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.
Ollama vs vLLM — Side-by-side comparison on A Versus B
Lightweight command-line tool for running open-source LLMs locally with OpenAI-compatible API
High-throughput LLM inference engine with PagedAttention optimization for rapid deployment across multiple GPU platforms.
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
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.
Ollama: 3 (macOS, Linux, Windows) | vLLM: N/A
Code Generation Accuracy (HumanEval Benchmark)
Monthly Operating Cost (5,000 token average session)
Ollama: 5-10s (CPU) / 2-4s (GPU) | vLLM: N/A
Data Privacy (0=external servers, 1=local only)
Ollama: No (suggestions only) | vLLM: N/A
Ollama: 15-45 sec (CPU), 3-8 sec (GPU) | vLLM: N/A
Ollama: 15-30 (CLI, GPU setup) | vLLM: N/A
Ollama: Not required after setup | vLLM: N/A
Ollama: Requires external plugins/API setup | vLLM: N/A
Ollama: 4GB (minimum); 8GB recommended | vLLM: N/A
Ollama: Unlimited (fully free) | vLLM: N/A
Ollama: $0 (fully open-source) | vLLM: N/A
Ollama: 100% local processing | vLLM: N/A
Ollama: 10,000+ GitHub stars, active Discord | vLLM: N/A
Ollama: 15-50 (GPU-dependent) | vLLM: N/A
Total Cost of Ownership (12 months, 1M daily tokens)
Ollama: $0 (hardware amortized) | vLLM: N/A
Inference Latency (7B model, first token)
Ollama: 8GB RAM + 4GB GPU (Llama 7B) | vLLM: N/A
Ollama: 8-10 (including model download) | vLLM: N/A
Ollama: 1-5 (limited by local hardware) | vLLM: 256
Model Inference Speed (Llama 2 7B on RTX 4090)
Ollama: Command-line interface | vLLM: N/A
Ollama: 3-5 minutes (depends on internet) | vLLM: N/A
Ollama: Yes (OpenAI-compatible /v1 endpoints) | vLLM: N/A
Ollama: NVIDIA CUDA, AMD ROCm, Metal (Apple) | vLLM: N/A
Ollama: Medium (CLI setup required) | vLLM: N/A
Throughput (tokens/second, batch size 32)
Ollama: 4 GB (with offloading) | vLLM: 8 GB
Ollama: 6-8 GB (fp16) | vLLM: 5-6 GB (with optimization)
Setup Time (from download to first inference)
Ollama: 20,000+ (registry) | vLLM: Unlimited (HuggingFace)
Ollama: 15-30 minutes (download, install, configure) | vLLM: N/A
Ollama: 50+ open-source models | vLLM: N/A
Ollama: Only for initial model download; runs offline after | vLLM: N/A
Multimodal Capabilities (Vision, Image Gen)
Ollama: Limited; vision support emerging in some models | vLLM: N/A
Ollama: Weekly updates (as of 2026) | vLLM: N/A
Ollama: Full support with graceful degradation | vLLM: Limited, requires GPU
Ollama: 200-5000ms (hardware dependent) | vLLM: N/A
Ollama: 1-10 (single device) | vLLM: N/A
Ollama: Community-only (none) | vLLM: N/A
Ollama: Unlimited (local only) | vLLM: N/A
Ollama: No external data transmission (100% offline) | vLLM: N/A
Ollama: Model registry only, 0 community features | vLLM: N/A
Ollama: Not applicable (CLI tool) | vLLM: N/A
Ollama: 100+ open-source models (single source) | vLLM: N/A
Ollama: 5-15 minutes (install + model download) | vLLM: N/A
Privacy Level (0=cloud-only, 100=fully local)
Ollama: Extensive REST API documentation | vLLM: N/A
Inference Throughput (RTX 4090, Llama 2 13B)
Ollama: ~175 tokens/sec | vLLM: ~700 tokens/sec
Ollama: 3-5 minutes | vLLM: 25-40 minutes
Ollama: 4 (CPU, Metal, CUDA, Vulkan) | vLLM: 4+ (CUDA, ROCm, CPU, TPU, custom)
Ollama: No (sequential only) | vLLM: Yes (native continuous batching)
Ollama: Custom REST endpoints | vLLM: OpenAI-compatible API
Ollama: N/A | vLLM: 7 (NVIDIA, AMD, Intel, Trainium, Gaudi, etc.)
Throughput (tokens/second, LLaMA 70B example)
Ollama: N/A | vLLM: Primarily PyTorch/Transformers (limited)
GitHub Stars (community adoption metric)
Ollama: N/A | vLLM: 40 GB (with PagedAttention)
Ollama: N/A | vLLM: High (requires separate instances)
Batch Size Improvement (via memory savings)
Ollama: N/A | vLLM: 4x larger batches possible
Ollama: N/A | vLLM: 15-30 (built-in helpers)
Memory Usage (KV cache, 7B model, batch=1)
Ollama: N/A | vLLM: 8.2 GB (with PagedAttention)
Ollama: N/A | vLLM: No native ensemble; requires external orchestration
Ollama: N/A | vLLM: ~1,200+ organizations (LLM-focused)
Ollama: N/A | vLLM: NVIDIA, AMD, Intel, CPU (4 platforms)
Ollama: N/A | vLLM: 500+ with auto-optimization
Ollama: N/A | vLLM: 50-60% (Paged Attention)
Ollama: N/A | vLLM: 120-420 minutes (2-7 days with infrastructure)
Cost per Million Tokens (A100, on-demand)
Ollama: N/A | vLLM: Inference-only, no native training
Ollama: N/A | vLLM: Community-dependent (typically 99.0%+)
Ollama: N/A | vLLM: User-managed (CUDA, Docker, scaling)
Ollama: N/A | vLLM: 25,000+ stars, weekly updates
Ollama: N/A | vLLM: Native (tensor parallelism)
Ollama: N/A | vLLM: Basic (throughput, latency)
Ollama: N/A | vLLM: 15+ formats (HF, GGUF, AWQ, GPTQ, etc)
Ollama vs vLLM — Structured Comparison Data
Machine-readable comparison dataset for Ollama vs vLLM. Contains 137 attribute comparisons, 5 FAQ pairs.
Ollama vs vLLM — Attribute Category Vocabulary
Taxonomy of attribute categories used to classify comparison data for Ollama vs vLLM.
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.
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.
Can both run open-source models like Llama 2, Mistral, and CodeLlama?
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.
Which one should I use for production API serving?
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.
Does Ollama's quantization hurt quality compared to vLLM's full precision?
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.
Primary Use Case: Ollama has Local desktop/laptop experimentation, while vLLM has Production server inference.
Consider: Throughput (tokens/sec on RTX 4090)
Throughput (tokens/sec on RTX 4090): Ollama has ~150-200 tokens/sec, while vLLM has ~600-800 tokens/sec.
Setup Time (minutes): Ollama has 2-5 minutes, while vLLM has 20-40 minutes.
Consider: Memory Overhead (GB on Llama 2 7B)
Memory Overhead (GB on Llama 2 7B): Ollama has ~8-10 GB, while vLLM has ~6-7 GB.
Supported Backends: Ollama has CPU, Metal (Apple), CUDA, Vulkan (4 backends), while vLLM has CUDA, ROCm, CPU, TPU (4+ backends with plugins).
API Complexity: Ollama has Simple REST API, chat/completion endpoints, while vLLM has Advanced OpenAI-compatible API with detailed control.
Batch Processing Support: Ollama has Limited (single request at a time), while vLLM has Native support for batching and paging.