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Start with the decision you actually need to make
An accelerator that is excellent for one workload can be a poor fit for another. Before testing hardware, write a one-page specification for the production job. Record the exact model and version, training or inference mode, sequence lengths, precision or quantization, batch sizes, concurrency pattern, quality target, latency service-level agreement (SLA), throughput target, expected cluster size, utilization assumption, deployment geography and operating horizon.
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Also state the procurement boundary. Are you comparing managed services, rented instances or owned servers? A cloud comparison must include instance, network and storage charges; an owned-cluster comparison must include hardware, networking, facility power, cooling, maintenance and amortization. A change in any of these assumptions can change the result, so publish them with every benchmark.
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- Inference: define the request mix, context lengths, concurrency, streaming behavior and maximum acceptable latency. A high peak rate that breaks the SLA is not useful capacity.
- Reliability: state the tolerance for retries, hardware faults, checkpoint recovery and pre-emption. These events belong in the useful-progress calculation.
Understand what you are comparing
GPUs
GPUs are programmable parallel processors with mature ecosystems for common AI frameworks, kernels and distributed-training libraries. Their broad support can reduce porting risk and make it easier to switch models, precisions or vendors. That flexibility may come with more general-purpose circuitry and a cost or power profile that is not optimal for a narrowly defined operation.
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Custom ASICs
An application-specific integrated circuit (ASIC) is designed around a narrower set of operations or a particular dataflow. It can deliver strong efficiency when the model’s tensor shapes, precision and software stack align with the design. The trade-off is less flexibility: unsupported operators, unusual tensor dimensions or immature compilers can require padding, custom kernels or model changes.
These are tendencies, not guarantees. A workload-specific measurement is the only defensible way to turn them into a buying decision.
Check model and software fit before measuring speed
For every candidate, create a fit checklist and record the maturity of each item:
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- Framework and exact version used by the production code.
- Supported operators, attention implementations, quantization formats and custom extensions.
- Compiler, graph-lowering and kernel versions, including known limitations.
- Collective communication, topology-aware libraries and storage/checkpoint integration.
- Monitoring, profiling, autoscaling and serving components.
- Numerical behavior and output quality at the chosen precision.
- Engineering hours needed to port, tune, validate and maintain the stack.
Report porting work as part of the result. A platform that becomes fast only after substantial kernel development has a different total cost from one that runs the model with existing, production-tested software. Do not describe a ported result as if hardware alone created the advantage.
Model geometry can dominate an ASIC result
Google Cloud’s benchmarking guidance gives a concrete example involving gpt-oss-120B: its attention head dimension is 64, while the described TPU design is optimized for matrix dimensions in multiples of 256. Padding the dimension can reduce tokens per second and utilization. A custom kernel may recover some performance, but it requires specialist expertise. Changing the model dimension to fit the accelerator invalidates existing weights and requires retraining. This is a finding about that model and geometry, not a universal weakness of every ASIC or TPU.
For your model, list hidden size, attention-head dimensions, feed-forward dimensions, sequence lengths, sparsity pattern and supported data types. Test the real shapes rather than a conveniently rounded proxy.
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Use a layered benchmark instead of a single score
A credible comparison progresses from isolated operations to a production-like service. Keep the model architecture, data, quality target and software behavior constant across candidates.
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1. Microbenchmarks
Isolate the operations that dominate your model: matrix multiplication at its actual shapes, memory movement, attention, normalization, embedding and any custom kernels. Add communication tests for the collectives used by your parallel strategy. Microbenchmarks reveal whether a platform is compute-bound, memory-bound or communication-bound, but they do not predict application performance by themselves.
2. Roofline analysis
Use measured arithmetic intensity and memory traffic to compare the workload with each platform’s attainable compute and bandwidth bounds. Advertised FLOPS and HBM bandwidth can be misleading or unattainable in real deployments. Google Cloud recommends microbenchmarks, roofline analysis and model benchmarks together rather than relying exclusively on specifications: AI accelerator performance and benchmarking.
3. End-to-end training
Run the unchanged representative model with the intended optimizer, data pipeline, precision, checkpoint interval and failure behavior. Report:
- Tokens per second per accelerator and for the complete job.
- Time to reach the required quality or checkpoint.
- Scaling efficiency as chips and nodes are added.
- Retries, recovery time, network stalls and other interruptions.
- Engineering time and model changes required to obtain the result.
Google Cloud suggests repeating scaling tests at 256, 1,024 and 4,096 accelerators as example points. Those are suggested test scales, not universal requirements; use sizes that bracket your planned deployment. “Goodput” is more informative than raw token rate when faults, retries or synchronization stalls reduce useful training progress.
4. Production-like inference
Replay the actual request distribution rather than a single fixed batch. Vary prompt and generation lengths, concurrency and arrival rate. Measure sustained throughput and a latency distribution (at least median and tail percentiles appropriate to your SLA), along with quality parity. Calculate the amortized cost per token or other useful output at the operating point that meets the SLA.
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Compare the results on the right axes
| Axis | Evidence to collect | Decision question |
|---|---|---|
| Workload performance | End-to-end training time, useful throughput, inference throughput and latency distribution | Does it meet the quality and service target? |
| Scaling | Per-chip throughput and efficiency as nodes are added; communication overhead; goodput | Will performance hold at the required cluster size? |
| Software and model fit | Porting hours, supported frameworks and kernels, compiler maturity, geometry match and quality parity | Will optimization effort erase the hardware advantage? |
| Cost | Instance or hardware charges, networking, storage, energy, cooling, utilization, engineering and amortization | What is the cost per useful training token or inference token? |
| Power and facility limits | Measured system power and performance per watt at the target workload | Can the deployment fit the available power and cooling envelope? |
| Operational flexibility | Capacity, availability, fault recovery, geography and migration options | Can the platform be obtained and operated for the whole horizon? |
Normalize performance to cost and power
For training, calculate at least tokens per second per chip and tokens per second per dollar. A useful cost-normalized figure is:
training cost per useful token = (accelerator + host + network + storage + energy + cooling + engineering amortization) / useful tokens produced
Use the same accounting boundary for both systems. If one provider bundles networking or storage and the other bills them separately, make the components visible rather than comparing headline accelerator rates.
For inference, calculate sustained cost per output token at the concurrency and latency point that meets the SLA. Include idle capacity, replicas kept for failover, model-loading time and energy. Measure power at the workload, not just the nameplate rating, and report performance per watt. A platform with lower energy per token may still lose if its utilization is poor or if engineering costs are large.
Test cluster effects, not just one accelerator
Single-chip tests hide the costs of synchronization, data exchange and failures. Repeat the same run at the smallest useful configuration and at the cluster sizes you may actually deploy. Record throughput per chip, scaling efficiency, network utilization, synchronization wait, checkpoint traffic and the time to recover from a failed worker.
For training, distinguish raw throughput from goodput. If a run produces 100 million tokens per hour but spends 12 minutes of each hour retrying failed steps, the useful rate is lower. Use the same failure-injection or historical-failure assumptions for every candidate. For inference, test replica startup, autoscaling delays and capacity during traffic bursts; a favorable steady-state number may not translate into acceptable customer latency.
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Interpret vendor benchmark and price claims carefully
Vendor material is useful evidence when its scope is preserved. Amazon’s 2025 shareholder letter reports “about 30% better price-performance” for Trainium2 than comparable GPUs and says Trainium3 is 30–40% more price-performant than Trainium2. Those are Amazon’s own comparisons, not neutral conclusions for every model, region or deployment. Amazon said Trainium3 began shipping at the start of 2026; verify current availability before planning capacity. Read the claim in context at Amazon CEO Andy Jassy’s 2025 Letter to Shareholders.
NVIDIA’s performance page reports that MLPerf Inference v6.0 (April 2026) GB300 NVL72 systems delivered 2.5 million tokens per second on DeepSeek-R1. The same page reports a cost-per-token figure attributed to SemiAnalysis InferenceX. These numbers apply to the named system, model, benchmark version and date; they are not evidence that GPUs are cheaper than ASICs generally. Review the underlying configuration before using the result in a business case: NVIDIA Performance Benchmarking.
NVIDIA’s benchmark hub also describes its Training v6 submissions and cites MLCommons entry IDs and retrieval dates. Treat those submissions as benchmark-specific results and compare matched configurations rather than converting a vendor page into a universal ranking: Inference Performance for Data Center Deep Learning.
Make the benchmark reproducible
- Freeze the model commit, tokenizer, dataset sample, precision, sequence lengths and quality-evaluation code.
- Record accelerator type, host configuration, topology, driver, runtime, compiler, kernel and framework versions.
- Warm up the system, define measurement windows and separate startup or compilation time from steady-state results.
- Run enough repetitions to show variation, and publish median and tail latency where applicable.
- Log power, utilization, memory pressure, network traffic, retries, failures and checkpoint time.
- Repeat at planned cluster sizes, using the same parallelism strategy where possible.
- Publish raw counters and the formulas for cost, scaling efficiency, goodput and cost per useful output.
If an implementation requires padding or a custom kernel, show both the unoptimized baseline and the tuned result, the engineering hours and any quality checks. If a model modification is proposed, treat retraining and validation as part of the project cost.
Common evaluation mistakes and their fixes
Choosing by peak FLOPS
Problem: theoretical throughput ignores memory, shape efficiency and communication. Fix: use measured kernels, roofline analysis and end-to-end runs.
Benchmarking a different model or batch
Problem: a result on a convenient model says little about your workload. Fix: hold architecture, sequence lengths, precision, quality and request mix constant.
Ignoring porting labor
Problem: a hardware-only price comparison hides compiler and kernel work. Fix: record engineering time, maintenance burden and retraining when geometry changes.
Using peak inference throughput
Problem: maximum tokens per second may violate the latency SLA. Fix: report sustained throughput and latency percentiles under the real concurrency and context distribution.
Stopping at one chip
Problem: network and synchronization costs can erase a single-chip advantage. Fix: test the deployment scale and calculate goodput and scaling efficiency.
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Problem: accelerator rental or purchase price omits energy, facility, networking, storage and utilization. Fix: use the same fully loaded cost boundary and amortization period for both options.
A practical decision rule
Select the platform that meets the required quality, throughput and latency at the lowest risk-adjusted cost over your operating horizon—not the one with the highest specification or the most attractive headline. A GPU is often the safer choice when model changes are frequent, framework coverage and portability matter, or the workload is too uncertain to justify specialized optimization. An ASIC can be compelling when the model and tensor geometry are stable, the software path is mature, utilization is high and measured performance per watt or per dollar remains superior after porting and cluster costs.
When the gap is small, value optionality: capacity guarantees, geographic availability, migration paths, debugging tools and the cost of changing models may outweigh a modest benchmark lead. When the gap is large, validate the result with a second implementation or an independent benchmark configuration before committing to a long-lived architecture.
What a procurement report should contain
- The decision specification and all workload assumptions.
- Hardware, software and topology details sufficient to reproduce each run.
- Microbenchmark, roofline, end-to-end training and inference results.
- Scaling curves, goodput, failure and recovery observations.
- Quality parity results and any padding, custom kernels or model changes.
- Fully loaded cost, performance per watt and sensitivity to utilization or energy price.
- Availability, capacity, geography, support and migration risks.
- A recommendation tied to a stated operating horizon, with vendor claims clearly attributed and dated.
This format keeps the decision anchored to useful output and makes it possible to rerun the comparison when accelerator generations, software versions, prices or cloud regions change.
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