LLM Inference Latency: Why Your 7B Model Gets 15 tok/s on a T4 but 3,500 tok/s on an H100
This technical analysis explains the massive performance gap between NVIDIA T4 and H100 GPUs when running 7B parameter models, citing differences in FP16 compute power.
- H100 GPUs offer vastly superior FP16 compute performance compared to T4s.
- Inference latency for 7B models can vary by orders of magnitude based on hardware.
- Hardware choice is critical for achieving real-time LLM response speeds.
The post analyzes the drastic difference in tokens per second generated by a 7B model when running on different NVIDIA hardware. It highlights that a T4 produces roughly 15 tokens per second, whereas an H100 can reach up to 3,500 tokens per second.
The author attributes this disparity to the raw compute capabilities outlined in NVIDIA specifications. Specifically, the H100 offers 989 TFLOPS of FP16 compute compared to the T4's 65 TFLOPS, illustrating the hardware bottleneck in inference latency.
This comparison serves as a practical guide for developers to understand the infrastructure requirements for deploying responsive AI applications.
Understanding hardware specs helps in selecting the right GPU for inference workloads.
Highlights the cost-performance trade-off between consumer and enterprise GPUs.
- FP16
- A half-precision floating-point format used in AI to reduce memory usage and increase speed.
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