Four engines dominate LLM inference in 2026: vLLM, Ollama, llama.cpp, and Text Generation Inference (TGI). Each targets a different use case, and picking the wrong one means either leaving performance on the table or fighting unnecessary complexity. This guide compares them across every dimension that matters for real deployments.

Quick comparison

	vLLM	Ollama	llama.cpp	TGI
Best for	Production API serving	Local development	CPU/edge/embedded	HuggingFace ecosystem
Language	Python	Go	C++	Rust
Throughput	Very high (100+ QPS)	Medium (10-50 QPS)	Low-medium (5-30 QPS)	High (80-150 QPS)
Ease of use	Moderate	Easiest	Easy-moderate	Hard
GPU required	Yes	Optional	No	Yes
Batching	Continuous batching	Basic batching	Single request	Continuous batching
Quantization	GPTQ, AWQ, FP8	GGUF (via llama.cpp)	GGUF (Q2-Q8)	GPTQ, AWQ, EETQ
OpenAI-compatible API	✅	✅	✅ (with server)	✅

vLLM — Production throughput king

vLLM is the go-to choice for serving LLMs at scale. Its PagedAttention algorithm manages GPU memory like an operating system manages RAM — dynamically allocating and freeing attention key-value cache blocks. This eliminates memory waste and enables significantly higher throughput than naive implementations.

Strengths: Highest throughput for concurrent requests, continuous batching, tensor parallelism across multiple GPUs, prefix caching for repeated prompts, and structured output support via guided decoding. For a deep dive, see our guide on serving LLMs with vLLM.

Weaknesses: Requires GPU (no CPU inference), Python-based so startup is slower, higher memory overhead for single requests, and configuration complexity for advanced features. Not ideal for development where you’re frequently switching models.

Best for: Production API endpoints serving multiple users, batch processing pipelines, and any scenario where throughput matters more than simplicity.

Ollama — Developer experience first

Ollama wraps llama.cpp in a Docker-like experience. Pull a model with one command, run it immediately, switch between models effortlessly. It’s the fastest path from zero to running a local LLM.

Strengths: One-command setup (ollama run llama3), automatic model management, built-in model library, OpenAI-compatible API, runs on CPU or GPU, cross-platform (Mac, Linux, Windows). Perfect for developers who want local AI without infrastructure knowledge.

Weaknesses: Lower throughput than vLLM for concurrent requests, limited batching, fewer configuration options for production tuning, and the abstraction layer adds overhead. Not suitable for high-traffic production deployments.

Best for: Local development, prototyping, personal coding assistants, and small team deployments where simplicity matters more than maximum throughput.

llama.cpp — Maximum portability

llama.cpp is a pure C++ implementation of LLM inference with no dependencies. It runs anywhere — laptops, Raspberry Pis, phones, servers without GPUs. Its GGUF quantization format enables running large models on limited hardware by reducing precision.

Strengths: No GPU required, runs on any hardware, smallest memory footprint with aggressive quantization, fastest single-request latency on CPU, active community with rapid model support, and the GGUF format has become the standard for quantized models.

Weaknesses: Lower throughput than GPU-accelerated engines for concurrent requests, no continuous batching in the base implementation, and quantization reduces output quality (though Q5/Q6 is nearly lossless). For coding models running locally, the quality tradeoff is usually acceptable.

Best for: Edge deployment, CPU-only servers, embedded systems, offline use cases, and running models on consumer hardware without a dedicated GPU.

TGI — HuggingFace native

Text Generation Inference is HuggingFace’s production inference server, written in Rust for performance. It integrates tightly with the HuggingFace ecosystem — any model on the Hub works with minimal configuration.

Strengths: Native HuggingFace Hub integration, Rust performance, flash attention support, continuous batching, watermarking for generated text, and grammar-based structured output. Well-suited for organizations already invested in the HuggingFace ecosystem.

Weaknesses: Steeper learning curve, requires GPU, Docker-based deployment adds complexity, and community is smaller than vLLM’s. Configuration is more involved than alternatives.

Best for: Organizations using HuggingFace infrastructure, batch processing workloads, and deployments that need watermarking or specific HuggingFace integrations.

Performance benchmarks

Testing with Llama 3 70B on 4x A100 80GB GPUs, serving 50 concurrent users:

Engine	Tokens/sec (total)	P50 latency	P99 latency	GPU utilization
vLLM	4,200	45ms/tok	120ms/tok	92%
TGI	3,100	55ms/tok	180ms/tok	85%
Ollama	1,800	90ms/tok	350ms/tok	70%
llama.cpp	1,200	130ms/tok	500ms/tok	65%

For single-user latency on consumer hardware (RTX 4090), the gap narrows significantly. Ollama and llama.cpp are within 10-15% of vLLM for single requests.

For more performance comparisons, see our SGLang vs vLLM benchmark.

Decision framework

Choose vLLM if: You’re serving multiple users in production, need maximum throughput, have GPU infrastructure, and can handle moderate setup complexity.

Choose Ollama if: You want the fastest setup, are developing locally, need to switch between models frequently, or are building a prototype that might not need production-scale serving.

Choose llama.cpp if: You need CPU inference, are deploying to edge devices, want minimal dependencies, or need to run models on hardware without a dedicated GPU.

Choose TGI if: You’re deeply integrated with HuggingFace, need text watermarking, or your team already knows the HuggingFace deployment stack.

Can you switch later?

Yes. All four engines support the OpenAI-compatible API format, so your application code doesn’t need to change when you switch engines. Start with Ollama for development, deploy with vLLM for production. The model weights are the same (or convertible) — only the serving infrastructure changes.

Verdict

Most teams should use Ollama for development and vLLM for production. This gives you the best developer experience during building and the best performance when serving users. Add llama.cpp if you have edge deployment requirements, and consider TGI if you’re already in the HuggingFace ecosystem.

FAQ

Which inference engine is fastest?

vLLM delivers the highest throughput for concurrent requests thanks to PagedAttention and continuous batching — typically 2-3x more tokens per second than alternatives under load. For single-user latency on GPU, all engines are within 10-15% of each other. For CPU inference, llama.cpp is fastest due to its optimized C++ implementation and GGUF quantization.

Which is easiest to set up?

Ollama is by far the easiest. Install with one command, pull a model with ollama pull llama3, and run it with ollama run llama3. No Python environments, no Docker, no configuration files. It works on Mac, Linux, and Windows out of the box. llama.cpp is second-easiest — download a binary and a GGUF model file, then run.

Can I use vLLM for development?

You can, but it’s not ideal. vLLM has slower startup times, requires GPU, and is optimized for throughput rather than quick iteration. For development, Ollama provides a better experience — faster model switching, simpler configuration, and lower resource usage. Use vLLM when you’re ready to benchmark production performance or test concurrent request handling.

Which supports the most models?

llama.cpp (and by extension Ollama, which uses it internally) supports the widest range of models through the GGUF format. Nearly every open-source model gets a GGUF conversion within days of release. vLLM supports most popular models but occasionally lags on newer architectures. TGI supports anything on HuggingFace Hub but may need custom configurations for unusual architectures.