CPU Fallback Support
type: PropertyValue
value reference: Hardware Compatibility

Base Cost
type: PropertyValue
value reference: Pricing

Average Inference Latency
type: PropertyValue
value reference: Performance

Maximum Throughput
type: PropertyValue
value reference: Scalability

Largest Available Model
type: PropertyValue
value reference: Capability

Commercial Support SLA
type: PropertyValue
value reference: Support

Available Pre-trained Models
type: PropertyValue
value reference: Ecosystem

Initial Setup Time
type: PropertyValue
value reference: Operational Efficiency

Minimum GPU Memory (7B LLM)
type: PropertyValue
value reference: Hardware Requirements

Free Tier Request Limit
type: PropertyValue
value reference: Pricing

Data Transmission
type: PropertyValue
value reference: Privacy

Community Features
type: PropertyValue
value reference: Collaboration

Download Size
type: PropertyValue
value reference: Installation

Transformers Library Downloads (weekly)
type: PropertyValue
value reference: Adoption

IDE Integration Support
type: PropertyValue
value reference: Integration

LLM Provider Options
type: PropertyValue
value reference: Features

Minimum Installation Time
type: PropertyValue
value reference: Setup

Runtime Memory Usage (Idle)
type: PropertyValue
value reference: Performance

Privacy Level (0=cloud-only, 100=fully local)
type: PropertyValue
value reference: Security

API Documentation Quality
type: PropertyValue
value reference: Developer Experience

Cost (Base Usage)
type: PropertyValue
value reference: Pricing

Inference Throughput (RTX 4090, Llama 2 13B)
type: PropertyValue
value reference: Performance

Memory Usage (Llama 2 7B quantized)
type: PropertyValue
value reference: Resource Efficiency

Installation Time (from zero)
type: PropertyValue
value reference: Ease of Use

Minimum VRAM for Llama 2 7B
type: PropertyValue
value reference: Hardware Requirements

Number of Supported GPU Backends
type: PropertyValue
value reference: Compatibility

Batch Processing Support
type: PropertyValue
value reference: Features

API Standardization
type: PropertyValue
value reference: Integration

GitHub Stars (as of 2026)
type: PropertyValue
value reference: Community Adoption

Peak Throughput (13B model, V100)
type: PropertyValue
value reference: Performance

Memory Usage (13B model, batch=32)
type: PropertyValue
value reference: Resource Efficiency

Time to First Token (p99 latency)
type: PropertyValue
value reference: Performance

Setup Time (from install to inference)
type: PropertyValue
value reference: Deployment

GPU Platform Support Count
type: PropertyValue
value reference: Compatibility

Throughput (tokens/second, LLaMA 70B example)
type: PropertyValue
value reference: Performance

KV Cache Memory Usage Reduction
type: PropertyValue
value reference: Memory Efficiency

Supported ML Frameworks
type: PropertyValue
value reference: Compatibility

GitHub Stars (community adoption metric)
type: PropertyValue
value reference: Community

Minimum GPU Memory (LLaMA 70B, 1 GPU)
type: PropertyValue
value reference: Hardware Requirements

Multi-Model Serving Setup Complexity
type: PropertyValue
value reference: Ease of Use

Batch Size Improvement (via memory savings)
type: PropertyValue
value reference: Scalability

Distributed Parallelism Setup Time
type: PropertyValue
value reference: Developer Experience

Token Throughput (A100-40GB, 7B model)
type: PropertyValue
value reference: Performance

Memory Usage (KV cache, 7B model, batch=1)
type: PropertyValue
value reference: Efficiency

Supported Model Frameworks
type: PropertyValue
value reference: Compatibility

P99 Latency (7B model, batch=32)
type: PropertyValue
value reference: Performance

Configuration Complexity
type: PropertyValue
value reference: Usability

Model Ensemble Support
type: PropertyValue
value reference: Features

Production Users (Estimated)
type: PropertyValue
value reference: Enterprise Adoption

Throughput (tokens/sec on A100)
type: PropertyValue
value reference: Performance

Per-Token Latency (Llama 2 70B)
type: PropertyValue
value reference: Performance

Supported GPU Platforms
type: PropertyValue
value reference: Compatibility

Pre-optimized Model Count
type: PropertyValue
value reference: Model Support

Memory Usage Reduction (vs PyTorch)
type: PropertyValue
value reference: Efficiency

GitHub Stars (2026)
type: PropertyValue
value reference: Community

Setup Time (basic deployment)
type: PropertyValue
value reference: Ease of Use

Cost
type: PropertyValue
value reference: Economics

Inference Throughput (single A100 GPU)
type: PropertyValue
value reference: Performance

Setup Time (basic inference)
type: PropertyValue
value reference: Ease of Use

Cost per Million Tokens (A100, on-demand)
type: PropertyValue
value reference: Cost

Supported Models (major open-source)
type: PropertyValue
value reference: Ecosystem

Training Capabilities
type: PropertyValue
value reference: Features

Enterprise SLA Uptime
type: PropertyValue
value reference: Reliability

Infrastructure Management
type: PropertyValue
value reference: Operations

Community & Documentation
type: PropertyValue
value reference: Support

LLM Throughput Improvement
type: PropertyValue
value reference: Performance

Memory Usage (KV Cache)
type: PropertyValue
value reference: Performance

Enterprise Deployment Features
type: PropertyValue
value reference: Enterprise

Multi-GPU Support
type: PropertyValue
value reference: Scalability

Production Monitoring
type: PropertyValue
value reference: Operations

GPU Memory Reduction vs Baseline
type: PropertyValue
value reference: Performance

Throughput Improvement (Batching)
type: PropertyValue
value reference: Performance

Supported Model Formats
type: PropertyValue
value reference: Compatibility

Time to Deploy (Minutes)
type: PropertyValue
value reference: Ease of Use

Token Streaming Native Support
type: PropertyValue
value reference: Features

Official Enterprise Support
type: PropertyValue
value reference: Support

Latest Version Release Cycle
type: PropertyValue
value reference: Maintenance