Match each GPU generation to the quantization format that lights up its native tensor cores.

**Each NVIDIA generation lowers the precision floor of its tensor cores.** Hopper added native FP8 (two flavors: E4M3 for weights and forward, E5M2 for gradients). Blackwell added native FP4 on top of FP8. The match for each option: **H100 / H200 (Hopper) maps to FP8.** This is the production sweet spot for the Hopper generation. FP8 doubles BF16 throughput on the same hardware. The toolchain (TensorRT-LLM, vLLM, Marlin) is mature. Every modern serving stack on H100 defaults to FP8. **B200 / GB200 (Blackwell) maps to FP4.** This is the new native floor on Blackwell. FP4 doubles FP8 throughput again. Running FP8 on Blackwell still works (FP8 is supported), but you leave half the chip's headline throughput on the table. **INT4 weights via AWQ / GPTQ** is portable. These weight-only schemes pack the weights at 4 bits but keep activations at 16 bits (W4A16). The advantage is hardware portability: works on Hopper, Blackwell, and even older Ampere with minor adjustments. Useful when your deployment target spans GPU generations. **W8A8 (INT8 weight + INT8 activation)** is the pre-Hopper baseline. A100-era quantization. On H100 it runs but loses to FP8 because FP8 has wider dynamic range with similar throughput. **FP16 / BF16** is the unquantized reference. Use it for accuracy floor measurements and ablations, but it leaves half the throughput on Hopper and three-quarters on Blackwell.

**The matches map each GPU generation to its native low-precision floor.** Each NVIDIA data-center generation introduces a new lowest-precision tensor-core format, doubling throughput for compute-bound work compared to the previous floor. **Hopper (H100 / H200) to FP8.** Hopper's fourth-generation tensor cores added native FP8 in two variants: E4M3 (4 exponent + 3 mantissa, for weights and forward activations) and E5M2 (5 exponent + 2 mantissa, for gradients). Peak FP8 throughput on H100 SXM is 1980 TFLOP/s versus 990 BF16. The Transformer Engine software stack made FP8 mixed precision plug and play in 2023, and FP8 is the production default on Hopper. **Blackwell (B200 / GB200) to FP4.** Blackwell's fifth-generation tensor cores add native FP4 alongside the existing FP8 path. B200 hits roughly 4500 TFLOP/s FP8 and 9000 TFLOP/s FP4. The FP4 path is what unlocks Blackwell's headline 2-3x throughput claims over Hopper. Running FP8 on Blackwell is supported and accurate, but leaves the FP4 throughput unused. **INT4 weights (AWQ / GPTQ) as portable W4A16.** These weight-only quantization schemes pack weights at 4 bits and run activations at 16 bits. Tensor cores process the activations in BF16 or FP16; the weights are dequantized just in time before the matmul. Throughput is bandwidth-bound (weight bytes are 4x smaller, so reads are 4x faster) but compute stays at BF16 rates. Portable across GPU generations because the math runs in BF16, regardless of whether FP8 or FP4 hardware exists. **W8A8 as the pre-Hopper baseline.** INT8 weight + INT8 activation was the A100-era quantization story. On H100 it works but does not exploit the new FP8 path. FP8 maintains wider dynamic range than INT8 (the 4-bit exponent gives more headroom for outlier activations) with similar throughput, so FP8 dominates W8A8 on Hopper. **FP16 / BF16 as the reference.** Useful for measuring quantization accuracy loss and as a baseline for ablations.