Question 1

H100 vs MI300X — which GPU should I choose?

Accepted Answer

H100 if you need the broadest software ecosystem (CUDA, TensorRT, vLLM). MI300X if you need maximum VRAM (192GB vs 80GB) for large model inference. MI300X offers better $/TFLOP but NVIDIA's software stack is more mature.

Question 2

How much VRAM do I need for LLM inference?

Accepted Answer

A model needs ~2x its parameter count in GB for FP16 inference (70B model = ~140GB VRAM). With INT8 quantization ~70GB, INT4 ~35GB. A single H100 (80GB) runs 70B at INT8; MI300X (192GB) runs it at full FP16.

Question 3

Should we buy GPUs or use cloud GPU instances?

Accepted Answer

If running GPUs 60%+ of the time, on-premise ownership wins on 3-year TCO. Below 40% utilization, cloud is more cost-effective. Many enterprises use hybrid: owned hardware for baseline, cloud for peak demand.

Question 4

What is the cheapest way to rent an H100 GPU?

Accepted Answer

As of 2026, H100 cloud pricing ranges from $2.23/hr (Lambda, RunPod spot) to $4+/hr (AWS, Azure on-demand). Reserved instances and spot pricing offer 30-60% savings. CoreWeave and Lambda typically offer the lowest rates.

Question 5

What GPU is best for LLM training in 2026?

Accepted Answer

NVIDIA H200 SXM (141GB HBM3e) for proven clusters, B200 for next-gen 4-5x speedup over H100, or AMD MI300X (192GB) for budget-conscious teams. For JAX workloads, Google TPU v5p pods offer unmatched scale.

Hopper H200 SXM vs Hopper H100 SXM5

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Hopper H100 SXM5 — Best For

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