Inference engines compared in new survey
A technical comparison profiled six leading LLM inference engines—vLLM, TGI, TensorRT-LLM, SGLang, llama.cpp, and Ollama—and highlighted Ollama’s strength for local deployments (including Mistral/DeepSeek) with remote API access, aligning with privacy-first on-device strategies reported. The piece frames the practical trade-offs engineers must weigh when choosing runtimes for local vs. cloud inference.
The n1n.ai survey by Nino (March 13, 2026) benchmarked Llama‑3‑class 70B models on H100/A100 and reported throughput ranges of vLLM 1,000–2,000 tok/s, TGI 800–1,500 tok/s, TensorRT‑LLM 2,500–4,000+ tok/s, SGLang “High–Very High,” llama.cpp 80–100 tok/s, and Ollama “Low–Med.” explore.n1n.ai The piece highlights vLLM’s PagedAttention and notes that vLLM v0.7.3 added automatic FP8 weight calibration for NVIDIA Hopper (H100) to reduce memory footprint and enable larger batches. explore.n1n.ai TensorRT‑LLM is characterized as NVIDIA’s high‑performance compiler that converts PyTorch models into optimized CUDA graphs and delivered the top throughput numbers in the report. explore.n1n.ai The survey’s comparison table lists Ollama as MIT‑licensed, “best for” cross‑platform local/edge prototyping, and reports its throughput as low–medium while calling out support for models like DeepSeek and Mistral in the tested stack. explore.n1n.ai Ollama’s official docs and blog explicitly list model support (gpt‑oss, Gemma, DeepSeek, Qwen) and document both local API and cloud/remote capabilities. docs.ollama.com Community guides show common remote access patterns for Ollama using SSH tunnels, Localtonet/ngrok, Cloudflare Tunnel or WireGuard VPN to expose the local API for distributed clients. localtonet.com n1n.ai frames the vendor/runtime decision as a trade‑off where TensorRT buys maximum tokens/sec on NVIDIA hardware, vLLM buys multi‑GPU memory efficiency and broader model support, and Ollama buys on‑device privacy with simpler prototyping and documented remote‑access workflows—trade factors the report says can influence multi‑million‑dollar infrastructure decisions. explore.n1n.ai
