Ollama vs vLLM

The short version

Ollama for simplicity and single-user / low-concurrency workloads. vLLM for production multi-user inference where throughput matters. If you're unsure which one fits, start with Ollama — it's faster to deploy and the delta only matters once you're serving real concurrent load.

What each one is

Ollama

A thin, operationally simple wrapper around llama.cpp with an OpenAI-compatible API, built-in model management (pull, list, run, delete), and a default installation that Just Works on macOS, Linux, and Windows. Optimizes for developer experience more than throughput.

vLLM

Production-grade inference server with continuous batching, PagedAttention memory management, tensor-parallel execution across multiple GPUs, and hard-won optimizations aimed at throughput and latency at scale. Optimizes for serving performance, not for beginner friendliness.

When to choose Ollama

• Single-user or developer workloads — local development, individual researcher use, small-team internal tools
• Mac and Apple Silicon deployments — Ollama's Metal backend is excellent; vLLM's Apple support lags
• Low concurrency — up to roughly 10–20 concurrent users, Ollama is operationally simpler with acceptable throughput
• Fast iteration — model swaps are one command; trying new open-weight releases is trivial
• Quantized-model workloads — 4-bit and 8-bit GGUF models run well
• Edge or embedded deployments — smaller memory footprint, CPU-capable fallback

When to choose vLLM

• Production multi-user inference — dozens to thousands of concurrent users. Continuous batching is the single biggest throughput advantage here.
• High-throughput batch workloads — document-ingestion pipelines, large-scale evaluation runs, agentic systems with many parallel calls
• Multi-GPU serving — tensor parallelism across 2, 4, or 8 GPUs on one machine; pipeline parallelism across nodes
• Enterprise data-center deployments on NVIDIA hardware (A100, H100, H200) where the infrastructure investment justifies the setup complexity
• Latency-sensitive use cases where PagedAttention's memory efficiency produces measurably lower p99 latency

Throughput: what the delta actually looks like

On a single NVIDIA GPU serving a 13B model, vLLM typically sustains 3–5× the tokens-per-second throughput of Ollama under concurrent load — the advantage comes almost entirely from continuous batching (serving the next request's prefill while the previous request is still decoding). For single-user interactive workloads, the difference is often invisible because the bottleneck is the user typing, not the model.

On multi-GPU setups (4× A100), vLLM's tensor parallelism unlocks throughput that Ollama can't match at all — vLLM shards the model across GPUs, Ollama largely doesn't. This is the regime where "use vLLM" isn't a preference, it's a requirement.

Operational tradeoffs

Ops complexity

Ollama: one binary, one command, works. vLLM: Python environment, CUDA compatibility matrix, more moving parts, more failure modes to learn. A small team can run Ollama in production with confidence; running vLLM in production benefits from someone who owns "the inference service" as a non-trivial part of their job.

Model compatibility

Ollama: uses GGUF format, broad support for quantized open-weight models, easy model pulls from the Ollama registry. vLLM: uses Hugging Face model format natively, broad support for non-quantized models, some lag on brand-new model architectures (though this gap has narrowed materially).

Observability

Ollama: basic logs, OpenAI-compatible request/response. vLLM: Prometheus metrics native, richer instrumentation, production monitoring out of the box.

Cold start

Ollama: fast — models load quickly from disk, unload when idle, re-load on demand. vLLM: slower cold start but optimized for staying warm.

What we default to in client deployments

Small deployments (under ~20 concurrent users), Apple Silicon environments, rapid-iteration POCs: Ollama. Production enterprise deployments with real multi-user load on NVIDIA hardware: vLLM. Hybrid environments sometimes run both — Ollama for dev/staging, vLLM for prod — to keep iteration speed high without sacrificing production throughput.

In discovery we benchmark both against your actual workload before picking. No ideological preference — the right tool depends on your concurrency, latency targets, hardware, and operational capacity. See local AI deployment.

Other engines worth knowing

• llama.cpp — the foundation under Ollama. Run directly when you need CPU-only deployment, edge devices, or extreme quantization.
• TensorRT-LLM — NVIDIA's optimized engine. Highest raw performance on Ampere/Hopper hardware; setup complexity is higher than vLLM.
• MLX (Apple) — Apple's native ML framework; best performance on Apple Silicon; tooling is catching up fast.
• TGI (Text Generation Inference) — Hugging Face's server; good middle ground between Ollama simplicity and vLLM throughput.
• SGLang — newer engine with strong performance on structured-output workloads; watch space.