NVIDIA A100 vs H100
The NVIDIA A100 and H100 are two of the most powerful GPUs on the market. Both GPUs are designed for AI and machine learning workloads, but there are some key differences between the two.
NVIDIA A100
Released: May 2020
CUDA cores: 6,912
Memory: 40GB HBM2e
Memory bandwidth: 1.6 TB/s
NVIDIA H100
Released: March 2023
CUDA cores: 16,896
Memory: 80GB HBM3
Memory bandwidth: 3.5 TB/s
Key differences
CUDA cores: The H100 has more than twice as many CUDA cores as the A100. This gives the H100 a significant advantage in terms of raw compute performance.
Memory: The H100 has twice as much memory as the A100. This is important for workloads that require large amounts of memory, such as training large language models.
Memory bandwidth: The H100 has more than twice the memory bandwidth of the A100. This means that the H100 can move data around more quickly, which can improve performance for workloads that are memory-bound.
Price: The H100 is more expensive than the A100, but it also offers significantly better performance.
Which GPU is right for you?
The right GPU for you depends on your specific needs and budget. If you need the best possible performance for AI and machine learning workloads, then the H100 is the way to go. However, if you're on a tighter budget, the A100 is still a very capable GPU.
As a premier integrator of GPUs and NVIDIAs partner rising star award winner, you can talk to
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NVIDIA A100 vs H100
The NVIDIA A100 and H100 are two of the most powerful GPUs on the market. Both GPUs are designed for AI and machine learning workloads, but there are some key differences between the two.
NVIDIA A100
Released: May 2020
CUDA cores: 6,912
Memory: 40GB HBM2e
Memory bandwidth: 1.6 TB/s
NVIDIA H100
Released: March 2023
CUDA cores: 16,896
Memory: 80GB HBM3
Memory bandwidth: 3.5 TB/s
Key differences
CUDA cores: The H100 has more than twice as many CUDA cores as the A100. This gives the H100 a significant advantage in terms of raw compute performance.
Memory: The H100 has twice as much memory as the A100. This is important for workloads that require large amounts of memory, such as training large language models.
Memory bandwidth: The H100 has more than twice the memory bandwidth of the A100. This means that the H100 can move data around more quickly, which can improve performance for workloads that are memory-bound.
Price: The H100 is more expensive than the A100, but it also offers significantly better performance.
Which GPU is right for you?
The right GPU for you depends on your specific needs and budget. If you need the best possible performance for AI and machine learning workloads, then the H100 is the way to go. However, if you're on a tighter budget, the A100 is still a very capable GPU.
As a premier integrator of GPUs and NVIDIAs partner rising star award winner, you can talk to