GPU Buying Guide for LLMs: RTX 5090 vs H100 vs H200 Complete Comparison (2026)

Choosing a GPU for large language models (LLMs) necessitates a focus on memory bandwidth and VRAM capacity rather than sheer compute power, with consumer cards often outperforming expensive workstation GPUs. The guide evaluates different GPU tiers, from budget consumer cards to high-end datacenter accelerators, emphasizing VRAM as the critical constraint for running models, with specific quantization methods reducing VRAM requirements. Memory bandwidth is identified as the primary determinant of inference speed, with consumer GPUs like the RTX 5090 offering notable performance for local LLM use. Professional workstation GPUs provide enterprise features but are less cost-effective for LLMs compared to consumer options. Datacenter GPUs, such as the H100 and H200, are designed for large-scale AI tasks but often make more sense in cloud-based scenarios due to cost and utilization considerations. Apple Silicon's unified memory allows running large models that exceed the VRAM of traditional GPUs, offering silent and energy-efficient operation, albeit with slower performance. The decision to buy or use cloud services depends on factors like model size, budget, and utilization needs, with cloud solutions frequently preferred for flexibility and cost efficiency.