H100 vs MI300X — which GPU should I choose?

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.

How much VRAM do I need for LLM inference?

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.

Should we buy GPUs or use cloud GPU instances?

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.

What is the cheapest way to rent an H100 GPU?

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.

What GPU is best for LLM training in 2026?

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.

Best GPU for AI Video Generation in 2026

AI video generation requires 10–100× more compute than image generation. Modern video diffusion models (Sora-style DiTs, WAN 2.1, Mochi) at 1080p/30fps demand high-end GPUs with substantial VRAM and compute headroom.

TL;DR

For video generation: H200 for the best performance-availability balance. B200 if you can get access. H100 for budget-conscious production. MI300X for maximum VRAM on a budget.

TOP 4 GPUS RANKED

NVIDIA H200 SXM

NVIDIATOP PICK

Best balance of speed and availability for video

Memory

141GB HBM3e

FP8 TFLOPS

3,958 TFLOPS

TDP

700W

Cloud Cost

~$4.50/hr

Pros

+141GB fits large video DiT models without tensor parallelism
+4.8 TB/s — fast enough for real-time video decoding
+Full TensorRT-LLM/FP8 support for inference acceleration
+Widely available on Lambda, CoreWeave, Azure

Cons

−$4.50/hr makes long video generation expensive
−B200 generates video 2–3× faster for same model

Full Specs →Compare →

NVIDIA B200

NVIDIA

Fastest available — 2–3× H100 for video

Memory

192GB HBM3e

FP8 TFLOPS

4,500 TFLOPS

TDP

1000W

Cloud Cost

~$8–12/hr

Pros

+4,500 FP8 TFLOPS — fastest commercial GPU available
+192GB handles multi-resolution video pipelines
+NVLink 5.0 for fast multi-GPU video generation clusters
+Best for commercial video generation services ($/video)

Cons

−Limited cloud availability in early 2026
−High hourly cost — only economical at scale

Full Specs →Compare →

NVIDIA H100 SXM5

NVIDIA

Proven for video production at reasonable cost

Memory

80GB HBM3

FP8 TFLOPS

3,958 TFLOPS

TDP

700W

Cloud Cost

~$2.50–3.50/hr

Pros

+Best cost-performance for 480p–720p video generation
+Widest availability on all major clouds
+Strong TensorRT and FP8 optimization for diffusion
+~2–3 min per 10-second clip at 720p (WAN 2.1)

Cons

−80GB limits 1080p+ video without tensor parallelism (2× H100)
−Slower than H200 for 1080p+ resolution

Full Specs →Compare →

AMD Instinct MI300X

AMD

Large VRAM for budget-conscious video teams

Memory

192GB HBM3

FP8 TFLOPS

2,614 TFLOPS

TDP

750W

Cloud Cost

~$3.20/hr

Pros

+192GB fits the largest video DiT models
+Lower cost than H200 with comparable VRAM
+Good for research and experimentation at scale
+PyTorch + diffusers ROCm backend supports video models

Cons

−Video diffusion kernel optimizations lag behind CUDA
−Commercial video generation frameworks less tested on ROCm

Full Specs →Compare →

KEY FACTORS TO CONSIDER

Resolution and duration scale compute quadratically

Doubling video resolution 4× compute requirements. Going from 5-second to 20-second clips 4× the compute. A 10-second 1080p video at 24fps using WAN 2.1 takes ~3–5 min on H100 vs ~8–12 min on A100. B200 cuts this to ~1–2 min.

Multi-GPU scaling for video generation

Video DiTs parallelize well across 2–8 GPUs using sequence parallelism. Two H100s (160GB total) generate 1080p video faster than a single MI300X despite MI300X having more total VRAM, due to H100's faster FP8 compute.

VRAM limits maximum resolution and duration

A 10B parameter video DiT at FP16 needs ~20GB just for weights. The attention maps for 1080p/24fps video fill 60–100GB+ depending on implementation. 80GB H100 requires careful memory management; 141GB H200 or 192GB MI300X/B200 have more headroom.

FREQUENTLY ASKED QUESTIONS

How long does it take to generate a 10-second video with AI on H100?

With WAN 2.1 at 720p/24fps, approximately 2–4 minutes on a single H100 SXM5 using FP16 with 50 inference steps. At 1080p: 5–10 minutes. With 2× H100 tensor parallel: roughly 2× faster. B200 generates the same video in ~45–90 seconds.

What GPU do I need to run Sora / commercial video generation?

OpenAI Sora uses clusters of H100/H200 GPUs. For running open-source equivalents (WAN 2.1, Mochi, Hailuo): a single H100 handles 720p well; H200 for 1080p. For a commercial service generating 100+ videos/day, budget for 8–16× H100s or equivalent.

Is MI300X good for video generation?

Acceptable for research and experimentation. Production video generation services almost exclusively use NVIDIA due to TensorRT optimizations and broader framework support. ROCm support for video models is improving but lags behind CUDA in 2026.

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