AI Hardware Benchmarking & Performance Analysis

Quick Overview

The B200 GPU significantly outperforms the H100 in raw performance metrics like tokens per second, achieving roughly double the output volume, but this raw speed comes at a much higher cost, resulting in the H100 still offering a superior cost-per-token efficiency for general-purpose inference work, especially when leveraging software optimizations like lower precision modes.

Key Points: The B200 hits a peak throughput of 92.9K tokens per second, approximately double the H100's 44.7K tokens/sec on the same benchmark. The cost-per-token for the B200 is significantly higher, costing $5.50/hour versus the H100's $1.68/hour. Despite the B200's raw speed advantage, the H100 is still the most cost-efficient choice for general-purpose LLM serving due to its superior cost-per-token ratio ($0.27/million tokens for H100 vs $1.29/million tokens for B200). Software optimizations, particularly using lower precision modes like FP4 with frameworks like TensorRT-LLM, are crucial for maximizing performance on the B200. The paper demonstrates an end-to-end latency improvement for the B200 over the H100, especially when running concurrent users. The H100 maintains a strong position as the cost-efficient champion for many common use cases, even with the B200's raw speed advantage. The paper highlights the trade-off between raw speed (throughput) and latency/cost efficiency, suggesting hardware choice depends on the specific business need (e.g., latency-sensitive chat vs. high-volume batch jobs).

Context: This podcast segment from Really Easy AI discusses the performance benchmarking and cost analysis of new AI hardware, specifically comparing the newer B200 GPU against the established H100. The analysis focuses on key metrics like throughput (tokens per second), latency, and cost-per-token to determine the most economically viable infrastructure for running large language models (LLMs) like Llama 3.3 70B.

Detailed Analysis

The discussion analyzes the performance trade-offs between the B200 and H100 GPUs for running LLMs, specifically Llama 3.3 70B. The B200 delivers significantly higher raw throughput, peaking at 92.9K tokens per second compared to the H100's 44.7K tokens per second, effectively doubling the output volume. However, the cost differential is dramatic: the B200 costs $5.50 per hour versus the H100 at $1.68 per hour. This translates to a cost-per-token of $1.29 million tokens for the B200 versus $0.27 per million tokens for the H100, making the H100 vastly more cost-efficient for general-purpose inference. The H100 is described as the undisputed performance king for cost efficiency, offering similar performance to the B200 when running on the older VLLM framework when utilizing aggressive low-precision optimizations like FP4. The paper suggests that engineering effort spent on optimizing software (like using TensorRT-LLM) alongside hardware selection is critical, as raw speed does not always equate to the best total cost of ownership or user experience, especially when factoring in latency for real-time applications versus batch throughput.