Ollama vs vLLM: Choosing a Self-Hosted LLM Inference Server (2026)
Ollama and vLLM both let you run open-weight LLMs on your own hardware without sending prompts to OpenAI or Anthropic. But they are built for different points on the scale curve — Ollama for developers and small teams, vLLM for production multi-user inference workloads where throughput matters.
TL;DR
| Ollama | vLLM | |
|---|---|---|
| License | MIT | Apache-2.0 |
| GitHub stars | ~170,000 | ~50,000 |
| Language | Go (binary) | Python |
| Install complexity | Single binary (curl | sh) | Python environment + CUDA dependencies |
| CPU support | ✓ (slow but functional) | ✗ (GPU only, CUDA required) |
| GPU support | ✓ NVIDIA + AMD (ROCm) + Apple Silicon | ✓ NVIDIA CUDA (primary), AMD experimental |
| OpenAI-compatible API | ✓ | ✓ |
| Model library | ✓ (ollama pull llama3) | Manual GGUF/safetensors download |
| Idle RAM (no model loaded) | ~100 MB | ~500 MB (Python runtime) |
| Throughput (multi-user) | Moderate | 3–5× higher on same GPU |
| Best for | Dev, prototyping, small teams, CPU inference | Production serving, 10+ concurrent users, max tokens/sec |
How each server works
Ollama: simplicity first
Ollama is a single Go binary that bundles model management, a model library, a local inference runtime (llama.cpp under the hood), and an OpenAI-compatible REST API. You install it with one command, pull a model by name, and query it immediately.
curl -fsSL https://ollama.com/install.sh | sh
ollama pull llama3.1:8b
ollama run llama3.1:8b "Summarize this in one sentence: ..."
The model library covers all major open-weight families (Llama, Mistral, Gemma, Qwen, Phi, DeepSeek) in multiple quantization levels. Ollama handles quantized GGUF format and selects the right compute backend (CPU, CUDA, Metal) automatically.
Limitations: Ollama processes one request at a time by default (configurable parallel slots, but not continuous batching). At 10+ concurrent users, queuing becomes the bottleneck — requests wait for the previous one to finish.
vLLM: throughput engineering
vLLM is a Python inference server built around two key innovations: PagedAttention (efficient KV-cache management that eliminates memory fragmentation) and continuous batching (new requests are inserted into active inference batches rather than waiting for a full batch to complete). These combine to deliver 3–5× higher throughput than naive inference on the same GPU.
pip install vllm
python -m vllm.entrypoints.openai.api_server \
--model meta-llama/Llama-3.1-8B-Instruct \
--tensor-parallel-size 1
vLLM serves an OpenAI-compatible API, so any client that works with Ollama's /v1/chat/completions endpoint also works with vLLM.
Limitations: vLLM requires CUDA (NVIDIA GPU). CPU inference is not supported. Installation involves Python, CUDA, and PyTorch dependencies — more surface area than a single binary. Model management is manual (download from Hugging Face; no pull command).
Throughput comparison
On a single NVIDIA A100 80GB serving Llama 3.1 8B Instruct:
| Ollama (1 parallel slot) | vLLM (continuous batching) | |
|---|---|---|
| Single-user tokens/sec | ~80 | ~90 |
| 10 concurrent users (tokens/sec total) | ~80 (queued, not parallel) | ~600–900 |
| 50 concurrent users (tokens/sec total) | ~80 (heavy queuing) | ~1,500–2,500 |
The throughput gap is negligible for a single user. It becomes decisive at 10+ concurrent users — which is when vLLM's continuous batching and PagedAttention start eliminating idle GPU time between requests.
Numbers above are illustrative. Actual throughput depends on model size, quantization, sequence length, and hardware. Run your own benchmarks with vllm bench and ollama bench before committing.
Model management
Ollama has a hosted model library at ollama.com/library. Pulling a model is one command:
ollama pull mistral:7b-instruct-q4_K_M
Ollama manages model storage, VRAM allocation, and model swapping (it unloads one model when another is loaded on single-GPU setups).
vLLM loads models from Hugging Face Hub or a local path. You specify the model at server startup — there is no runtime model switching without restarting the server. Multi-model serving requires multiple server processes or a router (LiteLLM is a common choice for routing across multiple vLLM backends).
Which should you choose?
Choose Ollama if:
- You are a developer prototyping an LLM-powered feature locally
- You have a small team (under 10 concurrent users)
- You want CPU inference fallback (no GPU available)
- You want simple model management with a pull-by-name library
- You value zero-dependency installation
Choose vLLM if:
- You are serving 10+ concurrent users in production
- Throughput (tokens per second per dollar) is the primary metric
- You have NVIDIA CUDA hardware
- You are running inference as an internal API for multiple teams or applications
- You need production-grade features: tensor parallelism, speculative decoding, structured output, and async batching
Both have OpenAI-compatible APIs, so you can prototype with Ollama and migrate to vLLM for production without changing your application code — just swap the base_url.
Further reading
- Self-Host Ollama on a VPS — Docker Compose setup, model management, and Open WebUI integration
Yes, for small-scale production (under 10 concurrent users and non-latency-critical workloads). Many teams use Ollama in production for internal tools, RAG pipelines, and low-traffic APIs. The caveat is that Ollama's default single-slot inference means requests queue rather than batch — at higher concurrency, response latency will increase proportionally. vLLM is the better choice when throughput and latency SLAs matter.
vLLM primarily targets NVIDIA CUDA. AMD GPU support via ROCm is experimental and not recommended for production as of 2026. Apple Silicon (Metal) is not supported — use Ollama for Mac-native inference. If your hardware is AMD or Apple, Ollama is the practical choice today.
Llama 3.1 70B at BF16 precision requires approximately 140 GB VRAM — typically two or four A100 80GB or H100 GPUs with tensor parallelism. At 4-bit quantization (GPTQ or AWQ), it fits in a single 48 GB GPU (like an L40S). For development and moderate traffic, the quantized path on a Liquid Web L40S GPU server is the most cost-effective option.