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 $6.88/hr (AWS on-demand). Reserved instances and spot pricing offer 30-60% savings. CoreWeave and Lambda typically offer the lowest on-demand rates. See gpuadvisor.com/cloud-pricing for regularly tracked 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.

Question 6

How do I benchmark a GPU vendor quote?

Accepted Answer

Paste your vendor quote into GPUAdvisor's Quote Benchmarker at gpuadvisor.com/quote-benchmark. It compares your rate against the p25–p75 market range across 10+ cloud providers and shows how much you could save by switching.

Question 7

What is the price difference between CoreWeave and AWS for H100?

Accepted Answer

As of 2026, CoreWeave charges approximately $2.39/GPU-hr for H100 80GB on-demand while AWS p4d.24xlarge works out to roughly $6.88/GPU-hr — nearly 3x the cost. See gpuadvisor.com/cloud-pricing for the current tracked rates.

Question 8

What cloud providers does GPUAdvisor track?

Accepted Answer

GPUAdvisor regularly tracks GPU pricing from CoreWeave, Lambda Labs, AWS, Crusoe, Hyperstack, RunPod, Google Cloud, Azure, and more — 10+ providers covering 55+ GPU listings including H100, H200, B200, A100, MI300X, MI355X, and L40S.

Question 9

What is a GPU TCO calculator?

Accepted Answer

A GPU Total Cost of Ownership (TCO) calculator estimates the full cost of running a GPU cluster including compute, power, cooling, networking, and personnel. GPUAdvisor's TCO calculator at gpuadvisor.com/tco-calculator compares cloud vs on-premise costs over 1–3 year horizons.

Question 10

H100 vs B200 — which is better for AI workloads?

Accepted Answer

B200 delivers 4-5x the training throughput of H100 and 3x inference performance, but costs significantly more and has limited cloud availability in 2026. H100 remains the most cost-effective option for most production AI workloads with broad cloud provider availability. See gpuadvisor.com/compare for regularly tracked pricing between H100 and B200 instances.

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