AI Accelerator Benchmarks

Comparing AI hardware requires understanding benchmark methodologies, real-world performance metrics, and cost-efficiency analysis.

MLPerf

MLPerf is the industry-standard benchmark suite for AI hardware, maintained by MLCommons. It provides apples-to-apples comparisons across hardware platforms:

• MLPerf Training: Measures time to train models to target accuracy (ResNet-50, BERT, GPT-3, Stable Diffusion)
• MLPerf Inference: Measures throughput and latency for serving models
• Categories: Closed (strict rules for fair comparison) and Open (any optimization allowed)

Key Performance Metrics

Throughput vs Latency

These two metrics often conflict:

• High throughput: Process as many requests as possible (batch processing, offline analysis). Favor large batches, high GPU utilization.
• Low latency: Respond to each request as fast as possible (chatbots, real-time systems). Favor small batches, fast individual inference.
• Trade-off: Larger batch sizes improve throughput but increase latency for individual requests. Continuous batching (used in vLLM, TGI) helps balance both.

Cost Analysis

Raw performance is only part of the picture. Total cost of ownership matters:

Running Your Own Benchmarks

import torch
import time

def benchmark_matmul(size=4096, dtype=torch.float16, warmup=10, runs=100):
    a = torch.randn(size, size, device="cuda", dtype=dtype)
    b = torch.randn(size, size, device="cuda", dtype=dtype)

    # Warmup
    for _ in range(warmup):
        torch.mm(a, b)
    torch.cuda.synchronize()

    # Benchmark
    start = time.perf_counter()
    for _ in range(runs):
        torch.mm(a, b)
    torch.cuda.synchronize()
    elapsed = time.perf_counter() - start

    flops = 2 * size**3 * runs
    tflops = flops / elapsed / 1e12
    print(f"Matrix size: {size}, TFLOPS: {tflops:.1f}")

benchmark_matmul()