The Tool Desk
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Vendor peak specifications are useful for narrowing a shortlist, not for declaring a winner. A fair decision requires the same model and service target on each candidate, measured under equivalent conditions.
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Define the workload before comparing hardware
Write a one-page workload specification before looking at accelerator names. Record the model architecture and parameter count, training or inference phase, input and output sequence lengths, batch size or concurrent requests, precision or quantization, target throughput, latency objective and availability requirement.
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- Is the job pre-training, fine-tuning, preference optimization or a short experiment?
- What are the model size, context length, global batch size and number of training tokens?
- Which precision is used for forward and backward passes, and which optimizer states must remain resident?
- What is the deadline for a run, and how much checkpointing or restart overhead is acceptable?
- Will the job use data parallelism, tensor parallelism, pipeline parallelism or a combination?
Inference questions
- Is the service offline batch generation, interactive chat, embeddings, reranking or another pattern?
- What are prompt and generated-token lengths, requests per second, concurrent sequences and the required p50, p95 or p99 latency?
- Which quantization format is permitted, and how large can the key-value (KV) cache become?
- Must the service support continuous batching, speculative decoding, streaming responses or strict isolation between tenants?
State the binding constraint explicitly: model fit, time-to-train, tokens per second, tail latency, reliability, energy, or total cost. An accelerator that excels on one objective can be a poor choice on another.
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Use a like-for-like comparison sheet
Every number in a comparison should carry its precision, sparsity assumption, measurement granularity and configuration. Put the following fields in a worksheet for each candidate.
| Axis | What to record | Why it changes the decision |
|---|---|---|
| Workload | Model, parameter count, sequence lengths, batch or concurrency, training or inference | Performance is workload-dependent; a different model can reverse the ranking. |
| Precision | FP32, BF16, FP16, FP8, INT8, INT4 or another format, plus accumulation precision | Peak figures in different precisions are not interchangeable. |
| Sparsity | Dense or structured/announced sparse operation, and whether the software actually uses it | Sparse peak can be roughly twice dense peak on some architectures but may not apply to your kernels. |
| Granularity | Per chip, GPU, board, node, pod, UltraServer, rack or complete cluster | A system total must not be compared with a single accelerator. |
| Memory | Physical and usable high-bandwidth memory (HBM), host memory, bandwidth and reserved runtime space | Capacity can determine whether the model runs at all; bandwidth often limits a running model. |
| Communication | On-package links, accelerator-to-accelerator fabric, node network, topology and scale-out bandwidth | Distributed training and serving spend time moving tensors and gradients, not only multiplying them. |
| Software | Framework and version, operators, kernels, compiler, distributed libraries, serving stack and observability | Unsupported operations or immature kernels turn theoretical hardware into engineering work. |
| Outcome | Time-to-train, tokens per second, latency distribution, utilization, failures and cost per completed outcome | The service result, rather than a peak specification, is what the business pays for. |
Check memory fit before compute
Memory capacity is a gating test. A model that cannot fit with its runtime state will not benefit from a higher peak-FLOPS number. Separate the estimates for weights, activations, optimizer state, temporary workspaces, framework overhead and communication buffers.
Training memory
For training, count at least the model weights, gradients, optimizer states and saved activations. Mixed-precision training commonly keeps more than one copy of parameters and may retain higher-precision master weights. Activation memory depends strongly on sequence length, batch size, checkpointing and the parallelism strategy. Calculate the footprint for the exact framework configuration rather than multiplying parameter count by one nominal byte size.
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For inference, include weights, runtime workspaces and the KV cache for the maximum number of simultaneous sequences. KV cache grows with context length and concurrency, so a configuration that fits a single prompt may fail under production load. Quantization can reduce weight storage, but kernels, scales, dequantization buffers and cache format still consume memory.
Capacity, bandwidth and usable memory
Record advertised physical HBM separately from the amount available to the application after system reservations. Then test whether the workload is capacity-bound or bandwidth-bound. Large models may require sharding across accelerators; that introduces communication overhead and can increase the memory reserved for collectives. A candidate with more HBM per chip can reduce the number of shards even when its arithmetic peak is lower.
Normalize compute specifications correctly
Compare arithmetic only when the precision, density assumption and granularity match. BF16, FP8, FP16 and integer TOPS describe different operations. A sparse figure is not a dense figure, and a pod or server total is not a per-chip result. The same label can also reflect different accumulation rules or operation counting conventions.
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Current official specifications illustrate why normalization matters:
| Accelerator or system | Published compute | Memory | Bandwidth | Scope and qualification |
|---|---|---|---|---|
| Google Cloud TPU7x (Ironwood) | 2,307 TFLOPs BF16; 4,614 TFLOPs FP8 peak per chip | 192 GiB HBM per chip | 7,380 GB/s HBM per chip | Google Cloud specifications; a pod can contain 9,216 chips. These are peak figures, not application throughput. |
| AWS Trainium3 | Not stated in the cited product specification in a directly comparable precision format | 144 GB HBM3e per chip; up to 20.7 TB across a 144-chip UltraServer | 4.9 TB/s per chip; 706 TB/s aggregate for the 144-chip UltraServer | AWS-published figures. UltraServer totals are system-level, not single-chip values. The documented software environment includes Neuron, PyTorch, vLLM and Hugging Face Transformers integrations. |
| NVIDIA Rubin GPU and HGX platform | Use the precision-specific tables; the platform distinguishes dense and sparse specifications | 288 GB HBM4 per GPU | 22 TB/s per GPU | NVIDIA’s HGX tables also report system totals and identify sparse values separately. Do not substitute an HGX total for one GPU. |
| AMD accelerator catalog | Model- and precision-specific fields are provided in the catalog | Model-specific | Model-specific | Select the exact model row and verify its software support; no single AMD model figure is assumed here. |
These specifications are not a normalized cross-vendor benchmark. For example, the TPU7x BF16 value cannot be ranked directly against a different product’s FP8 or sparse value. AWS Builder Center author Rohith Kumar made the same point in a September 21, 2026 vendor-platform comparison: “FLOPS alone cannot settle it, because the two chips publish different precision metrics and use different architectures.” That statement is vendor-platform commentary, not an independent test.
Evaluate memory bandwidth and communication together
High HBM bandwidth helps when a workload repeatedly streams weights or activations, but end-to-end performance also depends on moving data between accelerators. For multi-device training, record the topology and bandwidth of the local accelerator links, the node fabric and the scale-out network. All-reduce, all-gather, tensor-parallel exchanges and pipeline transfers can dominate step time when communication is slower than computation.
Scale-up questions
- How many accelerators share a board or server, and what links connect them?
- Can the framework map tensor and pipeline partitions onto the fastest links?
- What happens when one device fails or is removed from a tightly coupled group?
- Does the provider publish topology, oversubscription and collective-performance guidance for the configuration you can actually rent or buy?
Scale-out questions
- What network bandwidth and latency connect nodes, and is traffic isolated from other tenants?
- How many nodes are needed to meet the target, and does the scheduler reliably allocate them together?
- How much checkpoint, storage and data-loader traffic shares the network?
- Does adding devices improve useful throughput, or merely increase synchronization time and cost?
Measure scaling efficiency at the planned device count. A chip with lower single-device throughput can deliver the faster training run if it requires fewer shards or has better collective communication for the model.
Verify the complete software path
Framework names alone do not guarantee drop-in performance. Confirm the exact framework and version, model operators, custom kernels, compiler route, distributed-training libraries, serving framework and monitoring tools.
Training compatibility checklist
- Run a representative forward and backward pass, including mixed precision and gradient accumulation.
- Check attention, normalization, embedding, mixture-of-experts and any custom layers for native kernels.
- Verify data-parallel and tensor- or pipeline-parallel support at the intended device count.
- Confirm checkpoint format, restart behavior, profiler support and numerical-convergence expectations.
- Measure compilation time and cache behavior; a long first compile can affect short jobs and autoscaling.
Inference compatibility checklist
- Test the intended serving engine, continuous batching, streaming and KV-cache management.
- Verify tokenizer, quantization, speculative-decoding and model-parallel support.
- Measure cold-start time, warm-up, batching policy and p50/p95/p99 latency.
- Check observability, tracing, health checks and safe fallback when a device or host fails.
AWS describes Trainium as “a purpose-built AI chip designed for one goal: the best economics for high performance AI training and inference at scale.” This is AWS product positioning, not an independently established comparative result. Treat similar claims from every vendor as hypotheses to test on your workload.
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Benchmark the service outcome, not the datasheet
Build one benchmark harness that runs the same model, data, precision, software settings and stopping criteria on each candidate. Keep preprocessing, host CPU work, storage and networking in the measurement where they affect production.
- Freeze the workload. Record model revision, tokenizer, sequence-length distribution, batch or concurrency policy, precision, quantization and random seeds.
- Prove correctness. Compare loss curves, generated outputs or embedding quality, and define an acceptable numerical tolerance before timing runs.
- Warm up and compile. Exclude one-time compilation only if production also amortizes it; otherwise report it separately.
- Measure steady state. For training, record step time, samples or tokens per second, scaling efficiency, checkpoint time and time-to-target loss. For inference, record prompt and decode throughput, p50/p95/p99 latency, queueing, concurrency and error rate.
- Capture utilization. Log accelerator utilization, HBM usage and bandwidth, host CPU, network traffic, power where available, and idle periods caused by input or synchronization.
- Repeat failures and recovery. Include retries, preemption, node replacement and checkpoint restoration in long-run measurements.
- Report uncertainty. Use repeated runs and publish the configuration, software versions and device count so the result can be reproduced.
Do not turn a short kernel microbenchmark into a claim about application speed. Vendor peak numbers support shortlisting; only an end-to-end run establishes achieved throughput for your model.
Calculate total cost for the same target
Compare the cost of completing an outcome, not the hourly price of an accelerator in isolation. For training, calculate the cost of reaching the required quality or token count. For inference, calculate the cost per accepted request, generated token or million tokens at the required latency and availability.
Include every material cost
- Accelerator time and the host instance or server that carries it.
- Scale-up and scale-out networking, storage, checkpoint traffic and data egress.
- Reserved capacity, minimum commitments, queueing or idle time and failed or preempted runs.
- Power, cooling, rack and facility costs for owned systems.
- Engineering time for porting kernels, compiler work, numerical validation, deployment and observability.
- Support, maintenance, software licensing and the cost of keeping a fallback platform.
Use current prices for the specific geography, region, contract and configuration. No region-specific cloud rate or procurement quote is established here, so a universal cheapest option cannot be named. A lower device-hour price can lose once porting effort, lower utilization or additional replicas are included.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Choose a deployment model and availability plan
Hosted accelerators reduce procurement lead time and can provide managed scale, but capacity, region and quota may constrain the design. Owned hardware offers more control over placement and utilization but requires power, cooling, networking, spares and operations. A hybrid plan can reserve a stable baseline while sending burst or experimental jobs elsewhere.
For each candidate, verify that the required device type is available in the geography where data may be processed, that the provider can allocate the needed number of devices together, and that capacity is reliable during the hours your service runs. Record quota, reservation terms, maintenance windows, data-residency constraints and a tested fallback configuration.
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Common comparison mistakes
Ranking unlike precisions
Putting BF16, FP8, INT8 and sparse figures in one descending list creates a false ranking. Keep separate columns and benchmark the precision your model actually uses.
Mixing chip and system totals
A 144-chip server total or an HGX system number cannot be compared with one accelerator. Label every result with device count and topology.
Ignoring memory overhead
Weights alone are not the training footprint, and a single inference sequence is not the production KV-cache footprint. Include runtime reservations, activations, optimizer states and concurrency.
Assuming framework names mean compatibility
“Supports PyTorch” does not prove that your operator graph, compiler path or distributed strategy is fast or complete. Run the actual model and inspect unsupported operations.
Using peak FLOPS as throughput
Utilization, memory traffic, synchronization, input pipelines and software maturity determine achieved performance. Treat peak as an upper-bound specification.
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A platform that needs weeks of kernel work or suffers frequent allocation failures can cost more than a faster-to-deploy alternative. Record porting hours, incident rate and recovery time.
A practical decision workflow
- Write the target. Specify model, phase, precision, batch or concurrency, quality threshold and service-level objective.
- Eliminate memory failures. Estimate the full footprint and discard configurations that cannot fit with safe headroom.
- Normalize the shortlist. Separate dense and sparse figures, precision, device count and system scope.
- Check communication. Map the required parallelism to the available topology and test scaling at the planned size.
- Validate software. Run representative operators, compilers, distributed libraries and serving components.
- Benchmark outcomes. Measure time-to-train or latency and throughput distributions, not only kernels.
- Price the result. Calculate complete cost per run or served token, including engineering, capacity and failures.
- Choose with a fallback. Document a second platform or smaller configuration that can meet a degraded but acceptable target.
What the current specifications can—and cannot—tell you
The published figures provide useful starting points: TPU7x documentation describes 9,216-chip pods and reports per-chip BF16, FP8, HBM and bandwidth values; AWS publishes Trainium3 chip and 144-chip UltraServer memory and bandwidth; and NVIDIA’s HGX material distinguishes per-GPU, system, dense and sparse data. They do not establish a fair cross-vendor benchmark, current regional pricing, or a universal fastest or cheapest choice.
The only defensible final ranking is workload-specific. Re-run the benchmark when the model, context length, precision, serving engine, compiler version, device count or commercial contract changes.
Frequently Asked Questions
Should I compare accelerators by TFLOPS, TOPS or tokens per second?
Use TFLOPS or TOPS only to create a precision-matched shortlist. Use measured tokens per second, latency percentiles or time-to-target quality for the purchasing decision.
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What if the model does not fit on one accelerator?
Estimate the complete memory footprint, then evaluate sharding, quantization, activation checkpointing or a larger-memory device. Benchmark the communication overhead introduced by the chosen partition.
How should I compare a cloud accelerator with owned hardware?
Price the same completed outcome and include host, network, storage, utilization, power, cooling, support, engineering and capacity risk. Use the geography and contract you can actually obtain.
When should a benchmark be repeated?
Repeat it after changing the model, sequence-length distribution, precision, framework or compiler version, serving policy, device count, topology or commercial configuration.
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