Where does the NVIDIA A100 sit on the inference hardware ladder?

The **NVIDIA A100** is a datacenter GPU released in 2020 as part of the Ampere generation. It ships in two HBM2e memory configurations: a 40 GB variant and a more common 80 GB variant. The 80 GB version delivers roughly 2 TB/s of memory bandwidth, which is what matters for LLM decode. For low-precision compute, the A100 supports BF16 and FP16 on tensor cores at the headline throughput, plus INT8 for quantized inference. What it does NOT support natively is FP8. FP8 tensor cores arrived with the Hopper H100 in 2022, so any workload that requires FP8 weights or activations runs simulated rather than native on A100. The A100 sits one generation behind the H100 and H200 (both Hopper) and two generations behind the B200 (Blackwell). It is still widely deployed in 2026, especially for cost-sensitive serving of mid-size models that fit comfortably in 80 GB and do not need FP8. The most common mistake is to assume that more recent means strictly better and skip A100 reflexively. For inference workloads where bf16 or int8 weights are enough, the A100 is often the cheapest viable GPU per token served, which is why secondary-market and on-prem fleets still lean on it.

**A100** is NVIDIA's Ampere datacenter GPU, launched in 2020. It is the direct predecessor of H100 and the workhorse of the 2020-2022 datacenter generation. Memory: HBM2e in 40 GB and 80 GB SKUs, with the 80 GB variant offering roughly 2 TB/s of memory bandwidth. For LLM decode, which is memory-bandwidth-bound, that bandwidth number is the headline spec. Compute: tensor cores support BF16, FP16, and INT8 natively. **FP8 is not supported**; it arrived with the Hopper H100 in 2022. Any FP8 path on A100 runs as emulation rather than native tensor-core throughput, which means weight-only INT8 quantization is the practical low-precision option for A100 deployments. Generation ladder: A100 (Ampere, 2020) -> H100 (Hopper, 2022) -> H200 (Hopper refresh with HBM3e) -> B200 (Blackwell, 2024) -> GB200 NVL72 (rack-scale Blackwell). Each step adds memory capacity, bandwidth, and lower-precision tensor-core formats. In 2026, A100 is still common for cost-sensitive serving of mid-size models that fit in 80 GB and do not need FP8. It is the default on-prem GPU for many enterprises whose fleet predates Hopper, and the secondary market is liquid enough that small teams can rent it cheaply for LLM workloads under 70B parameters.