Can You Run Qwen 2.5 Coder 32B on NVIDIA A100 80GB SXM4?

The Qwen 2.5 Coder 32B (32.5B Dense) contains approximately 32.5 billion parameters. At FP16 (2 bytes per parameter), the raw weight matrix occupies 65 GB. With a 15% CUDA kernel overhead factor, effective VRAM for weights alone is 65 GB. The NVIDIA A100 80GB SXM4 provides 80 GB HBM2e of memory, leaving a headroom of 15 GB for KV-cache and activation tensors.

At INT8 quantization (1 byte per parameter), the weight footprint drops to 33 GB, requiring a tensor parallel degree of approximately 1× across NVIDIA A100 80GB SXM4 instances. The estimated inference throughput is ~42 tok/s (FP16, batch=1), which translates to a cost-per-million-output-tokens of roughly $2.00 at current spot rates. For production deployments, vLLM (INT8 or FP16, no FP8 support) with PagedAttention is the recommended serving stack.