Use Ollama when you want a simple local model workflow and API. Use llama.cpp when you want portable, low-level local inference with strong GGUF support across CPU and GPU hardware. Use vLLM when you need production-oriented GPU serving, continuous batching and an OpenAI-compatible server. Use SGLang when you need high-performance serving with features such as prefix caching, multi-GPU parallelism and production-scale model execution. The right choice depends on hardware, model format, concurrency and operational complexity.
Ollama: local models with a product-like workflow
Ollama packages model acquisition, local execution and an API into a relatively simple developer experience. Its documentation presents it as a way to run models locally, connect them to applications and use capabilities such as embeddings, tool calling and vision where supported.
That makes Ollama attractive for local prototypes, desktop applications, internal experimentation and teams that do not want to assemble a lower-level runtime configuration for every model.
The abstraction is also the trade-off: advanced users may want more direct control over model files, low-level flags, kernels or distributed serving than the default workflow exposes.
llama.cpp: portable inference and the GGUF ecosystem
llama.cpp is a C/C++ inference project designed to run LLMs and VLMs with minimal setup across a broad range of hardware. It is strongly associated with GGUF model files and quantized local inference.
The project supports command-line use and an OpenAI-compatible server. It can use CPU, GPU acceleration and hybrid offloading depending on platform and build. This makes it valuable for laptops, desktops, edge systems and custom applications that need direct runtime control.
For very high-concurrency production GPU serving, a runtime designed specifically around datacenter batching can be a better fit.
vLLM: production GPU serving
vLLM provides an OpenAI-compatible HTTP server and is built around efficient large-model serving. It is widely used for GPU deployments where throughput, batching and memory management matter.
A vLLM deployment looks more like infrastructure than a desktop utility: operators choose model, tensor parallelism, quantization, API settings, scheduling and security controls. Its documentation explicitly notes that API-key authentication does not protect every endpoint, so production deployments should be hardened behind appropriate network and reverse-proxy controls.
This is a useful reminder that a model server is part of the security perimeter, not just a library call.
SGLang: high-performance serving and prefix-aware workloads
SGLang describes itself as a high-performance serving framework for large language and multimodal models. Its documentation highlights low latency, high throughput, RadixAttention, prefix caching and multi-GPU parallelism, with OpenAI-compatible APIs.
Prefix caching can be especially relevant when many requests share system prompts, document prefixes or agent scaffolding. Instead of recomputing the same prefix repeatedly, a serving system can reuse cached work where supported.
SGLang is therefore attractive for demanding production workloads, although teams should benchmark it against vLLM on the exact model and traffic pattern rather than assuming one runtime is universally faster.
Runtime comparison
| Criterion | Ollama | llama.cpp | vLLM | SGLang |
|---|---|---|---|---|
| Local UX | Excellent | Strong but lower-level | Server-oriented | Server-oriented |
| GGUF | Common workflow | Core ecosystem | Not primary path | Not primary path |
| OpenAI-compatible API | API available | Yes | Yes | Yes |
| High concurrency | Not primary focus | Moderate / use-case dependent | Strong | Strong |
| Multi-GPU production | Limited compared with server frameworks | Supported patterns vary | Core use case | Core use case |
| Best fit | Developer/local | Portable local/edge | GPU serving | Advanced GPU serving |
How to choose without over-engineering
- If one developer needs a local assistant, start with Ollama.
- If the application needs precise GGUF control, custom embedding or portable inference, evaluate llama.cpp.
- If many users need a GPU-hosted API, benchmark vLLM.
- If prefix-heavy, multimodal or advanced distributed serving matters, benchmark SGLang as well.
- Measure tokens/second, time-to-first-token, memory, concurrent throughput and failure behavior on your exact workload.
Frequently asked questions
Is Ollama the same as llama.cpp?
No. They are separate projects. Ollama provides a higher-level local model workflow, while llama.cpp is a lower-level inference project and GGUF ecosystem.
Which is better, vLLM or SGLang?
There is no universal winner. Both target high-performance serving. Benchmark the exact model, accelerator topology and request pattern.
Can vLLM expose an OpenAI-compatible API?
Yes. vLLM documents an OpenAI-compatible server for completions, chat and other endpoints.
Can llama.cpp run an OpenAI-compatible server?
Yes. The project includes a server mode with an OpenAI-compatible API, useful for local or custom deployments.
Apply this knowledge
Move from the concept to a concrete deployment shortlist.
Primary sources and technical references
OpenWeightModels prefers publisher documentation, standards bodies, official repositories and original research papers. The source material remains authoritative where it changes.