When should I use Ollama vs vLLM vs SGLang?

Ollama: local development and prototyping on a laptop — simplest setup, runs fully offline. vLLM: serving many concurrent users with different prompts — continuous batching and PagedAttention maximize GPU throughput. SGLang: AI agents, multi-turn chats, and structured JSON outputs — RadixAttention reuses shared prompt prefixes instead of recomputing them.

What is PagedAttention?

vLLM's core technique: it stores the KV cache (the model's memory of processed tokens) in small pages like an OS manages RAM, instead of one fixed block per request. This eliminates 60-80% memory waste and lets far more requests share the GPU.

What is RadixAttention?

SGLang's core technique: a radix tree that caches shared prompt prefixes (system prompts, tool definitions, chat history) across requests, so repeated beginnings are reused rather than recomputed — a large win for agents and multi-turn chat.

Which inference engine is best for data privacy?

Ollama runs fully offline on your own machine — prompts never leave it. vLLM and SGLang are typically deployed as servers, so prompts travel to the server; keep them inside your own network or VPC for sensitive data.

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LLM INFRA — ENGINE PICKER

Vibe coding stack picker: Ollama vs vLLM vs SGLang — which local LLM runtime fits?

Ingenious AI publishes 21 live security platforms, and 100% of them run on permanent getingeniousai.com domains.

All three run the same open models. Pick wrong and you'll either waste an expensive GPU or watch requests queue behind each other. Five questions to match the engine to your workload — plus what each choice means for where your data goes.

Short answer: just you on a laptop → Ollama. Many users asking different things → vLLM. Agents, long chats, or JSON outputs with repeated prompts → SGLang. The quiz below makes it precise.

The 60-second vibe coding runtime picker

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The three engines, side by side

Ollama — the local one

You → OpenAI-compatible API → FIFO request queue → quantized GGUF model (via llama.cpp) → response. One command pulls a compressed model that runs fully offline. Best for: local dev, prototyping, laptops & Macs.

vLLM — the throughput one

Many concurrent users → continuous batching (new requests join the running batch every step) → PagedAttention (KV cache in pages, prefix caching available) → streamed responses. Best for: high-traffic API serving, maximum GPU utilization.

SGLang — the agent one

Agents & multi-turn chats → cache-aware router (routes each request to the worker holding its prefix) → RadixAttention (radix tree reuses shared prefixes) → strong structured outputs and constrained decoding. Best for: AI agents & tool loops, multi-turn chat & RAG, structured JSON.

	Ollama	vLLM	SGLang
Concurrency model	FIFO queue (1 request per model at a time)	Continuous batching	Continuous batching + prefix-aware routing
Memory trick	GGUF quantization (fits on laptop)	PagedAttention	RadixAttention (prefix reuse)
Shines when	One user, offline	Many users, unique prompts	Prompts share long prefixes
Structured outputs	Basic	Good	Excellent (constrained decoding)

What do benchmarks actually show?

Single stream (Qwen 2.5 Coder 32B, dual RTX 4090): Ollama 44.2 tok/s, vLLM 58.7, SGLang 63.4 — close enough that simplicity wins for one user.
32 concurrent clients: Ollama crashed (OOM); vLLM 412.8 tok/s; SGLang 461.5 tok/s — roughly 8x the throughput of single-stream via continuous batching.
Prefix-heavy workloads (shared 500-token system prompts): SGLang ~4.6x faster than vLLM in one 16-request test, because vLLM recomputes the shared prefix every time.

Sources: dev.to multi-GPU benchmark (Oct 2026) · dev.to concurrency benchmark · Substack 16-request test. Numbers vary by model, hardware, and workload — benchmark your own job before committing.

The security trade-off nobody puts in the diagram

Ollama: data never leaves the machine. Fully offline inference is the strongest data-residency posture available — no prompt, document, or embedding crosses a network boundary. For regulated or sensitive data on a single machine, this is the answer.
vLLM / SGLang: prompts travel to the server. Keep them inside your own VPC or network for sensitive workloads; a public endpoint is a new attack surface (prompt injection at scale, log exposure, tenant isolation).
Constrained decoding cuts injection risk. SGLang's structured-output enforcement (JSON schemas, regex grammars) narrows what a model can emit — a real, underused defense against prompt-injection exfiltration in agent tool loops.
KV-cache sharing leaks across requests in theory. Prefix caching reuses computation between requests — ensure your deployment isolates tenants so one tenant's cached prefix can't inform another's timing or content. Keep engines patched.

How to choose, in practice

01 — Setup & control: Ollama is the simplest local starting point. One install, one command, offline.
02 — Throughput & scale: vLLM is strongest when many users hit it at once with different prompts.
03 — Reuse repeated context: SGLang shines when prompts share stable context — system prompts, tool lists, chat history.
04 — Benchmark the real job: compare first-token latency, full response time, GPU usage, and cost per completed task on your workload. Key take: stable instructions first, variable data later.

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Built by Ingenious AI — engine facts verified against project docs and independent benchmarks; numbers vary by workload, benchmark your own.
This page is general information, not legal advice.