Ollama vs llama.cpp vs vLLM vs SGLang: Which Local AI Runtime Should You Use?

By AiCybr Editorial · Published August 13, 2026

There is no single best local-AI runtime. Ollama is usually the easiest starting point, llama.cpp gives the broadest lightweight GGUF/hardware path, while vLLM and SGLang are built for serious concurrent serving. The right choice depends less on a synthetic tokens-per-second chart than on model format, hardware, concurrency and how much operational complexity you actually need.

Quick answer

That table is a starting point, not a universal benchmark result. vLLM and SGLang can overlap heavily, Ollama can serve multiple users, and llama.cpp includes a capable server. The differences become clearer when you look at what each project optimizes for.

The four runtimes solve different problems

Ollama: local AI as an application service

Ollama packages model acquisition, configuration and serving into a relatively simple local workflow. It exposes its own API and supports parts of the OpenAI API, including chat completions, completions, embeddings and Responses API functionality.

Its advantage is operational simplicity. For a developer who wants a local model behind Open WebUI, a coding tool or a small application, the model-management layer is often more valuable than squeezing every possible request per second from the GPU.

Ollama is not limited to one request at a time. Its current documentation describes concurrent model loading, parallel requests for a model and a configurable request queue. Those features consume memory: parallel requests increase context-memory requirements, and concurrently loaded GPU models need to fit available VRAM.

Choose Ollama when: you value easy installation, pulling and switching models, local application integration and modest multi-user serving more than maximum serving density.

llama.cpp: lightweight inference with exceptional hardware reach

llama.cpp is the lowest-level choice in this comparison. Its core goal is efficient LLM inference with minimal setup across a wide range of hardware. GGUF is central to its ecosystem, and the project supports aggressive integer quantization, CPU inference, Apple Metal, CUDA, HIP, Vulkan and SYCL paths.

One especially useful capability is CPU+GPU hybrid inference. A model that exceeds VRAM can place only part of its layers on the GPU and use system memory for the rest. That is slower than keeping the complete workload on a fast accelerator, but it can make otherwise impossible local models usable.

llama-server also provides an HTTP server with OpenAI-compatible endpoints, so choosing llama.cpp does not mean giving up API serving.

Choose llama.cpp when: GGUF is your preferred format, CPU or Apple Silicon matters, you need unusual backend support, or fitting a large quantized model matters more than production request throughput.

vLLM: throughput-oriented model serving

vLLM is primarily a serving engine rather than a desktop model manager. Its current architecture includes PagedAttention-based KV-memory management, continuous batching, chunked prefill, prefix caching and multiple distributed parallelism modes.

It integrates closely with Hugging Face model formats and exposes an OpenAI-compatible API. Current vLLM documentation lists NVIDIA and AMD GPUs plus CPU and a growing plugin ecosystem for other accelerators. Its quantization support includes formats such as AWQ, GPTQ, FP8/INT8/INT4 families, BitsAndBytes and GGUF, although support is hardware- and method-dependent. Do not read “vLLM supports GGUF” as meaning every GGUF workflow is as mature or flexible as llama.cpp.

The key reason to move from a desktop-oriented runtime to vLLM is concurrency. Continuous batching allows a server to combine work from incoming requests rather than treating each interactive request as an isolated job.

Choose vLLM when: you are serving a supported model to multiple clients, throughput and GPU utilization matter, or you need tensor/pipeline/data/expert parallelism across serious inference hardware.

SGLang: production serving with aggressive prefix reuse

SGLang occupies much of the same production-serving territory as vLLM. Its current documentation describes continuous batching, paged attention, speculative decoding, quantization and tensor/pipeline/expert/data parallelism, with OpenAI-compatible APIs.

Its distinguishing architectural idea is RadixAttention. SGLang organizes reusable KV-cache state in a radix tree and uses cache-aware scheduling. This is particularly interesting when many requests repeatedly share long prefixes: system prompts, tool definitions, few-shot examples or agent/RAG scaffolding.

That does not mean SGLang is automatically faster than vLLM. Performance depends on model, accelerator, request distribution, context lengths, concurrency, quantization and software version. A benchmark measured for one workload should not be generalized to another.

Choose SGLang when: you operate concurrent agentic, RAG or structured-generation workloads where prefix reuse is important, or its current model/hardware path fits your deployment better than vLLM.

Architecture comparison

Model format can decide the answer before benchmarks do

For local inference, model format is often the first filter.

If you already have a carefully selected GGUF quantization, llama.cpp is the natural reference runtime. Ollama also makes GGUF-based local use approachable through its model packaging, but hides more of the low-level controls.

If your deployment begins with a Hugging Face checkpoint and targets one or more datacenter-class GPUs, vLLM or SGLang is generally the more natural evaluation path. Their serving architectures are designed around batching, KV-cache management and distributed accelerators rather than primarily around squeezing a model into consumer system memory.

Converting formats merely to use a fashionable runtime can introduce extra testing work. Start from the model and hardware you actually intend to operate.

VRAM: why concurrency changes the calculation

A model fitting into VRAM for one chat session does not prove it will fit your production workload.

Memory can also be consumed by KV cache, runtime workspaces, multimodal components and concurrent sequences. Ollama explicitly notes that parallel requests increase context-memory allocation. vLLM and SGLang deliberately use available accelerator memory to serve and cache multiple sequences efficiently.

For a 24GB consumer GPU, this often creates two very different goals:

• largest model possible for one user: GGUF plus llama.cpp/Ollama can be attractive;
• highest useful concurrency from a model that comfortably fits: vLLM/SGLang become much more interesting.

This is why “runtime X is faster” is usually an incomplete buying recommendation.

vLLM vs SGLang: how to choose

For a new production deployment, benchmark your own request trace against both when the model is supported.

Favor vLLM when its mature model path, quantization option, hardware support or operational ecosystem is the cleaner fit. Favor SGLang when repeated-prefix workloads, agent pipelines or its RadixAttention scheduling produce a measurable advantage for your traffic.

Test at least:

1. time to first token;
2. output tokens per second per request;
3. aggregate throughput at realistic concurrency;
4. p50 and p95 latency;
5. VRAM use at the context lengths you actually need;
6. failure behavior under overload;
7. tool-calling and structured-output correctness for your model.

A single-user tokens-per-second result is not a production serving benchmark.

Ollama vs llama.cpp: how to choose

The tradeoff is largely convenience versus control.

Use Ollama if you want a persistent local service, easy model pulls, application integration and minimal model-management work. Use llama.cpp directly when you want explicit GGUF selection, low-level launch controls, broad hardware experimentation or hybrid CPU/GPU placement.

They are not mutually exclusive ecosystems. Ollama's convenience layer and llama.cpp-style local inference address overlapping needs, and many users can start with Ollama before moving to a lower-level or production-oriented runtime when a concrete bottleneck appears.

Recommended progression

For most local-AI users, a sensible progression is:

Ollama → llama.cpp when you need more control → vLLM/SGLang when concurrency becomes the problem.

Do not migrate just because a benchmark says another runtime is faster. Migrate when you can identify the bottleneck: unsupported model format, insufficient memory flexibility, poor concurrent throughput, weak prefix reuse, missing distributed serving, or operational complexity.

Sources and verification

This comparison uses current upstream documentation rather than cross-project marketing benchmarks:

• Ollama OpenAI compatibility
• Ollama FAQ: concurrency, model loading and memory behavior
• llama.cpp upstream repository
• llama.cpp server documentation
• vLLM documentation
• vLLM quantization compatibility
• SGLang documentation
• SGLang upstream repository

Runtime capabilities change quickly. Check the current model, quantization and hardware compatibility pages before standardizing a production deployment.