Cloud Provider D (Academic Research Cluster)

At FP16 precision, a 70-billion-parameter model needs roughly 140 GB of GPU memory just for the weights, so it fits on two A100 80GB cards or four 40GB GPUs. With 4-bit quantization you can squeeze it onto a single 80GB card at some quality cost. Meta released Llama 3 in April 2024.

In the US, university researchers typically apply through ACCESS, the NSF-funded allocation program that succeeded XSEDE in 2022, or through their own campus HPC center. Startup or education allocations are easier to get than research allocations and are often enough for a benchmarking project.

Fine-tuning is much heavier than inference — even parameter-efficient methods like LoRA need multiple GPUs and substantial memory for a 70B model. Check your cluster's per-node GPU limits and queue policies, since some shared partitions cap the number of GPUs or hours per job.

Llama 3 is distributed under Meta's Llama 3 Community License, which permits research use and publishing results, with restrictions mainly around companies exceeding a large monthly user threshold. Read both the license and your university cluster's acceptable-use agreement to be safe.