Can You Run Llama 3.3 70B on NVIDIA H100 SXM5?

The Llama 3.3 70B (70.6B Dense) contains approximately 70.6 billion parameters. At FP16 (2 bytes per parameter), the raw weight matrix occupies 140 GB. With a 15% CUDA kernel overhead factor, effective VRAM for weights alone is 140 GB. The NVIDIA H100 SXM5 provides 80 GB HBM3 of memory, leaving insufficient headroom — quantization to INT8 or INT4 is mandatory for KV-cache and activation tensors.

At INT8 quantization (1 byte per parameter), the weight footprint drops to 70 GB, requiring a tensor parallel degree of approximately 1× across NVIDIA H100 SXM5 instances. The estimated inference throughput is ~120 tok/s (FP8, batch=1), which translates to a cost-per-million-output-tokens of roughly $2.00 at current spot rates. For production deployments, vLLM (PagedAttention + FlashAttention-3) with PagedAttention is the recommended serving stack.