Qwen2.5-72B-Instruct-AWQ GPU Requirements: VRAM & Cheapest GPU

Qwen2.5-72B-Instruct-AWQ has about 73.0B parameters. At FP16 it needs roughly 159 GB of VRAM for inference, including weights, activations, and KV cache. Quantized to INT4 that drops to around 40 GB. Leave about 10% headroom for production traffic.

For FP16 inference, the cheapest fit on Spheron is 2x A100 80GB at about $1.64/hr. If you quantize to INT4, A100 80GB at about $0.82/hr runs it for less. Pricing is aggregated live from 5+ data center partners with per-minute billing.

LoRA fine-tuning adds roughly 50% on top of inference memory, so it usually fits the same class of GPU. Full fine-tuning holds gradients and optimizer state and needs about 4x the inference VRAM, which often means multiple GPUs for Qwen2.5-72B-Instruct-AWQ. The VRAM matrix above shows the exact estimate for each setup.

We read the parameter count directly from the model's safetensors metadata on HuggingFace, then estimate peak VRAM from weights, activations, KV cache, and framework overhead at your chosen precision. The estimate lands within about 15% of real-world use for most transformer models.

Quantized to INT4, Qwen2.5-72B-Instruct-AWQ needs about 40 GB of VRAM, so it is too large for a single 24 GB RTX 4090 and needs a bigger card or multiple GPUs. At FP16 it needs roughly 159 GB, which usually means a data center GPU. The precision picks above list the cheapest GPU that fits each setup.