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. Use GPUAdvisor inside Claude to compare live rates instantly.

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. You can also ask Claude directly if you have the GPUAdvisor plugin installed.

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. At 8 GPUs running 24/7, this difference amounts to approximately $168,000 per year.

Question 8

How do I add GPUAdvisor to Claude?

Accepted Answer

Visit gpuadvisor.com/connect, click 'Add to Claude', and follow the one-click setup. GPUAdvisor works as a Claude connector (requires Claude Pro or Max). No API key, no login, no code required — setup takes under 30 seconds.

Question 9

What cloud providers does GPUAdvisor track?

Accepted Answer

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

Question 10

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 11

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. Use GPUAdvisor to compare live pricing between H100 and B200 instances.

Insights

AWS GPU Prices Are Up in 2026 — What Teams Are Paying Now