Ollama vs llama.cpp vs vLLM: Which Should You Use in 2026?

From the Best GPU for LLM archive. The canonical version has interactive calculators, an up-to-date GPU comparison table, and live pricing.

Three tools dominate local LLM inference in 2026. They are not interchangeable — each has a distinct use case, and choosing wrong wastes both time and hardware. Here is the direct comparison.

Quick comparison

Feature	Ollama	llama.cpp	vLLM
Setup difficulty	Easiest (one command)	Easy (compile or binary)	Harder (Python env)
Speed (single user)	Good	Best	Good
Speed (multi-user)	Limited	Limited	Best
Model format	GGUF	GGUF	HuggingFace / GPTQ / AWQ
GPU requirement	Any supported	Any	NVIDIA CUDA required
AMD support	Partial	Vulkan backend	Limited
API	OpenAI-compatible REST	REST (server mode)	OpenAI-compatible REST
Best for	Personal use	Power users	Production serving

Ollama — easiest, best for personal use

Ollama wraps llama.cpp under the hood with a model registry, automatic GPU detection, and a clean CLI. ollama run llama3 downloads the model and starts inference in seconds.

Best for:

Personal daily driver (chat, code assist, writing)
macOS users (native Apple Silicon support)
Non-technical users who want zero-friction setup
Running one model at a time

Limitations:

Less control over inference parameters than raw llama.cpp
Multi-user concurrency is limited
Model selection is limited to what's in the Ollama registry (though custom models work)

Minimum GPU: Any 8GB+ VRAM card with CUDA, ROCm, or Apple Silicon. Start here if you are new to local LLMs.

llama.cpp — fastest raw inference, most flexible

llama.cpp is a C++ inference engine that runs GGUF-format quantized models. It is what Ollama is built on, but running it directly gives you more control: batch size, rope scaling, context length, GPU layer splitting across multiple cards.

Best for:

Squeezing maximum tokens per second from a single GPU
Splitting large models across multiple GPUs or GPU+CPU
Running any GGUF model file, not just registry models
Linux power users who tune inference settings
Embedding and batch processing workloads

Limitations:

No built-in model management (you download files yourself)
Server mode is less polished than Ollama's API
Config requires some familiarity with inference parameters

GPU requirement: Same as Ollama — any CUDA or ROCm GPU. Vulkan backend provides AMD compatibility without ROCm. For multi-GPU tensor parallelism on large models, you need matching GPU pairs.

Speed note: Direct llama.cpp with optimized settings runs 10-20% faster than Ollama on the same hardware, since Ollama adds wrapper overhead. For interactive chat, the difference is small. For batch processing, it adds up.

vLLM — best for production serving

vLLM is a Python inference server designed for high-throughput multi-user serving. Its PagedAttention algorithm allows it to batch multiple requests efficiently, turning what would be sequential processing into parallel GPU utilization.

Best for:

Serving LLMs to multiple users simultaneously
Production API endpoints with SLA requirements
Teams running shared LLM infrastructure
Maximizing GPU utilization on expensive hardware (A100, H100)

Limitations:

Requires NVIDIA CUDA. AMD support exists but is incomplete.
Higher VRAM overhead than llama.cpp due to paging and batching buffers (plan for 20-30% more VRAM than the model base size)
Slower than llama.cpp for single-user, single-request inference
More complex setup (Python environment, HuggingFace model formats)

GPU requirement: NVIDIA cards with 16GB+ VRAM minimum for practical serving. The sweet spot for vLLM is 24GB+ cards. For multi-user production use, A100/H100 class hardware is the real target.

GPU tier list available at the original article

GPU requirements side by side

Tool	Minimum VRAM	Recommended	Notes
Ollama	8GB	16GB+	8GB limits you to small quantized models
llama.cpp	8GB	16GB+	Same as Ollama, but better multi-GPU support
vLLM	16GB	24GB+	Needs VRAM headroom for batching buffers

vLLM needs more VRAM than llama.cpp for the same model because it pre-allocates memory for its paging mechanism. A 14B Q4_K_M model that fits in 12GB under llama.cpp may need 16GB under vLLM.

Which tool should YOU use?

New to local LLMs, just want to run models? Use Ollama. Install in 30 seconds, download a model, start chatting. No config needed.
Want maximum speed on your personal setup? Use llama.cpp directly. The extra tokens-per-second adds up over long sessions. Worth it if you know what you're doing.
Building an LLM API for a team or app? Use vLLM. PagedAttention batching makes it the only practical choice for multi-user workloads. Ollama and llama.cpp do not scale to concurrent users efficiently.
Running on AMD or Apple Silicon? Use Ollama or llama.cpp. vLLM's AMD support is incomplete. Ollama is the easiest path on macOS.
Need to run very large models across multiple GPUs? llama.cpp with tensor split gives you the most control over layer distribution. vLLM handles multi-GPU better for serving workloads.

Common mistakes to avoid

Using vLLM for personal single-user inference. vLLM's advantages are for concurrent requests. For a single user, llama.cpp is faster with less overhead and complexity.
Using Ollama for production serving. Ollama is a personal tool. It handles one request at a time without batching. Under load from multiple users, it becomes a bottleneck immediately.
Assuming all three tools run identical models. Ollama and llama.cpp use GGUF quantized models. vLLM uses HuggingFace format with GPTQ or AWQ quantization. The model files are different — you can't swap them.
Forgetting vLLM's CUDA requirement. People coming from Ollama on AMD sometimes assume vLLM will work the same way. It won't. Check hardware compatibility before planning a production vLLM deployment.

Final verdict

You are...	Use this	GPU needed
Personal daily user	Ollama	8GB+ any vendor
Power user, max speed	llama.cpp	8GB+ any vendor
Serving to a team	vLLM	16GB+ NVIDIA only
Building a product	vLLM	24GB+ NVIDIA

All three tools are excellent. Ollama for getting started, llama.cpp for squeezing performance, vLLM for scaling to users. If you are weighing Ollama against a GUI-first alternative, our LM Studio vs Ollama comparison shows how the two tools differ on GPU utilization, model loading, and ease of setup for non-technical users.

For GPU-specific Ollama advice, see our best GPU for Ollama guide. Optimizing your Ollama configuration? Check how to choose a GPU for Ollama. For production vLLM deployments, see best GPU for vLLM. If you are sizing hardware for a dedicated, always-on inference box rather than a personal workstation, our best GPU for an LLM server guide covers the throughput, ECC, and 24/7 thermals math.