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Compare AI accelerators by measuring useful work per joule on the same complete system, not by dividing advertised TFLOP/s by a chip’s TDP. Hold the model, precision, quality target, batch or concurrency, software stack and latency requirement constant. Then measure achieved throughput and wall power for training or inference and report tokens, samples or inferences per joule.
Peak arithmetic-per-watt figures can screen architectures, but memory bandwidth, capacity, communication, utilization, software and total cost decide which accelerator is actually more efficient for your workload. Training and inference also need different metrics and test scenarios, so there is no universal winner.
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Performance per watt is useful work divided by the power required to deliver it. For a steady workload, calculate throughput in tokens per second, samples per second or inferences per second, then divide by measured watts. The equivalent energy metrics are joules per token, joules per sample and joules per inference; lower joules are better.
The unit must describe a defined workload. “Tokens per joule” is incomplete unless you state the model, sequence length, precision, batch or concurrency, quality target, software version and whether the result covers one accelerator or the whole server. A chip that looks efficient at large-batch training may be a poor choice for batch-one, latency-sensitive decoding.
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Use the reciprocal when it is easier to audit
If a server sustains T tokens per second at P watts, its energy intensity is P/T joules per token and its efficiency is T/P tokens per joule. Reporting both prevents a reader from confusing a larger-is-better efficiency number with a smaller-is-better energy number.
Why TFLOP/s per watt is only a screening number
TFLOP/s per watt divides a theoretical arithmetic ceiling by a rated power figure. It says how much floating-point work an architecture could perform under ideal conditions, not how many useful tokens or samples your application receives. Memory stalls, kernel overhead, communication, control flow and host-system power can dominate before the arithmetic units reach that ceiling.
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Training and inference stress accelerators differently. Use a training metric that captures sustained work over a fixed run, and an inference metric that includes the service-level latency your users experience.
| Scenario | Primary throughput metric | Required constraints to disclose | Useful energy metric |
|---|---|---|---|
| Model training | Tokens per second per chip, or samples per second | Model and dataset, precision, global batch, optimizer, sequence length, number of accelerators, step-time target and convergence or quality criterion | Joules per token or sample over a representative steady-state interval |
| Offline inference | Sustained tokens/s or samples/s at the chosen batch | Model, input and output lengths, precision, batch size, software version and quality target | Joules per token, sample or inference |
| Interactive LLM serving | Throughput at a stated tail-latency SLA | P99 (or another percentile), time to first token, inter-token latency, prompt/output lengths, concurrency and admission policy | Joules per generated token or complete request at the SLA |
| Vision or speech service | Inferences/s or samples/s at the required latency | Input resolution or audio length, preprocessing, postprocessing, batch, precision and quality target | Joules per inference or sample |
Google Cloud’s accelerator guidance recommends tokens per second per chip for training and sustained tokens per second at the service-level agreement for inference. Do not compare a training result with an inference result, even when both are expressed as tokens per second.
How do I set up a fair accelerator comparison?
A fair test changes only the accelerator (and the minimum platform components it requires). Freeze the workload and acceptance criteria before collecting power data.
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- Define the workload. Record the exact model architecture and size, dataset or prompt set, sequence lengths, tokenizer, precision, quantization, batch or concurrency and any sparsity or mixture-of-experts routing.
- Define quality and completion criteria. For training, specify the target loss, validation score or number of steps. For inference, specify accuracy or perplexity limits and the output-quality test that every platform must pass.
- Separate training from inference. Create independent benchmark runs; their optimal batch sizes, kernels and power profiles are not interchangeable.
- Freeze software. Pin framework, compiler, driver, runtime, libraries, model code and kernel versions. Record launch flags, graph-capture settings, communication libraries and precision settings.
- Set the service target. For serving, choose a latency percentile such as P99 and measure time to first token and inter-token latency, not just average latency.
- Warm up and sample steady state. Exclude compilation and one-time initialization from a steady-state result, but report those costs separately if they affect your deployment.
- Repeat the run. Use the same duration and repetition count on every system, report variation and retain raw throughput and power traces.
Google Cloud recommends holding model, precision, quality target, dataset, batch or concurrency and software version constant. If one platform needs a different batch to meet the same latency target, report that fact rather than forcing identical settings that produce an unfair service.
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How does roofline analysis explain efficiency differences?
Roofline analysis compares a workload’s operational intensity (arithmetic work per byte fetched) with an accelerator’s compute and memory ceilings. A compute-bound workload can approach the arithmetic roof; a memory-bound workload is limited by bandwidth and may gain little from more TFLOP/s.
Common memory-bound paths
- Autoregressive, batch-one decoding repeatedly reads weights and key-value caches while doing relatively little arithmetic per byte.
- Element-wise operations and layer normalization move data through memory without enough computation to hide bandwidth limits.
- Small or irregular kernels can be limited by launch overhead, cache misses or synchronization rather than peak math throughput.
Common compute-bound paths
- Large matrix multiplications in training and prefilling usually have high arithmetic intensity.
- Large-batch convolutions can keep tensor or matrix units busy when data reuse is high.
Use the roofline as a diagnostic before ranking hardware. If your measured point is memory-bound, compare high-bandwidth memory, cache behavior and weight or activation movement. If it is compute-bound, examine usable tensor-core or matrix-unit throughput, compiler kernels and utilization. A peak compute advantage can disappear when the workload sits below the memory roof.
What power boundary should the measurement include?
For deployment decisions, measure AC power at the wall for the complete server during the benchmark. Include the accelerator, host CPUs, memory, storage, networking and the server’s power-conversion losses. State whether cooling is inside the measured boundary; facility overhead can be reported separately, but it must not be silently mixed with chip-only figures.
MLCommons uses system power or energy per stream for its accompanying benchmark scenarios. Its results messaging policy states: “System power measured using the MLPerf Power methodology is the only MLCommons officially-sanctioned power metric to be used for the purposes of portraying MLPerf results and/or making comparisons.” The policy also prohibits public MLPerf comparisons based on normalized TDP, rated power or PSU ratings.
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Why TDP and PSU ratings are not substitutes
TDP is a design or thermal target, not the power a particular run draws. A workload may draw below or above it, and the host can consume substantial power while the accelerator waits on data. A PSU rating describes capacity, not consumption. Never compare one vendor’s chip-only TDP with another vendor’s full-server wall measurement.
How should I calculate and report tokens per joule?
Log throughput and power over the same interval. For a steady run, divide total tokens completed by total joules measured at the wall. Total joules are the time integral of watts; using an average watt reading is acceptable only when the sampling interval and workload are stable.
- Report raw tokens/s (or samples/s) and average watts before calculating the ratio.
- State whether tokens include input, output or only generated output.
- For variable-length requests, disclose the prompt and completion-length distribution.
- For training, include only the defined training interval and identify checkpointing, evaluation and data-loading treatment.
- For inference, report both throughput at the latency target and the latency distribution that produced it.
For a multi-accelerator job, publish single-accelerator and full-job figures. A cluster can deliver more total tokens while delivering fewer tokens per joule because networking, synchronization and idle time rise with scale.
What do published peak arithmetic-per-watt figures tell me?
They are architecture-screening ceilings, not measured application efficiency. Harvard Edge’s 2026 educational table lists these dense-FP16 ceilings:
| Accelerator | Dense FP16 ceiling | How to interpret it |
|---|---|---|
| V100 | 0.42 TFLOP/s/W | Theoretical arithmetic ceiling; not training throughput or facility energy |
| A100 | 0.78 TFLOP/s/W | Theoretical arithmetic ceiling; memory and utilization are not represented |
| H100 | 1.41 TFLOP/s/W | Theoretical arithmetic ceiling; communication and host power are excluded |
| B200 | 2.25 TFLOP/s/W | Theoretical arithmetic ceiling; not a deployment ranking |
The table itself warns that utilization, memory stalls, communication and non-accelerator power must be included for a real decision. Use the figures to identify generations worth testing, then replace them with measured workload results.
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A 2026 Federal Reserve note reproduces Epoch AI release tables containing accelerator cost per TFLOP/s and cost per GB/s for parts including H100, H200, MI300X, TPU v6e, Trainium2 and B200. Those are procurement-context measures, not joules per workload; bandwidth and price changes between generations do not establish energy efficiency.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How do memory, interconnect and scale change the result?
Check whether the model fits in available memory without costly sharding, and record memory bandwidth, capacity, interconnect topology and communication volume. A lower-throughput accelerator with enough local memory can beat a faster part that requires more devices and synchronization.
Measure scale-out efficiency
- Benchmark one accelerator with the same workload and quality target.
- Repeat at the intended device count and record aggregate throughput, wall power and latency.
- Calculate scaling efficiency as observed throughput divided by ideal linear throughput.
- Attribute losses to all-reduce, parameter exchange, pipeline bubbles, data loading or host bottlenecks.
Report the network hardware and topology. Interconnect saturation can make a nominally efficient accelerator look inefficient at cluster scale, while a strong interconnect can preserve utilization.
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How much do software and reproducibility matter?
Kernel availability, compiler maturity, framework support and reproducible launch settings determine how much of the silicon you can use. Record which operations fall back to generic kernels, whether graph capture or fusion is enabled, and how often the runtime recompiles.
Use the same model implementation where possible, but allow vendor-specific kernels only when they are production-supported and the quality target remains identical. Document every optimization so another team can reproduce the result. A one-off benchmark requiring unreleased software is evidence of a prototype, not a dependable production ranking.
How do I convert efficiency into total cost?
Energy efficiency is one input to total cost of ownership. Add the amortized accelerator and server price, networking, storage, software or cloud charges, electricity, cooling, operations and the expected service life. Use cost per million tokens or cost per inference only after disclosing throughput, latency, model, utilization and energy-price assumptions.
- Capital cost: amortize the complete server or rented commitment over its useful life and expected utilization.
- Energy cost: multiply measured wall joules by the electricity rate; apply a stated cooling or facility factor if it is outside the test boundary.
- Capacity cost: include extra devices required for memory, redundancy or latency headroom.
- Operations: account for engineering time, software support, monitoring and maintenance.
- Utilization: model realistic duty cycles; an idle accelerator has poor cost efficiency even if its active benchmark is excellent.
Cloud instance prices can change by region, contract and time. Compare the billed instance boundary with the measured hardware boundary, and do not infer a cloud provider’s energy performance from its hourly price alone.
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- Ranking TFLOP/s divided by marketing TDP as measured performance per watt.
- Comparing chip-only power for one device with full-server wall power for another.
- Mixing model sizes, precisions, batch sizes, sequence lengths or quality targets.
- Using average latency when the requirement is a P99 or another tail percentile.
- Reporting peak throughput without memory capacity, bandwidth or interconnect details.
- Using a single benchmark submission to declare a universal winner.
- Ignoring host CPUs, memory, networking, storage or cooling in the energy boundary.
- Quoting procurement cost per TFLOP/s or per GB/s as if it were joules per token.
- Failing to disclose software versions, kernels, drivers and benchmark duration.
Which accelerator is the most energy-efficient for LLM inference?
There is no model-independent answer. The winner is the accelerator that delivers the required quality and P99 latency with the lowest measured joules per request or generated token on your model, prompt and concurrency distribution, using the same wall-power boundary as the alternatives.
Batch-one decode may favor memory capacity and bandwidth; high-concurrency serving may favor compute density and batching efficiency. Test prefill and decode separately, then combine them using the request mix your service actually receives.
Frequently Asked Questions
Should I measure power during warm-up or only steady state?
Record both when they matter. Exclude one-time compilation and initialization from a steady-state efficiency figure, but report their energy and elapsed time separately if workers restart, autoscale or frequently load models in production.
Can I compare an on-premise server with a cloud accelerator instance?
Yes, if you align the boundaries. Measure or obtain the instance’s documented host allocation, use the same workload and latency target, and compare billed cost and energy assumptions separately rather than treating an hourly price as a power measurement.
How many benchmark runs are enough for a reliable result?
Use repeated runs long enough to reach stable thermal and utilization behavior, and publish the spread rather than only the best run. The exact count depends on workload variance; the important requirement is identical duration, warm-up policy and sampling on every platform.
Do quantized models always improve performance per watt?
No. Lower-precision arithmetic can reduce memory traffic, but dequantization kernels, unsupported operators, accuracy loss or lower utilization can offset the benefit. Compare quantized systems at the same quality target and include all conversion work in the measured interval.
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