vLLM vs Ollama: which should you use for LLM serving?
Use vLLM if you need high-throughput concurrent inference on GPU with batching. Use Ollama if you want the simplest local setup with a single command, even on CPU.
VERDICT
Side-by-side comparison
| Feature | vLLM | Ollama | Winner |
|---|---|---|---|
| GPU required | Yes (CUDA/ROCm/Metal) | No (CPU-only works, GPU optional) | Ollama |
| Throughput (7B model, A100) | ~2,000 tokens/sec | ~100-300 tokens/sec | vLLM |
| Time to first token (7B) | ~100ms | ~500ms (CPU) / ~200ms (GPU) | vLLM |
| Installation complexity | pip install vllm (requires GPU drivers) | Single binary download or brew install | Ollama |
| OpenAI API compatibility | Full (/v1/chat/completions) | Partial (HTTP, not full OpenAI SDK) | vLLM |
| Concurrent user support | Yes (batched inference) | Sequential (one request at a time) | vLLM |
| Model management | Manual HuggingFace IDs | Automatic download (ollama pull) | Ollama |
| License | Apache 2.0 | MIT | Tie |
| Memory efficiency (7B Q4) | ~8GB VRAM required | ~4GB RAM (CPU) / ~6GB VRAM (GPU) | Ollama |
| Ease of local deployment | Requires setup | Works immediately | Ollama |
Performance benchmarks
Throughput with continuous batching (7B model, NVIDIA A100)
vLLM uses PagedAttention and continuous batching for multiple concurrent users; Ollama processes requests sequentially
Time to first token (7B Llama-2, A100)
vLLM optimizes for latency with KV-cache reuse; Ollama prioritizes simplicity over latency optimization
CPU-only inference (7B on Intel i7-12700K)
Ollama's llama.cpp backend handles CPU well with quantization; vLLM not designed for CPU
Memory footprint (7B Q4 GGUF quantized)
Ollama's GGUF format is more memory-efficient; vLLM typically uses FP16
Setup time to first inference (local laptop)
Ollama includes automatic model downloading; vLLM requires HF model ID specification
When to use each
- ✓ Serving multiple concurrent users where throughput matters: vLLM's continuous batching handles 10-100x more simultaneous requests than Ollama
- ✓ Building a production API that needs OpenAI-compatible endpoints at /v1/chat/completions with existing client SDKs
- ✓ Fine-tuning inference performance with LoRA, prefix caching, and speculative decoding for latency-critical applications
- ✓ Running on a GPU cluster or cloud instance where you're paying per compute: vLLM's batching efficiency maximizes throughput-per-dollar
- ✓ Need for Python integration with LangChain, LlamaIndex, or other frameworks that expect OpenAI-compatible APIs
- ✓ Local machine inference (MacBook, Linux laptop, or home server) where simplicity and immediate functionality matter more than throughput
- ✓ CPU-only deployment or edge devices: Ollama's llama.cpp backend is optimized for CPU inference with quantization
- ✓ Quick prototyping or running models interactively without building an API or service infrastructure
- ✓ Embedding LLM inference directly into desktop or CLI applications with minimal dependencies
- ✓ Memory-constrained environments: Ollama's GGUF quantization uses 50% less memory than vLLM's FP16
Common misconceptions
vLLM
vLLM only works with specific model architectures or providers
vLLM supports 100+ HuggingFace models (Llama, Mistral, Qwen, Phi, etc.) and runs any model in GPTQ/AWQ quantized formats. You specify models by HF ID, not locked to specific providers.
vLLM requires Kubernetes, complex deployment, or cloud infrastructure
vLLM runs on a single GPU with `pip install vllm` and one Python script. The OpenAI API server is a 10-line script. Most small deployments run on a single $1-2k GPU.
vLLM is unstable or unproven in production
vLLM powers production LLM APIs at major startups and scale to millions of requests. It's battle-tested and has been stable since 0.1 release (2023).
Ollama
Ollama can only be used locally and doesn't support concurrent requests
Ollama can run a server accessible over HTTP and supports multiple requests, but they queue sequentially (one at a time). High concurrency will cause queueing delays.
Ollama is 'just a wrapper' and less capable than vLLM
Ollama uses the mature llama.cpp backend with careful GGUF quantization. For single-user local inference, it's often faster and more memory-efficient than vLLM.
Ollama doesn't support GPU or requires special setup for GPU acceleration
Ollama auto-detects NVIDIA/AMD GPUs and GPU offloading works out of the box. It just doesn't batch multiple requests across GPUs like vLLM does.
Code examples
Task: Load a model and generate text from a single prompt using vLLM's LLM interface.
from vllm import LLM, SamplingParams
# vLLM automatically handles batching and GPU memory
llm = LLM(model="meta-llama/Llama-2-7b-hf", gpu_memory_utilization=0.8)
sampling_params = SamplingParams(temperature=0.7, max_tokens=100)
prompt = "What is machine learning?"
# This call can handle multiple prompts; vLLM batches them automatically
outputs = llm.generate([prompt], sampling_params)
for output in outputs:
print(output.outputs[0].text)vLLM's LLM class abstracts away GPU complexity and automatically batches requests. You can pass 100 prompts and vLLM efficiently processes them in parallel on GPU.
import requests
import json
# Ollama runs as a local HTTP server (start with: ollama serve)
ollama_url = "http://localhost:11434/api/generate"
prompt = "What is machine learning?"
response = requests.post(
ollama_url,
json={
"model": "llama2", # Ollama auto-manages model downloads
"prompt": prompt,
"stream": False,
"temperature": 0.7,
},
)
result = response.json()
print(result["response"])Ollama uses HTTP requests, not Python-native APIs. Each request queues on the server; multiple concurrent requests will wait, not batch.
Migration path
- Migrating from Ollama to vLLM:
- Install vLLM: `pip install vllm` instead of using Ollama binary.
- Replace Ollama's HTTP endpoint calls with vLLM's Python LLM class: `from vllm import LLM; llm = LLM(model='meta-llama/Llama-2-7b-hf')`.
- For model names, use HuggingFace IDs ('meta-llama/Llama-2-7b-hf') instead of Ollama's model tags ('llama2').
- If you need an HTTP API, use vLLM's OpenAI-compatible server: `python -m vllm.entrypoints.openai.api_server --model meta-llama/Llama-2-7b-hf`.
- If using client libraries, switch to OpenAI SDK and point to vLLM's /v1 endpoint: Ollama's sequential HTTP is a drop-in replacement only for single-request apps. Expect code changes if you were relying on Ollama's model auto-download; vLLM requires explicit HF model IDs. For CPU-only environments, stay on Ollama: vLLM is GPU-required.
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