A language model is only half of a deployment. The other half is an inference server that handles many users at once, copes with long contexts and can be monitored. We compare vLLM vs Ollama, along with SGLang and NVIDIA NIM, and show which stack fits the testing stage and which belongs in production.
“vLLM vs Ollama” comes up in almost every project where a prototype running on a developer’s laptop has to serve dozens or hundreds of users. The answer is rarely “one or the other”. Each tool has its place, and production needs more than a model server: authentication, rate limits, metrics, logging and an update process. Below we cover all of it from the perspective of a team that builds and runs these environments.
The tools and what they are for
Ollama and llama.cpp: the quick start
Ollama is the simplest way to run a model locally: one command pulls the model and exposes an API. Under the hood it uses llama.cpp and GGUF models, and it runs on NVIDIA and AMD GPUs, Apple Silicon and plain CPUs. It also provides an OpenAI-compatible endpoint. It is designed for a single user or a small group, not for hundreds of simultaneous requests.
llama.cpp is the engine Ollama is built on, and you can use it directly. It gives you more control over parameters, runs on a very wide range of hardware from servers to edge devices, and its built-in llama-server also speaks an OpenAI-compatible API.
vLLM: the default for production serving
vLLM was built to serve many users on the same GPU. Its two key techniques are PagedAttention, which manages KV cache memory in small blocks instead of reserving it up front, and continuous batching, which adds new requests to the running batch without waiting for earlier ones to finish. Add an OpenAI-compatible API, splitting a model across multiple GPUs and broad hardware support, including Intel Gaudi.
SGLang: agents, RAG and structured output
SGLang is a fast inference server that also uses continuous batching and speaks an OpenAI-compatible API. It stands out for reusing shared request prefixes (RadixAttention) and for strong support for constrained output, such as JSON that must match a schema. That is particularly useful for agents and RAG systems, where many requests start with the same system prompt and the same documents.
NVIDIA NIM and TensorRT-LLM: the NVIDIA ecosystem
NVIDIA NIM provides ready-made containers bundling a model, an optimised inference engine and an OpenAI-compatible API. NIM picks an optimisation profile for the GPU it detects and, under the hood, uses engines including TensorRT-LLM, NVIDIA’s library for compiling models for specific GPUs. NIM also runs on NVIDIA DGX Spark as part of DGX OS and the NVIDIA AI stack. The constraints: it runs only on NVIDIA GPUs, and production use requires an NVIDIA AI Enterprise licence – check the current terms before you commit.
Hugging Face TGI is also available, but in new deployments the choice usually comes down to the four options above.
vLLM vs Ollama vs SGLang vs NIM: comparison
The table sums up the key differences. Even a quick look at vLLM vs Ollama shows they are tools for different jobs rather than direct competitors.
| Feature | Ollama / llama.cpp | vLLM | SGLang | NVIDIA NIM |
|---|---|---|---|---|
| Main use case | development, testing, single user, edge devices | production with many users | production, agents, RAG, structured output | production within the NVIDIA ecosystem, fast rollout of packaged models |
| Multi-user throughput | limited; a few parallel requests | continuous batching, PagedAttention, prefix caching | continuous batching, RadixAttention (shared prefixes) | optimised engines (including TensorRT-LLM) with batching |
| API | OpenAI-compatible (plus Ollama’s own API) | OpenAI-compatible | OpenAI-compatible | OpenAI-compatible |
| Hardware | NVIDIA, AMD, Apple Silicon, CPU | NVIDIA, AMD, Intel Gaudi and others | mainly NVIDIA and AMD | NVIDIA only (including DGX Spark) |
| Model formats | GGUF (quantised) | Hugging Face weights, FP8, common quantisations | Hugging Face weights, FP8, common quantisations | models from NVIDIA’s catalogue, optimised profiles |
| Complexity | low | medium | medium | low to medium; tied to NVIDIA and licensing |
Why Ollama usually is not enough in production
Ollama is not “worse”. It is built for a different job. When dozens of people query a model at the same time, what matters is how the server manages KV cache memory and how it batches requests. vLLM and SGLang keep the GPU busy and avoid making new requests queue behind earlier ones. As the number of users grows, the difference in aggregate throughput and response times becomes clear. The exact figures depend on the model, hardware and context length, so always measure them on your own workload.
There are operational differences too. vLLM and SGLang expose Prometheus metrics and let you set memory limits, context length and the number of concurrent sequences precisely. These are the knobs you turn every day in production.
When is Ollama enough? When an internal tool has a handful of users who rarely query it at the same time, and simple maintenance matters more than throughput. It is also a good choice on a developer workstation for testing prompts and models.
vLLM or SGLang?
Both are mature, both speak the same API and both are production-ready. The choice depends on your workload:
- vLLM – the broadest model and hardware support, a large community, a safe default. If you are considering Intel Gaudi, vLLM is the natural candidate.
- SGLang – worth testing when requests share long prefixes (agents, RAG, a common system prompt), when you need strictly formatted output, or when you are building multi-step call flows.
In practice, the best approach is to run both on the same model and hardware, replay realistic traffic and compare time to first token and throughput. Since both are OpenAI-compatible, swapping one for the other behind an API gateway requires no application changes.
Production is more than an inference server
A model server exposed on the network is not yet a production service. These are the pieces we add to every deployment.
Authentication and rate limiting
vLLM supports a simple API key, but one shared key for the whole company is not enough. We put an API gateway in front of the inference server: LiteLLM, or a gateway you already run (such as Kong or Envoy). The gateway issues separate keys for teams and applications, enforces request and token limits, integrates with SSO and routes traffic to the right model. That way one application cannot starve everyone else of GPU time.
Monitoring and metrics
Prometheus and Grafana collect inference server metrics: time to first token, inter-token latency, queue length, KV cache usage and number of running sequences. Add GPU metrics (for example via NVIDIA DCGM Exporter): utilisation, memory, temperature and power draw. Alert on growing queues and response times, not just on “server is down”.
Logging without leaking prompts
Prompts and responses often contain personal data, contract excerpts or source code. By default we log metadata only: who, when, which model, token counts, latency and error codes. Content is stored only when a specific purpose requires it (an audit, for example), with restricted access and a defined retention period. Also check that the inference server itself does not write request content to its logs – in vLLM this is controlled by a startup option. This is a GDPR matter as well as a security one.
Autoscaling on Kubernetes
Kubernetes with the NVIDIA GPU Operator lets you manage GPU nodes like the rest of the cluster. Base scaling on queue length and pending requests (for example with KEDA), not on CPU load, which says little about inference. Remember cold starts: loading weights measured in tens or hundreds of gigabytes takes time, so keep weights on local NVMe or fast storage in the same data centre, and set the minimum replica count to cover baseline traffic. For larger installations there are established projects such as KServe and llm-d.
Model updates
A new model version changes how your application behaves. So we pin a specific revision of the weights and run a test set of prompts with expected answers before rollout. The new model first receives a share of traffic (a canary via the API gateway), while the old one stays ready for a quick rollback. We cover this lifecycle in more depth in MLOps in practice: secure AI model deployments and MLOps in day-to-day practice.
Recommended stack for each stage
The table below maps to the hardware tiers we describe in DGX Spark vs RTX PRO 6000 server vs HGX B200.
| Stage | Typical hardware | Inference server | Around it |
|---|---|---|---|
| Development and prototype | workstation, DGX Spark | Ollama or llama.cpp; NIM on DGX Spark | nothing beyond access on the company network |
| Team or department | server with 1–4 RTX PRO 6000 GPUs, GPU VPS | vLLM or SGLang | API gateway with keys, basic monitoring |
| Company-wide production | server with 8 RTX PRO 6000 GPUs, HGX B200/B300 | vLLM or SGLang, several replicas | gateway with limits and SSO, Prometheus and Grafana, Kubernetes, update process |
| NVIDIA ecosystem | DGX Spark, NVIDIA GPU servers | NIM, TensorRT-LLM | as above, plus an NVIDIA AI Enterprise licence |
| Intel Gaudi 3 | Supermicro system with 8 Gaudi 3 accelerators | vLLM | as above, plus model validation |
One rule matters most: the application always talks to the gateway through an OpenAI-compatible API. Then moving from Ollama to vLLM, from one GPU to a cluster, or from one model to another does not require rewriting code. We show an example of this architecture in our article on a self-hosted AI coding assistant.
Deploying and running your LLM stack
We build and operate the whole stack: GPU servers in our Tier III, ISO 27001-certified data centre in Poland, the inference server, the API gateway, monitoring and the model update process. Through our DevOps services we build Kubernetes deployments and CI/CD pipelines for models, and our server administration team looks after everything 24/7. Alternatively, you get root access and run the stack yourself.
FAQ: frequently asked questions
What is the difference between vLLM and Ollama?
Ollama is a simple tool for running models locally, best for a single user and testing. vLLM is an inference server designed to serve many users at once using continuous batching and PagedAttention. Both offer an OpenAI-compatible API.
Is Ollama production-ready?
For small internal tools with a few users, yes. With dozens of concurrent users or more, vLLM or SGLang are a better fit: they use the GPU more efficiently and expose more metrics.
Which is faster, vLLM or SGLang?
It depends on the workload. SGLang gains where requests share long prefixes, as in agents and RAG. vLLM supports more models and hardware. Measure both on your own model and traffic.
Is NVIDIA NIM free?
You can try NIM through NVIDIA’s developer programmes, but production use requires an NVIDIA AI Enterprise licence. Check the current terms before choosing this route.
How do you monitor an LLM inference server?
Collect inference metrics (time to first token, queue length, KV cache usage) and GPU metrics in Prometheus, visualise them in Grafana and alert on growing queues and response times. Do not log prompt content unless there is a clear need.
Want to run a model in production without building everything from scratch? We will choose the hardware and stack, deploy it and take over operations. See our GPU servers or contact us.