Choosing the Right GPU for Training and Inference (www.buysellram.com)

NVIDIA’s GPU roadmap — from Volta and Ampere to Hopper and the new Blackwell family — is now the central decision point for AI teams. The guidance: match the architecture to the workload rather than always buying the newest chip. Ampere/A100 remains a cost-effective, mature choice for traditional CNN/RNN training and mixed workloads; Hopper (H100/H200) and Blackwell (B100/B200/GB200) are increasingly essential for large transformer and generative models because of their advanced Tensor Cores, support for low-precision formats (FP8 and, on Blackwell, FP4), higher HBM capacity/bandwidth, and the Transformer Engine that dynamically manages precision for speed and stability. Technically, HBM capacity and bandwidth are now often the primary bottleneck for LLMs, and NVIDIA’s full-stack optimizations (CUDA-X, Transformer Engine, FlashAttention) are as important as raw silicon for real-world throughput. Features like MIG for fractional GPU tenancy, NVLink for high-bandwidth multi-GPU scaling, and FlashAttention’s reduced memory I/O materially affect utilization and cost. Practically, Ampere and Hopper are broadly available and mature; Blackwell is ramping through 2025 but faces high demand and price pressure. For many teams, cloud offerings provide flexibility to access the latest architectures and avoid rapid CapEx obsolescence, while on-premise can still win for predictable, long-term heavy workloads.