Compare · GPUs & AI chips
H200 SXM vs Trainium2
RANKED BY DENSE FP16/BF16 TENSOR TFLOPS PER CHIP
#8 VS #12
CHECKED 06 SEPT 2026
Side by side
Verdict
H200 SXM ranks #8 of 20 on Dense FP16/BF16 tensor TFLOPS per chip (0.990 PFLOPS); Trainium2 ranks #12 (667 TFLOPS).
Rank 08 of 20 · leads
Dense FP16/BF16 tensor TFLOPS per chip
0.990 PFLOPS
MakerNVIDIA
Date2024
Verified04 Sept 2026
Evidence
NVIDIA H200 product page, H200 SXM column: FP16/BF16 Tensor Core 1,979 TFLOPS. Footnote 2: "With sparsity." Dense is 1,979 / 2 = 989.5 TFLOPS (0.990 PFLOPS). 141 GB HBM3e. Same Hopper Tensor Core throughput as H100 SXM; H200's difference is memory.Rank 12 of 20
Dense FP16/BF16 tensor TFLOPS per chip
667 TFLOPS
MakerAWS
Date2024
Verified04 Sept 2026
Evidence
AWS Neuron Trainium2 Architecture page, compute table: each Trainium2 chip delivers "667 BF16/FP16/TF32 TFLOPS" dense; sparse counterpart is 2,563 TFLOPS and was not used. 96 GiB device memory. Trn2 instances are generally available (AWS announcement 3 Dec 2024).Try another pair
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Source Vendor product pages and official spec documents (AMD Instinct MI355X / MI350X / MI325X / MI300X / MI300A / MI250X / MI250 / MI210; NVIDIA…
Last checked 06 Sept 2026