Compare Ollama and vLLM performance with real benchmarks. Learn when to use each tool, throughput differences, memory usage, and best use cases for local LLM serving.

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A controlled benchmark comparing Ollama and vLLM for local LLM serving in 2026, using an NVIDIA RTX 4090 with Llama 3.1 8B and DeepSeek-R1-Distill-Llama-8B. At single-user concurrency, Ollama (Q4_K_M) delivers ~62 tok/s versus vLLM FP16's ~71 tok/s — a 13% gap partly attributable to quantization differences. Under concurrent load the gap widens dramatically: at 50 users, vLLM achieves ~920 tok/s aggregate versus Ollama's ~155 tok/s, with p99 latency of 2.8s versus 24.7s. The difference stems from vLLM's continuous batching and PagedAttention versus Ollama's FIFO queue and static memory allocation. Ollama wins on setup simplicity, lower VRAM/RAM footprint, and single-user performance; vLLM wins decisively for production multi-user workloads requiring SLA compliance. A practical pattern is using Ollama for local dev and vLLM for staging/production, leveraging their shared OpenAI-compatible API. Full Docker Compose setup and benchmark Python script are included for reproducibility.

Ollama vs vLLM: Performance Benchmark 2026

Throughput Benchmark Results: Requests per Second

Latency Benchmark Results: Time-to-First-Response and P95

Developer Experience and Ecosystem Comparison

When to Use Ollama vs vLLM: Decision Framework