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There is no universal winner. Choose the accelerator that runs your specific model, request pattern and service-level objectives at the lowest practical total cost. Define the workload first, reject chips that cannot fit the model or support its operators, then run a matched benchmark that measures latency, throughput, scaling and capacity—not just a vendor’s peak score.
Start with the inference workload, not the chip category
A GPU and a custom inference chip can produce very different results on the same model because production traffic is not a single, fixed test. Prompt length, generated output, concurrency, batching, precision and latency targets all change which hardware is efficient. Write down those variables before looking at accelerator specifications.
- Model: architecture, parameter count, number of layers, mixture-of-experts routing and any custom operators.
- Precision and quantization: for example FP16, FP8 or INT4. A lower precision can reduce memory use and increase throughput, but only if the resulting accuracy is acceptable and the accelerator supports the required kernels.
- Request shape: distribution of prompt tokens and output tokens, including long-context or unusually large requests.
- Traffic: average and peak requests per second, concurrency, burstiness and the amount of batching the application can tolerate.
- Service objectives: time to first token (TTFT), end-to-end latency, per-token generation speed, tail-latency limits and availability targets.
Two services using the same model may rationally select different hardware. An interactive assistant with a strict TTFT objective behaves differently from an offline summarization queue that maximizes tokens per second. Capacity planning must use the production distribution, not an average request that hides the expensive tail.
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Gate 1: prove model and operator fit
Memory capacity is a hard constraint
Before comparing price or benchmark charts, verify that the model and runtime fit in accelerator memory. Account for weights, temporary activations, attention key-value cache, compiler workspaces, communication buffers and the memory required by your serving framework. A model that barely fits at idle may fail as concurrency rises because the key-value cache grows with active sequences.
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Evaluate memory separately for each precision. FP16, FP8 and INT4 have different weight footprints and may require different kernels or conversion steps. Quantization can make an otherwise impossible deployment feasible, but benchmark the exact quantized model you intend to serve; results from FP16 cannot be carried over to INT4.
List unsupported operations and porting work
Read the accelerator’s compiler, runtime and framework documentation for every operator in the model. Identify custom CUDA or other device-specific code, dynamic shapes, sparse operations, control flow and post-processing that may fall back to a CPU. A nominally supported framework is not proof that your complete model will compile or that all layers will execute on the accelerator.
Record the engineering work needed for conversion, graph partitioning, kernel replacement, calibration, debugging and continuous integration. A lower hourly instance price can be erased by months of porting or by a fallback path that transfers tensors between host and device.
Check serving-stack compatibility
Confirm support for the inference server, batching policy, streaming responses, autoscaling, observability and model-update process you use in production. AWS documents Neuron integration with PyTorch and TensorFlow for its own accelerators; that is an AWS capability description, not an independent compatibility audit. Treat every vendor-supported combination as a starting point for your own validation.
Build a fair benchmark before comparing results
Hold the variables constant
Run the identical model version, precision, tokenizer, prompt and output-length distributions, concurrency levels, backend and serving configuration on every candidate. Keep software optimizations visible and documented: compiler version, driver or runtime version, batch limits, sequence limits, speculative decoding, caching and power mode.
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Public numbers are often incomparable. A result measured with short prompts, a different quantization or a specialized serving framework may look better than your deployment while representing a different workload. AWS specifically warns that aggregate throughput can conceal unacceptable TTFT, and that a good single-request TTFT can coexist with inadequate throughput.
Use layered tests
Google’s accelerator methodology recommends combining microbenchmarks, roofline analysis and complete model benchmarks. Use each layer for a different question:
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- Microbenchmarks: isolate matrix multiplication, attention, memory bandwidth, host-device transfers and collective communication. They explain a bottleneck but do not predict end-to-end service quality by themselves.
- Roofline analysis: compare the workload’s arithmetic intensity with compute and memory ceilings. A memory-bound model will not benefit from a higher theoretical FLOP rate unless its data movement also improves.
- Model benchmarks: run the actual graph and serving path with realistic prompts, outputs and concurrency. This is the evidence used for capacity planning.
Warm up the runtime, discard startup samples, repeat tests at each concurrency level and report variability. Measure both steady-state behavior and scale-out behavior if production will use multiple accelerators.
Measure latency and capacity together
“No single metric is sufficient on its own,” AWS Prescriptive Guidance notes. Track the following metrics in one results set:
| Metric | What it tells you | How to use it |
|---|---|---|
| Time to first token (TTFT) | Delay before a streaming response begins | Check median and tail values at the concurrency your SLO requires. |
| Generated tokens per second | Decode speed after generation starts | Important for user-perceived responsiveness and long answers. |
| End-to-end latency | Total request time, including queueing and post-processing | Compare with the actual deadline users or downstream systems enforce. |
| Concurrent requests | Number of in-flight requests the deployment sustains | Test the operating point, not only an uncongested single request. |
| Sustained requests per second | Capacity over a realistic interval | Run long enough to expose thermal throttling, queue growth and autoscaling delays. |
| Tail latency | Worst-case experience, commonly p95 or p99 | Use the percentile specified by your service objective; averages can hide overload. |
Plot throughput against latency as concurrency rises. The useful operating point is usually before the knee where queueing causes TTFT and end-to-end latency to increase sharply. Record error rates, out-of-memory events and rejected requests alongside performance.
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Compare candidates on the dimensions that affect deployment
| Axis | Questions to answer | Evidence required |
|---|---|---|
| Model and operator fit | Are all operators, dynamic shapes, precision modes and quantization paths supported? Is conversion required? | Successful compilation and correctness tests for the production model. |
| Memory fit | Do weights, runtime state, key-value cache and buffers fit at target concurrency? | Peak memory telemetry under the largest expected request mix. |
| Latency | What are TTFT, decode speed, end-to-end and tail latency at the required load? | Matched benchmark percentiles, not a vendor’s best-case sample. |
| Throughput and scaling | How many tokens and requests per second are sustained? What happens when chips are added? | Single-chip and multi-chip tests including communication overhead. |
| Cost and capacity | How many accelerators and instances are needed, including headroom? | Regional pricing and a cost model tied to the measured operating point. |
| Software and operations | How mature are compilers, runtimes, monitoring, autoscaling and rollback? | Deployment rehearsal and an ownership estimate for upgrades and incidents. |
| Power and cooling | What is sustained draw and thermal behavior in an owned data center? | Long-duration power and thermal measurements for the complete server. |
For owned infrastructure, Google’s benchmarking methodology includes sustained power and thermal testing. The available public material does not establish a current ownership-cost comparison between particular GPU servers and custom-chip systems, so measure your own configurations.
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The objective is not the highest absolute benchmark score. AWS describes the goal as identifying “the lowest-cost accelerator that satisfies the application’s service-level objectives (SLOs).” Calculate the cost of a deployment that actually meets those objectives:
- Determine peak and sustained request rate from production or a conservative forecast.
- Use benchmark throughput at the required TTFT and tail-latency limits, not the maximum throughput obtained after relaxing latency.
- Compute the accelerator and host count needed for peak load, then add explicit capacity headroom for failures, traffic bursts and maintenance.
- Include instance or lease cost, storage, networking, data transfer, monitoring, orchestration and engineering operations.
- For owned hardware, include power, cooling, rack space, support and depreciation.
- Divide total deployment cost by a useful unit such as requests, generated tokens or completed jobs, while retaining the SLO check as a pass/fail condition.
Prices vary by region and configuration. No live regional prices or current physical-server cost comparison are established here, so substitute the quotations and utilization assumptions for your deployment.
Understand what current custom-chip examples do—and do not—prove
| Accelerator | Published details | How to interpret the claim |
|---|---|---|
| AWS Inferentia2 | AWS lists 32 GB of HBM per chip and says Inferentia2 delivers up to 4× the throughput and up to 10× lower latency than first-generation Inferentia. EC2 Inf2 instances support distributed inference scale-out. | These are AWS-published comparisons with AWS’s prior Inferentia generation, not a GPU comparison. Validate your model, precision and serving stack. |
| Google Ironwood TPU | Google announced Ironwood on April 9, 2025, as its seventh-generation TPU and first designed specifically for inference. Google reports 192 GB HBM, 7.37 TB/s HBM bandwidth, 1.2 TB/s bidirectional ICI bandwidth and a peak 4,614 TFLOPs per chip, with 256-chip and 9,216-chip configurations. | These are dated Google specifications and vendor claims. They describe available scale and hardware characteristics, not a matched independent GPU result. |
Historical results illustrate why context matters. Jouppi and colleagues reported that the original TPU averaged about 15–30 times the speed and 30–80 times the TOPS per watt of contemporary CPU and NVIDIA K80 GPU systems on the paper’s Google data-center workloads in 2017. Those workloads and hardware generations do not predict a modern GPU-versus-ASIC outcome.
Account for multi-chip behavior and network costs
A model that fits on one device may require multiple devices after applying the chosen precision, batch size or context limit. Test tensor, pipeline or expert parallelism exactly as it will run in production. Measure synchronization time, all-reduce or collective bandwidth, host transfers and network contention. An accelerator with excellent single-chip numbers can lose its advantage when communication dominates.
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Include failure and maintenance behavior in the scale test. Determine whether one failed device drains the whole replica, whether workloads can be rescheduled without reloading large weights, and how autoscaling affects cold-start latency. Scale-out claims—such as the distributed inference support AWS documents for Inf2—still require validation with your topology and traffic pattern.
Choose GPUs when flexibility is the binding requirement
GPUs are often the safer choice when you need broad framework coverage, frequent model changes, custom kernels, rapid experimentation or one platform that serves many model families. Their advantage is operational optionality, not a guaranteed lower cost or latency. Confirm that the specific GPU memory size, precision mode and serving software meet your SLO; a flexible platform that misses TTFT targets is not a successful deployment.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a custom inference chip when efficiency survives the whole stack
A custom chip is a strong candidate when your model is stable, operators are supported, the compiler produces an efficient graph, and measured throughput or power efficiency lowers the cost of an SLO-compliant deployment. High memory bandwidth, specialized matrix engines or tightly coupled multi-chip links may matter for a specific workload. Treat vendor performance statements as candidate evidence until your matched test confirms them.
A repeatable selection procedure
- Document the request distribution. Record model version, parameter size, precision, prompt and output token distributions, peak rate, concurrency and latency percentiles.
- Set hard gates. Remove any candidate that cannot fit memory, execute required operators or support your serving framework without unacceptable porting.
- Build identical environments. Pin model artifacts, tokenizer, runtime, compiler, backend, batching and power settings. Log every version.
- Run representative tests. Warm up, then measure TTFT, generated tokens per second, end-to-end latency, tail latency, sustained requests per second, errors and memory at each concurrency.
- Explain bottlenecks. Use microbenchmarks and roofline analysis to distinguish compute, memory, transfer and communication limits.
- Test scale-out. Measure the number of chips and instances required at peak load, including network overhead and failure recovery.
- Price only passing configurations. Compare complete deployments with headroom; discard a cheap configuration that violates the SLO.
- Run a production trial. Replay anonymized traffic or a statistically equivalent load, observe tail behavior and validate monitoring, rollback and autoscaling.
- Re-test after material changes. Repeat when the model, quantization, runtime, instance type or traffic mix changes. A result for one request shape does not transfer automatically to another.
Common mistakes and how to correct them
Picking from peak FLOPs
Peak compute ignores memory stalls, unsupported operators and queueing. Replace the comparison with measured model throughput at the required latency.
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Using a single-request test
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Comparing unlike benchmarks
Different prompt lengths, output lengths, quantization or serving frameworks produce different work. Re-run both candidates with one harness and one workload file.
Forgetting runtime and porting cost
Include compiler integration, kernel development, debugging, monitoring and upgrade effort in the total-cost model. Document fallback paths and test numerical correctness after conversion.
Ignoring memory growth
Leave room for the key-value cache and runtime buffers at peak concurrency. Monitor peak, not average, allocation and test the largest supported context.
Assuming vendor claims are head-to-head evidence
Label the baseline, date and workload for every published figure. AWS’s Inferentia2 comparison is against first-generation Inferentia; Google’s Ironwood figures are specifications and vendor claims. Neither establishes superiority over a modern GPU for your service.
Bottom-line decision rule
Select the lowest-cost configuration that passes model compatibility, memory, TTFT, tail-latency, throughput, reliability and operational gates under a matched workload. Choose a GPU when flexibility or software coverage is more valuable than a specialized efficiency opportunity. Choose a custom chip when your stable model and serving path demonstrate a repeatable SLO-compliant advantage after engineering, scaling, power and capacity costs are included. Re-run the comparison whenever the model or traffic changes.
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