Training and fine-tuning are memory-bound problems before they're compute-bound ones, and that's the main reason H100 remains the default GPU for LLM training in 2026. A model's optimizer state (Adam's momentum and variance terms) typically needs 4x the parameter count in memory on top of the weights themselves, so full fine-tuning of even a 7B model can need well over 100GB across GPUs before you've processed a single batch. H100's 80GB of HBM3 at 3,350 GB/s bandwidth (SXM5), combined with native FP8 training support, is why it's still the baseline target for PyTorch FSDP, DeepSpeed ZeRO and Megatron-LM. Full pretraining of large models needs many H100s networked over NVLink and InfiniBand; most teams fine-tuning an existing model use LoRA or QLoRA instead, which cuts trainable parameters by orders of magnitude and makes single-GPU or small-cluster fine-tuning realistic. H200 for LLM training reduces the sharding needed for models where 80GB genuinely isn't enough.
Training is dominated by a different bottleneck than inference: memory capacity during the backward pass, not just bandwidth during generation. Adam's optimizer state alone typically needs 4x a model's parameter count in memory, on top of the weights, gradients, and activations, which is why full fine-tuning of even mid-sized models can require sharding across multiple GPUs. H100's 80GB of HBM3 gives real headroom for LoRA and QLoRA fine-tuning on a single card, and its native FP8 support roughly doubles training throughput over BF16 once enabled, on top of being meaningfully faster than A100 per GPU at the same precision. For full pretraining or full fine-tuning of larger models, PyTorch FSDP or DeepSpeed ZeRO shard the model across a cluster of H100s connected via NVLink within a node and InfiniBand between nodes, trading added communication overhead for the ability to train models that don't fit on one card.
Training runs are often long-lived and data-residency sensitive, so where your cluster sits matters as much as the interconnect inside it. See H100 availability by country below.
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