Ollama vs vLLM vs TGI for Llama 3.3 70B Resume Matching

Frequently Asked Questions

01.Can Ollama serve Llama 3.3 70B for production resume-to-req matching

Ollama can serve Llama 3.3 70B reliably for a single recruiter querying one requisition at a time, since it wraps llama.cpp in a single-binary developer experience with over 178,000 GitHub stars. It has no continuous batching scheduler, so throughput does not scale efficiently once a staffing platform needs to score dozens of resumes against a requisition concurrently, which is where vLLM or TGI-class batching engines take over.

02.Why is Text Generation Inference no longer the default choice for new Llama 3.3 deployments?

Hugging Face placed Text Generation Inference into maintenance mode, accepting only minor bug fixes and documentation updates, and now directs new production deployments toward vLLM or SGLang instead. Teams evaluating TGI for a new Llama 3.3 70B resume-matching stack in 2026 are effectively choosing a frozen feature set over an actively developed one.

03.What GPU memory does Llama 3.3 70B need for staffing inference workloads?

Llama 3.3 70B carries 70 billion parameters at a native BF16 precision, which puts full-precision inference near 140GB of GPU memory before KV cache overhead, meaning a single 80GB H100 cannot hold the unquantized model alone. Most staffing deployments run a two-GPU H100 node or apply 8-bit or 4-bit quantization through bitsandbytes to fit resume-matching workloads on tighter hardware budgets.