NVIDIA H20 96GB vs NVIDIA GH200 144GB HBM3e
The GH200 delivers 6.7x the FP8 throughput of the H20 (1,979 vs 296 TFLOPS dense). The GH200 also carries 48 GB more memory (144 GB vs 96 GB).
NVIDIA H20 96GB
Full specs →NVIDIA GH200 144GB HBM3e
Full specs →Specifications
Performance (TFLOPS)
FLOPS by Precision
What actually differs
Both GPUs launched in 2024: the H20 on Hopper and the GH200 on Hopper.
Dense throughput for the H20 against the GH200: FP64 1 vs 34 TFLOPS, FP32 44 vs 67 TFLOPS, FP16 148 vs 990 TFLOPS, FP8 296 vs 1,979 TFLOPS. Sparse figures, where the vendor publishes them, appear under each dense number in the performance table.
Memory is 96 GB against 144 GB, fed at 4.0 TB/s versus 4.9 TB/s. More memory per GPU means larger models fit before you have to shard across cards, which often matters more than raw TFLOPS for inference.
Power budgets are 400 W for the H20 and 1.0 kW for the GH200. At FP32 that works out to 0.11 against 0.07 TFLOPS per watt.
For LLM training and inference, weight the FP8 and FP16 rows and memory capacity most heavily. For scientific and HPC workloads, the FP64 row is the one to read.
Frequently asked questions
Is the H20 faster than the GH200?
At FP8 precision the GH200 reaches 1,979 TFLOPS dense against 296 TFLOPS for the H20. The performance table on this page lists every published precision for both GPUs.
Which has more memory, the H20 or the GH200?
The GH200 carries 144 GB of memory versus 96 GB for the H20. Memory bandwidth is 4.0 TB/s for the H20 and 4.9 TB/s for the GH200.
How much power do the H20 and the GH200 draw?
The H20 is rated at 400 W TDP and the GH200 at 1.0 kW. On FP32 throughput per watt, the H20 is the more efficient part.
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