Gaudi 2 vs RTX 3070

Memory capacity and bandwidth directly shape feasible batch sizes during model execution. The Gaudi 2 supplies 96 GB HBM2e at 2450 GB/s while the RTX 3070 supplies 8 GB GDDR6 at 448 GB/s. This difference permits substantially larger working sets on the Gaudi 2 before swapping or partitioning becomes necessary. On the RTX 3070 the published dense FP16 rating reaches 40.6 TFLOPS and the sparse FP16 rating reaches 81.3 TFLOPS. Its dense FP32 rating stands at 20.3 TFLOPS. The absence of published tensor figures for the Gaudi 2 precludes direct arithmetic comparison yet the memory subsystem metrics alone indicate different operating regimes for training and inference pipelines.

The Gaudi 2 suits deployments that require the 96 GB HBM2e capacity and 2450 GB/s bandwidth together with RoCE Ethernet connectivity. Workloads that exceed the 8 GB limit of the RTX 3070 benefit from the larger on board memory without immediate model sharding.

The RTX 3070 fits environments constrained to 220 W TDP and standard PCIe slots. Its published dense FP16 rating of 40.6 TFLOPS and dense INT8 rating of 162.6 TOPS support compact inference or fine tuning sessions where the 8 GB GDDR6 memory remains sufficient.