vLLM vs Ollama Production Benchmarks: Serving DeepSeek R1 and Llama Models

Engineering teams deploying open-weight foundational models such as DeepSeek R1 and Llama 3 face critical trade-offs between hardware cost, request latency, and token throughput.

While Ollama offers an accessible local execution environment reaching ~62 tokens per second for single users, vLLM’s PagedAttention architecture, continuous batching, and tensor parallelism make it the enterprise standard for high-concurrency cloud environments.

Technical Comparison Matrix

       OLLAMA ARCHITECTURE (Sequential Execution)       VLLM ARCHITECTURE (PagedAttention & Continuous Batching)
 ┌──────────────────────────────────────────────┐     ┌──────────────────────────────────────────────┐
 │ Request 1 ──► [ KV Cache Alloc ] ──► Compute │     │ Request 1 ──┐                                │
 ├──────────────────────────────────────────────┤     │ Request 2 ──┼─► [ Dynamic Paged VRAM Blocks ] │
 │ Request 2 ──► Wait (Sequential Blocked)       │     │ Request 3 ──┘   (Continuous GPU Utilization) │
 └──────────────────────────────────────────────┘     └──────────────────────────────────────────────┘

Performance Benchmarks: DeepSeek R1 70B (NVIDIA H100 SXM 80GB)

Deployment Guidance

1. Use Ollama for local terminal usage, offline coding, and quick offline experiments.
2. Use vLLM on RunPod or Modal for serving web applications, multi-agent frameworks, and high-concurrency API endpoints.