Local LLM for Teams: vLLM vs SGLang vs Ollama

The wall every local AI pilot eventually hits

Ollama is genuinely excellent — and that is exactly the problem. It is so easy to set up that teams run it as a proof-of-concept, share the API endpoint with the wider department, and then wonder why response times have ballooned to 30 seconds. The culprit is not the model. It is the serving layer.

Ollama processes requests sequentially. One user, one request at a time. That is fine — even fast — for a single developer. Add five colleagues making simultaneous requests and every user waits in a queue. At ten concurrent users, the queue is the bottleneck, not the GPU.

This is the moment when the question shifts from "which model should we run?" to "which server should we run it on?" Practitioners on X have been discussing this exact question throughout 2026, with the emerging consensus being: Ollama for development, vLLM or SGLang for production. This article explains why — and how to choose between the two.

What community benchmarks show

Since early 2026, developers have been publishing systematic benchmarks comparing local LLM serving frameworks. Based on results reported by the community, the differences at scale are substantial:

Ollama vs. vLLM at concurrency: At 50 simultaneous requests, vLLM delivers roughly six times the total throughput of Ollama, with a p99 latency reported under three seconds — compared to Ollama's reported p99 of around 24 seconds.

SGLang vs. vLLM on shared context: SGLang, a framework optimised for structured and batched generation, has been reported to deliver approximately 29% higher throughput than vLLM for workloads with shared prompt context — chatbots, RAG systems, agent pipelines. Some benchmarks report SGLang running 4.6× faster than vLLM under those specific conditions.

These numbers reflect specific hardware and workload configurations. They will not translate identically to your setup. But the directional signal is consistent across multiple independent measurements and worth factoring into infrastructure decisions.

The three tools compared

Ollama — right for development, wrong for teams

Ollama's value proposition is speed of setup: a single command installs the server, another pulls the model, and you have an OpenAI-compatible local API running in under five minutes. For individual developers, that is hard to beat.

Strengths:

• Runs on macOS, Linux, and Windows; native MLX support on Apple Silicon
• Pulls models from Ollama Hub and Hugging Face in GGUF or MLX format
• Compatible with LangChain, OpenWebUI, Continue.dev, and most LLM frameworks
• Works without a dedicated GPU — CPU inference is slow but functional

Limits:

• No request batching; sequential queue by design
• No KV-cache sharing across sessions
• Performance degrades sharply above two or three concurrent users

Use when: One developer is running a local AI workflow, you are evaluating models, or your Mac team needs a fast development backend.

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vLLM — the production default for GPU servers

vLLM emerged from academic research and has become the de facto standard for serving open-weight models at scale. Its core innovation is PagedAttention: rather than pre-allocating KV-cache memory in fixed blocks, vLLM manages it in dynamic pages — much like how an OS handles virtual memory. This eliminates the memory waste that degrades throughput under concurrent load.

Strengths:

• Scales from 5 to 100+ concurrent users without proportional latency growth
• Drop-in OpenAI API replacement — existing applications need zero code changes
• Supports quantised models (GPTQ, AWQ, FP8) for better VRAM efficiency
• Works with Llama 3.3, Qwen 2.5, Mistral, DeepSeek, Gemma 4, and most open-weight models
• Active open-source development with frequent releases

Limits:

• Requires a Linux server with a CUDA-compatible NVIDIA GPU for production performance
• Setup involves Python environment management and CUDA driver configuration
• Apple Silicon support is limited compared to Ollama's native MLX integration

Use when: Multiple team members need concurrent access to a shared model, you have a dedicated GPU server, or you are building an internal API used by more than one application.

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SGLang — the specialist for shared-context workloads

SGLang (Structured Generation Language) was designed for complex, multi-step LLM programs, but its architecture makes it particularly efficient for a specific class of production workloads: requests that share a common prompt prefix. Internal chatbots with a fixed system prompt, RAG systems where retrieved documents are prepended to every query, and agent pipelines where tools and instructions are constant all fall into this category.

The enabling technology is RadixAttention: SGLang detects shared context automatically and computes its attention values once, reusing the cached result across all matching requests. For workloads with high prefix repetition, this produces measurable throughput gains over vLLM.

Strengths:

• Highest reported throughput for shared-context workloads (RAG, chatbots, agents)
• OpenAI-compatible API
• Integrates with LangChain, LlamaIndex, and agent frameworks
• Supports the same open-weight model families as vLLM

Limits:

• Smaller community and less documentation than vLLM
• Offers minimal advantage over vLLM when requests have fully individualised contexts
• Similar setup complexity to vLLM

Use when: You are building an internal knowledge-base chatbot, a RAG system over company documents, or an agent pipeline where system context is shared across all users.

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Decision matrix

The tiebreaker between vLLM and SGLang is straightforward: if your requests share a system prompt, a document corpus, or any fixed prefix, SGLang's RadixAttention will make a measurable difference. If requests are fully independent, vLLM's PagedAttention provides equivalent performance with better documentation and a larger support community.

GDPR as a structural feature, not a checklist item

All four tools described here share one property that matters for European businesses: inference stays entirely on your infrastructure. No token, no prompt, no response is transmitted to an external service. This is not a privacy mode — it is the default operating model.

For businesses handling personal data, client confidential information, or regulated records, this addresses Article 25 GDPR (data protection by design and by default) at the infrastructure level. You remain the sole data controller, with no data processing agreement required with a US cloud provider.

Whether you run Ollama on a developer laptop or a vLLM cluster in your own server room, the data model is identical. The difference is scale and the data sovereignty it delivers across your whole organisation.

Infrastructure considerations for European SMBs

Migrating from Ollama to vLLM or SGLang does not require rewriting applications. Both frameworks expose an OpenAI-compatible API, which means any application built against Ollama — internal chatbots, RAG pipelines, coding assistants using Continue.dev — switches over by changing a single base URL.

The real work is hardware planning: which GPU, how much VRAM, what network topology for multi-user access. A Qwen 2.5 72B model in Q4 quantisation requires roughly 40–48 GB of VRAM — that means an NVIDIA A100, two 3090s, or an H100 depending on your concurrency target. Smaller models (7B–14B in Q4) fit on a single 24 GB consumer GPU with room for dozens of concurrent sessions under vLLM.

EU businesses exploring funding options for on-premise AI infrastructure may find relevant programmes through the European Regional Development Fund (ERDF) and national digitalisation support schemes. These are informational observations based on our reading of programme eligibility criteria — consult a qualified adviser for your specific situation.

Getting started

If your team is hitting the Ollama concurrency wall, or if you are planning a deployment that will serve more than a handful of users, the path forward is well-understood. Start a pilot project with Freshlab to size the infrastructure correctly and get vLLM or SGLang running in your environment — or get in touch to discuss the right architecture for your use case.