How to Choose AI Accelerators for Inference

Choosing an AI accelerator for data center inference workloads means matching hardware to your model’s characteristics, traffic patterns, and cost targets. This guide walks through the key technical and operational decisions—from profiling your workload to evaluating vendors—so you can pick an accelerator that balances latency, throughput, power, and total cost of ownership.

What You’ll Need

• Details about your inference workloads: model types (Transformer, CNN, etc.), batch sizes, and latency requirements.
• An estimate of current and projected inference traffic (requests per second or tokens per second).
• Power and cooling constraints for your data center racks.
• A budget range for hardware acquisition and operational costs.
• Access to vendor documentation or a test environment for benchmarking.

Step-by-Step

1. Profile your inference workload. Identify the model families you run (e.g., BERT, GPT, ResNet), their input sizes, and whether you need real-time responses or can tolerate batched processing. Measure the current latency and throughput on existing hardware to establish a baseline.
2. Define performance targets. Set concrete goals: p99 latency (e.g., under 50 ms for interactive requests), throughput (e.g., 10,000 inferences per second), and batch size that maximizes utilization without violating latency goals.
3. Determine the dominant bottleneck. Inference is often memory-bandwidth or compute-bound. For large models with high batch sizes, compute (TOPS) matters; for small models or low latency, memory bandwidth and latency per inference are critical.
4. Consider precision requirements. Check if your models can run in FP16, INT8, or even INT4 without accuracy loss. Accelerators with dedicated tensor cores or INT8 throughput will give a big speedup for quantized models.
5. Evaluate accelerator types. Compare GPUs, custom ASICs (like Google TPU, AWS Inferentia), and FPGAs. GPUs are versatile and software-rich; ASICs offer high efficiency per watt for specific workloads; FPGAs are flexible but harder to program.
6. Check software and framework support. Ensure the accelerator works with your inference serving stack (TensorRT, ONNX Runtime, vLLM, etc.). Look for mature compilers, quantization tools, and debugging utilities.
7. Benchmark with your own models. Run representative workloads on candidate hardware. Use realistic batch sizes and measure latency, throughput, power draw, and thermal behavior. Include warm-up and steady-state measurements.
8. Calculate total cost of ownership (TCO). Include hardware purchase, power consumption, cooling, maintenance, and the cost of the software licenses or cloud instances. Compare on a per-inference or per-token basis.
9. Plan for scaling and future needs. Consider whether the accelerator can be scaled across multiple nodes for larger models or higher traffic. Check the vendor’s roadmap for next-generation parts.
10. Make a decision and pilot. Choose the top candidate and run a limited production pilot for a few weeks, monitoring real-world performance, stability, and operational overhead before full deployment.

Common Mistakes

• Focusing only on raw TOPS. Peak operations per second don’t reflect real inference performance; memory bandwidth, latency, and software efficiency matter more.
• Ignoring batch size effects. Some accelerators shine at large batches but struggle with low-latency single-stream requests. Test both extremes.
• Overlooking power and cooling. A high-performance accelerator may require liquid cooling or extra rack space, raising facility costs.
• Skipping quantization. Running FP32 models on hardware designed for INT8 can leave performance on the table. Always evaluate quantized versions.
• Assuming one accelerator fits all. Mixed workloads may need a mix of hardware, or a CPU fallback for small requests.

Tips

• Use open-source benchmarking suites like MLPerf Inference for standard comparisons, but always supplement with your own workloads.
• Check the accelerator’s memory capacity—it must hold the largest model you plan to serve, with room for key-value caches in LLMs.
• Look for accelerators with good multi-tenant support if you’ll serve multiple models on one device.
• Consider the network interconnect (PCIe, NVLink, Ethernet) for distributed inference.
• Ask vendors for reference designs and case studies in your industry.

Troubleshooting

If the chosen accelerator underperforms in testing:

• Check software versions. Update drivers, compilers, and runtime libraries to the latest stable releases.
• Review model optimization. Ensure the model is correctly quantized and fused (e.g., layer fusion, operator fusion).
• Examine batch tuning. Try different batch sizes and dynamic batching strategies to find the sweet spot.
• Monitor thermal throttling. If the device heats up, performance may drop—improve cooling or reduce power limits.
• Test with a simpler model. If a basic model also underperforms, the issue is likely system-level (PCIe bandwidth, CPU bottlenecks).
• Compare with vendor benchmarks. If your results are far off, contact the vendor’s support with your exact configuration.

Frequently Asked Questions

What is the difference between training and inference accelerators?

Training accelerators are optimized for high throughput on large batches and support backward pass operations; inference accelerators prioritize low latency, energy efficiency, and often support reduced precision (INT8) more aggressively.

Can I use a GPU for inference instead of a dedicated ASIC?

Yes, GPUs are widely used for inference and offer flexibility across model types. ASICs may provide better price-performance for specific, stable workloads, but they are less flexible to changes in model architecture.

How important is memory bandwidth for inference?

Very important, especially for autoregressive models like LLMs where each token generation is memory-bound. High bandwidth reduces latency and improves throughput.

What does TOPS mean and should I rely on it?

TOPS (Tera Operations Per Second) is a peak compute metric. It is a rough indicator but doesn’t account for real-world efficiency, software overhead, or memory constraints. Use it as a starting point, not a decision-maker.

Should I choose a cloud accelerator or on-premises hardware?

Cloud offers flexibility and no upfront cost, but for steady, high-volume workloads, on-premises may be cheaper. Analyze your traffic patterns and TCO.

How do I handle very large models that don’t fit on one accelerator?

Use model parallelism or sharding across multiple accelerators, or consider memory-efficient inference techniques like quantization and pruning. Some accelerators support multi-chip packaging for larger memory pools.

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