TL;DR
Last updated October 4, 2026.
Ollama is the easiest way to run an open model on your own machine. vLLM is the standard engine for serving one on GPUs to many users at once. Both speak the OpenAI API. Pick Ollama for local development, laptops, Apple Silicon, and single-user tools. Pick vLLM when concurrent requests and tokens per GPU-hour decide your bill. Ollama processes one request per model at a time by default; vLLM batches requests continuously.
Ollama wins on
Setup time. One install, ollama pull and ollama run, a curated model library, GGUF quantized models that fit in laptop memory, and first-class Apple Silicon support through its llama.cpp-derived backend.
vLLM wins on
Throughput under load. PagedAttention keeps KV cache fragmentation low so more sequences fit in a batch, continuous batching keeps the GPU busy, and tensor and pipeline parallelism spread large models across GPUs.
vLLM vs Ollama Comparison Table
Each row comes from the projects' own GitHub READMEs and documentation, checked October 4, 2026.
| vLLM | Ollama | |
|---|---|---|
| Built for | Multi-user GPU serving | Running models locally |
| License | Apache 2.0 | MIT |
| Latest release | v0.30.0 | v0.35.1 |
| GitHub stars | 93.2k | 182k |
| Model format | Hugging Face weights (safetensors), plus GGUF | GGUF; Safetensors imported through a Modelfile |
| Engine core | PagedAttention + continuous batching | llama.cpp-derived backend |
| Concurrency default | Batches concurrent requests | 1 parallel request per model (OLLAMA_NUM_PARALLEL) |
| Default context | Configurable per server | 4,096 tokens (OLLAMA_CONTEXT_LENGTH) |
| Quantization | AWQ, GPTQ, BitsAndBytes, GGUF, FP8, INT8, INT4 | Pre-quantized GGUF; does not quantize on import |
| Multi-GPU | Tensor, pipeline, data, and expert parallelism | Splits a model across GPUs only when it does not fit on one |
| Models per server | One model per server process | Several loaded at once (OLLAMA_MAX_LOADED_MODELS) |
| OpenAI-compatible API | Yes, at :8000/v1 | Yes, at :11434/v1 |
| Docker image | vllm/vllm-openai | ollama/ollama |
vLLM vs Ollama Performance and Concurrency
The performance gap between the two is mostly a concurrency gap. Ollama's FAQ sets OLLAMA_NUM_PARALLEL to 1 by default, so a model answers one request at a time and the rest wait in a queue that holds up to 512 requests (OLLAMA_MAX_QUEUE) before the server starts returning 503 errors. Raising the parallel count works, but Ollama allocates context memory per slot: a 2K context with 4 parallel requests becomes an 8K context allocation.
vLLM was designed around that problem. The PagedAttention paper (Kwon et al., 2023) stores each sequence's KV cache in fixed-size blocks that need not be contiguous, so memory is not lost to fragmentation and more sequences fit in one batch. Continuous batching adds new requests to the running batch as soon as others finish. With ten agents or users calling the same model, that design keeps the GPU busy where a one-slot server would serialize the work.
| Workload | Likely leader | Why |
|---|---|---|
| One developer, one prompt at a time, laptop | Ollama | Quantized GGUF fits in memory; batching has nothing to batch |
| Apple Silicon Mac | Ollama | Metal backend from the llama.cpp lineage |
| CPU-only machine | Ollama | GGUF quantization keeps models small enough for system RAM |
| Many users or agents on one model | vLLM | Continuous batching and paged KV memory |
| Model larger than one GPU | vLLM | Tensor and pipeline parallelism |
| Long shared system prompts | vLLM | Automatic prefix caching reuses the shared tokens |
Published vLLM vs Ollama benchmarks rarely match each other because they mix quantization levels, GPUs, context lengths, and concurrency settings. Run both against a replay of your own requests at the concurrency you expect, and compare tokens per second and time to first token at that load.
Model Formats and Quantization
Ollama runs GGUF models. You can pull them from the Ollama library or import your own with a Modelfile whose FROM line points at a GGUF file or a Safetensors directory. Ollama's import docs say it does not quantize GGUF models during import, so you quantize first with llama.cpp's llama-quantize.
vLLM loads Hugging Face checkpoints directly. Its quantization docs cover AutoAWQ, GPTQModel, BitsAndBytes, GGUF, and FP8, and point to LLM Compressor for producing FP8, INT8, and INT4 weights. Support varies by GPU generation, so check the hardware matrix in the vLLM docs before you pick a format. For the trade-offs between FP8 and lower-bit formats, see FP8 quantization.
Hardware Support
| Platform | vLLM | Ollama / llama.cpp |
|---|---|---|
| NVIDIA GPUs | Yes | Yes (CUDA) |
| AMD GPUs | Yes | Yes (HIP / ROCm) |
| Intel | Intel GPUs; Gaudi via plugin | SYCL backend in llama.cpp |
| Apple Silicon | Via hardware plugin | First-class (Metal, Accelerate) |
| CPUs | x86, ARM, PowerPC | Yes |
| Other accelerators | Plugins for Google TPU, IBM Spyre, Huawei Ascend, Rebellions, MetaX | Vulkan and MUSA backends in llama.cpp |
APIs and Launch Commands
Both servers accept the standard OpenAI client, so moving a prototype from Ollama to vLLM is mostly a base URL and model name change.
# Ollama: OpenAI-compatible API on http://localhost:11434/v1
ollama pull llama3.2
ollama run llama3.2
OLLAMA_NUM_PARALLEL=4 OLLAMA_CONTEXT_LENGTH=8192 ollama serve
# vLLM: OpenAI-compatible API on http://localhost:8000/v1
uv pip install vllm
vllm serve Qwen/Qwen3-4B
# Same client for both
from openai import OpenAI
client = OpenAI(base_url="http://localhost:8000/v1", api_key="unused")Ollama binds to 127.0.0.1 by default. To reach it from another machine, set OLLAMA_HOST, for example to 0.0.0.0:11434. Ollama also keeps a model in memory for five minutes after the last request; the keep_alive parameter changes that. vLLM's server hosts one model per process, and --host and --port set the address.
vLLM vs llama.cpp
Many searches compare vLLM with llama.cpp rather than Ollama. llama.cpp is the C/C++ library behind the GGUF format, MIT licensed, with 130k GitHub stars. It treats Apple Silicon as a first-class target through ARM NEON, Accelerate, and Metal, and adds CUDA, HIP, Vulkan, and SYCL backends. Its CLI can launch an OpenAI-compatible server with llama serve -hf <repo>.
Ollama wraps the same lineage with a model library and a background service, so the vLLM vs llama.cpp decision follows the same line as vLLM vs Ollama. Use llama.cpp when you want direct control over a local or edge runtime. Use vLLM for data-center GPUs serving many concurrent requests. If you are weighing vLLM against another server engine, read SGLang vs vLLM.
vLLM vs Ollama Pros and Cons
- Ollama: one-command install and model pulls
- Ollama: runs on laptops, Apple Silicon, and CPUs
- Ollama: several models loaded at once
- vLLM: continuous batching for concurrent traffic
- vLLM: tensor and pipeline parallelism across GPUs
- vLLM: broad quantization support including FP8 and AWQ
- Ollama: one request per model by default
- Ollama: parallel slots multiply context memory
- vLLM: one model per server process
- vLLM: built for data-center GPUs, heavier to set up
Which One to Pick
Start with Ollama if you are one developer trying models, building a local tool, or running on a Mac. Move to vLLM when a model serves a team, a product, or a fleet of agents, because that is when one-request-at-a-time serving becomes the bottleneck. Teams often use both: Ollama on laptops during development and vLLM behind the production endpoint, with the same OpenAI client code in each place.
To understand the techniques vLLM relies on, read continuous batching, KV cache, and nano-vLLM, a 1,200-line reimplementation of the engine. For picking a model to run in Ollama, see the best Ollama models. If you would rather not operate a serving stack, Morph serves open models through an OpenAI-compatible API; see Morph Open Source Models.
Frequently Asked Questions
What is the difference between vLLM and Ollama?
Ollama is a local model runner. You pull models from its library with ollama pull, it runs GGUF models on a llama.cpp-derived backend, and its server listens on port 11434. vLLM is a GPU serving engine that loads Hugging Face weights, batches concurrent requests with PagedAttention and continuous batching, and serves an OpenAI-compatible API on port 8000. Ollama is optimized for ease of use on one machine; vLLM is optimized for throughput under many simultaneous requests.
Is vLLM faster than Ollama?
Under concurrent load, usually yes. Ollama's docs set OLLAMA_NUM_PARALLEL to 1 by default, so each model handles one request at a time unless you raise it, and every extra parallel slot multiplies the context memory. vLLM is designed to batch many requests on the same GPU. For a single user sending one prompt at a time on a laptop, the gap is small and Ollama's quantized GGUF models may be the only option that fits in memory. Benchmark with your own model, hardware, and request mix.
Can Ollama handle multiple users?
Yes, with tuning. Set OLLAMA_NUM_PARALLEL to allow parallel requests per model and OLLAMA_MAX_LOADED_MODELS to keep several models in memory (default 3 per GPU, or 3 on CPU). Requests beyond capacity queue up to OLLAMA_MAX_QUEUE, which defaults to 512, and the server returns a 503 once the queue is full. Required memory scales with OLLAMA_NUM_PARALLEL times OLLAMA_CONTEXT_LENGTH.
Does vLLM support GGUF models like Ollama?
Yes. vLLM's quantization docs list GGUF alongside AWQ, GPTQ, BitsAndBytes, and FP8, and LLM Compressor produces FP8, INT8, and INT4 checkpoints for vLLM. Support for each format varies by GPU and accelerator, so check the hardware matrix in the vLLM quantization docs before choosing one.
Do vLLM and Ollama both support the OpenAI API?
Yes. vLLM's server implements the OpenAI protocol at http://localhost:8000/v1, including models, completions, and chat completions. Ollama exposes OpenAI-compatible /v1/chat/completions, /v1/completions, and /v1/responses endpoints at http://localhost:11434/v1. Pointing the OpenAI SDK at either one is a base URL change.
What is the difference between vLLM and llama.cpp?
llama.cpp is a C/C++ inference library and CLI built around the GGUF format, with Metal on Apple Silicon, CUDA, HIP, Vulkan, and SYCL backends and an OpenAI-compatible server (llama serve). It is MIT licensed and is the engine lineage Ollama builds on. vLLM is a Python GPU serving engine (Apache 2.0) aimed at batched multi-user throughput. Choose llama.cpp for local and edge inference, vLLM for data-center GPUs serving many requests.
The fastest endpoints are private deployments
Morph's top speeds come from dedicated deployments, not shared public endpoints: speculators trained on your traffic, caching tuned to your workload, and volume discounts over public per-token rates. Over 500 billion tokens per day run this way.
Skip running the serving stack yourself
Morph serves open-weight models on custom kernels behind an OpenAI-compatible API, with dedicated deployments when you need guaranteed throughput.
Sources
- vLLM GitHub README (features, hardware, license, releases)
- vLLM Quickstart (OpenAI-compatible server, default port 8000)
- vLLM quantization documentation
- Kwon et al., Efficient Memory Management for LLM Serving with PagedAttention
- Ollama GitHub README (license, releases)
- Ollama FAQ (concurrency, context length, ports, keep-alive)
- Ollama OpenAI compatibility
- Ollama model import (GGUF and Safetensors)
- llama.cpp GitHub README (backends, server, license)