Ollama vs vLLM on Linux: Memory Usage and Token Latency Benchmarked
This deep-dive covers ollama vs vllm benchmark with actionable analysis, practical implications, and expert insights.
Why Benchmark Local LLM Inference Engines?
Developers choosing an open-source inference engine on Linux often debate between Ollama (built on llama.cpp) and vLLM (built on PyTorch with PagedAttention). Both serve OpenAI-compatible endpoints, but their architectures target completely different production constraints.
Test Environment & Methodology
- OS: Ubuntu 24.04 LTS (Kernel 6.8).
- GPU: NVIDIA GeForce RTX 4070 (12GB GDDR6X, Driver 550.120, CUDA 12.4).
- Test Model: Qwen 2.5 7B Instruct (Q4_K_M for Ollama, AWQ 4-bit for vLLM).
- Workload: 50 consecutive requests with a fixed 450-token prompt.
Benchmark Results Comparison Table
| Metric | Ollama (llama.cpp) | vLLM (PagedAttention) | Winner |
|---|---|---|---|
| Idle VRAM Usage | 0 MB (unloads on idle) | 10.8 GB (pre-allocates KV cache) | Ollama |
| Single Stream Latency | 48 tokens/sec | 54 tokens/sec | vLLM |
| Batch Concurrency (8 users) | 62 total tok/sec (queues) | 174 total tok/sec (continuous) | vLLM (2.8x faster) |
| Setup Complexity | 1 command (curl install) | Python venv / Docker / CUDA dependencies | Ollama |
Our Take & Verdict
Choose Ollama: If you are running models on your personal Linux workstation or developer laptop. It frees VRAM automatically when not in use and requires zero configuration.
Choose vLLM: If you are deploying an API backend for multiple team members or production web applications. Its continuous batching handles concurrent requests with superior throughput.
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FAQ
Frequently Asked Questions
Structured for search engines and AI answer systems (AEO/GEO).
Ollama is ideal for local desktop use, development workflows, and single-user CLI tasks due to its zero-configuration setup and low idle memory overhead.
vLLM is engineered for multi-user server production workloads, featuring PagedAttention and continuous batching that deliver higher concurrency throughput.
Yes, provided you do not run them simultaneously on the same GPU to avoid VRAM allocation conflicts.
More answers in our FAQ hub.
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