Choose a GPU when your models, frameworks, kernels, or deployment locations may change. Choose a custom accelerator—such as a TPU or another inference ASIC—when one or a few stable models run at high volume and fit the chip’s compiler, matrix geometry, memory system, and interconnect. Peak FLOPS alone cannot settle the choice. Benchmark the exact model and serving stack, measure prefill and decode separately, and compare fully loaded cost per token at the utilization you can actually sustain.
This guide provides a repeatable way to make that decision for cloud, data-center, edge, and local inference. It covers model compatibility, latency and throughput testing, economics, operations, supply, and the situations in which a nominally faster chip becomes the wrong platform.
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What you are actually choosing
A GPU is a programmable parallel processor with a broad ecosystem of drivers, kernels, frameworks, and serving tools. That generality makes it useful for changing model architectures, experimental quantization, custom operators, and deployments that may move between vendors or clouds.
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GPU versus custom accelerator: the decision at a glance
| Decision axis | GPU | Custom AI accelerator |
|---|---|---|
| Model and framework coverage | Broad general-purpose support and a large kernel and serving ecosystem | Best for models and operators covered well by the vendor compiler and runtime |
| Portability | Usually easier to move across clouds and generations, although drivers and kernels still differ | Often tied to a vendor SDK, compiler, cloud, or supported model shapes |
| Performance | Strong general performance; results depend on kernels, precision, memory, and batching | Can be excellent on aligned workloads; mismatches can waste compute or require porting work |
| Latency | Many tuning options and serving engines | Specialized, deterministic pipelines can improve tail latency when the workload matches |
| Scale-out | Mature interconnect and distributed-serving options | Custom fabrics may be efficient, but collective bandwidth and software support must be verified |
| Economics | Flexibility, availability, and resale options; utilization drives cost | Potentially lower energy or token cost at sustained high utilization, offset by engineering and lock-in |
| Operations | Larger talent and tooling pool, but infrastructure can be complex | More specialized expertise; managed offerings reduce hardware burden |
Start with the workload and service-level objective
Write down the workload before looking at accelerator specifications. The same chip can be excellent for one request pattern and uneconomical for another. Record the following values for the production service:
- Model family, parameter count, dense or mixture-of-experts (MoE) architecture, and whether weights are open, fine-tuned, or likely to change.
- Context length, expected input and output token mix, quantization format, and maximum concurrent sequences.
- Requests per second, burst pattern, target availability, and the amount of idle capacity required for failover.
- Time-to-first-token (TTFT), inter-token latency (ITL), and P95 and P99 latency limits. Streaming chat and offline batch jobs need different limits.
- Whether traffic is steady enough to keep the accelerator busy, or spiky enough that capacity sits idle between bursts.
- Data-residency, region, networking, and isolation requirements that constrain which clouds or facilities are acceptable.
Separate interactive and batch objectives. An offline summarization queue may tolerate high TTFT if it maximizes tokens per dollar. A coding assistant may need a strict TTFT and ITL at the 99th percentile, even if average throughput is high.
Confirm that the exact model can run
Check operators, precision, and serving software
For each candidate platform, verify every operator used by the model, the supported precision and quantization path, maximum model size, available device memory, host-to-device transfer behavior, and the serving engine you intend to operate. Confirm that container images, drivers, observability agents, and orchestration integrations are available for the required version. A platform that supports a similar model but requires rewriting kernels or changing weights is not an apples-to-apples alternative.
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Matrix dimensions can leave hardware lanes idle. Google’s benchmarking guidance gives a concrete example: a 64-wide attention head in gpt-oss-120B does not fill TPUs’ preferred 256-wide matrix geometry. Padding can reduce utilization unless you use custom kernels or retrain with shapes that map better. Ask the vendor for utilization data on your actual hidden size, head dimension, sequence lengths, and batch shapes rather than relying on a peak TOPS figure.
Inspect memory and communication requirements
Determine whether weights, key-value (KV) cache, activations, and runtime buffers fit in device memory at the target context length. Then measure host-to-device transfer, device-to-device bandwidth, and collective operations for tensor or expert parallelism. An accelerator with ample arithmetic throughput can still stall on KV-cache movement or an under-sized interconnect.
Benchmark prefill and decode as different workloads
Large-language-model inference has two phases with different bottlenecks. Prefill processes the prompt and is generally compute-bound. Decode generates one or a few tokens per step and is generally limited by memory bandwidth and KV-cache traffic. A single blended throughput number can conceal a serious weakness in either phase.
Build a representative model set
Use at least one small dense model, one medium dense model, and one large MoE model. Google lists Llama 3.1 8B, Llama 3.1 70B, and Qwen3 Coder 480B as representative inference benchmarks. Add your production model and any likely successor; benchmark suites that omit the production architecture can lead to the wrong purchase.
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Run controlled tests
- Pin software versions, compiler flags, quantization, tokenizer, sequence lengths, and sampling settings. Record the accelerator generation, device count, host CPU, memory, and network topology.
- Warm the service and record cold-start and warm-start behavior separately. Include model load time, compilation time, and container startup if autoscaling is part of the design.
- Measure prefill with several prompt lengths and decode with several output lengths. Test the input/output token ratios expected in production.
- Increase concurrency and batch size gradually. For every point, record sustained input and output tokens per second, TTFT, ITL, P50, P95, and P99 latency.
- Identify the largest batch that still meets the P99 SLA. Report throughput at that operating point, not only at an unconstrained maximum.
- Capture device memory, host memory, power draw, thermal behavior, compiler warnings, kernel fallbacks, and any CPU execution.
- Repeat the run after scale-out to the planned number of devices. Measure collective communication, synchronization stalls, and cross-host network traffic.
| Metric | Why it matters | How to use it |
|---|---|---|
| TTFT | Perceived responsiveness and prompt-processing capacity | Set a target for interactive requests and test at realistic concurrency |
| Inter-token latency | Streaming smoothness after the first token | Check median and tail values during decode |
| P50/P95/P99 latency | Exposes queueing and noisy-neighbor effects | Use the percentile required by the SLA, usually P95 or P99 |
| Input and output tokens per second | Separates prefill and decode capacity | Use sustained values at the SLA-compliant batch size |
| Memory use and KV-cache occupancy | Determines maximum context and concurrency | Include fragmentation and headroom for failover |
| Power and cooling | Contributes directly to operating cost and rack limits | Measure under the same sustained load as performance tests |
Use effective performance, not peak FLOPS
Google Cloud’s guidance states: “Comparing theoretical FLOPS tells only part of the story. What matters is effective FLOPS, since peak numbers are almost never reached in real-world workloads.” Effective performance includes kernel efficiency, precision, memory bandwidth, batching, compiler scheduling, communication, and time spent waiting in queues. A lower-peak device can deliver more tokens per second if it keeps the model’s hot path busy.
When comparing results, preserve the conditions that produced each number: model version, prompt and output lengths, quantization, batch size, device count, software stack, and whether the result is warm or cold. Do not compare a vendor’s single-device peak with your own multi-tenant P99 measurement.
Calculate cost per token at realistic utilization
Use an end-to-end metric rather than an hourly accelerator rate alone. A practical calculation is:
cost per million tokens = (amortized hardware + hosting + energy + networking + software + operations) / sustained output tokens × 1,000,000
For owned hardware, amortize the accelerator, server, networking, storage, and facility work over the useful life. Add electricity, cooling, maintenance, spares, monitoring, and the staff who maintain drivers, compilers, kernels, and serving systems. For rented capacity, include instance or endpoint charges, attached storage, egress, regional premiums, and minimum commitments. Count redundancy and idle capacity: a fleet sized for failover cannot be priced as though every device produces tokens continuously.
Model utilization explicitly
Run the calculation at several utilization levels, including the low end of expected demand. A custom chip that wins at 90% sustained utilization may lose at 35% when traffic is spiky. Include warm standby devices, autoscaling lag, and the fraction of time spent compiling or loading models. AWS guidance recommends validating cost per token at projected utilization because managed services can remain cheaper after operational costs are included.
Include engineering and lock-in
Estimate one-time porting, kernel, compiler, and test work, then spread it over the expected volume and service life. Price the risk of a model upgrade that invalidates those optimizations. Also account for the option value of moving to another cloud or reselling hardware; a less specialized platform may cost more per hour but less over the life of a changing product.
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Choose a deployment path
Managed foundation-model API
A managed API is the fastest route when you do not need to own weights, require minimal infrastructure work, or have highly variable traffic. The provider absorbs accelerator procurement, scaling, patching, and much of the reliability work. Trade-offs include limited model customization, less control over the data path, and pricing that may be higher for steady, very large volumes.
Managed hosting for your model
Managed hosting suits teams that own or fine-tune a model but want the provider to operate the fleet. You retain more control over weights and serving configuration while avoiding most hardware operations. Verify supported accelerators, container interfaces, autoscaling behavior, observability, regional capacity, and whether custom kernels are allowed.
Self-hosting
Self-hosting is defensible when you need a specific serving engine, custom kernels, an unusual parallelism strategy, direct control of the data path, or predictable high utilization. AWS’s August 12, 2026 guidance describes steady high-volume traffic as a case that can favor self-hosting on unit cost, while low utilization and operational burden favor a managed service. Self-hosting also makes you responsible for capacity planning, hardware failures, firmware, security updates, and on-call expertise.
When a GPU is the safer choice
Your model roadmap is unsettled
Choose GPUs when you expect frequent architecture changes, new quantization formats, retrieval components, or custom operators. The broad software ecosystem reduces the chance that a future model becomes a porting project.
You need portability
GPUs are generally easier to move across clouds and generations, subject to driver and kernel differences. This matters when capacity is scarce in one region, when regulations require a second provider, or when a product may move from cloud inference to a local or edge installation.
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Many batching, speculative-decoding, multimodal, and observability features arrive first in GPU-oriented serving stacks. If a custom accelerator lacks the exact feature or requires a vendor-specific fork, the engineering cost can outweigh a lower device price.
When a custom accelerator can win
The workload is stable and large
Custom silicon is most compelling when traffic is predictable, utilization remains high, and the same model and precision will run for years. A stable path allows you to tune compiler schedules, kernels, batching, and placement instead of paying to preserve generality.
The model maps cleanly to the chip
Look for high measured utilization, adequate memory bandwidth, efficient collective communication, and no frequent fallback to CPU or unsupported operators. Deterministic execution can help tail latency: a 2017 Google TPU production study said its deterministic model matched 99th-percentile response-time requirements better than time-varying CPU and GPU optimizations for the neural-network applications it studied.
Energy and facility limits dominate
If power, cooling, or rack density is the limiting resource, compare tokens per joule and total facility cost, not just tokens per device. The accelerator with lower energy per token may let you serve more demand within an existing power envelope.
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What historical TPU results do—and do not—prove
A 2017 Google production TPU study described a 65,536 8-bit multiply-accumulate matrix unit delivering 92 tera-operations per second (TOPS). On the production neural-network workloads studied, it reported 15–30× average speed versus a contemporary GPU or CPU and 30–80× higher TOPS per watt. Those are historical results for one TPU generation and workload mix. They are not predictions for current TPUs, GPUs, or other ASICs. Use them as evidence that system-level specialization can matter, then run current tests on your model.
The UK Competition and Markets Authority’s 2025 final decision report similarly notes that custom-supply ASICs can enable systems-level optimizations and lower costs. The same report identifies Google TPUs, AWS Trainium and Inferentia, and Microsoft Maia as cloud-provided ASICs. Availability through a cloud makes experimentation easier, but it does not remove the need to verify regional capacity, quotas, compiler support, and commercial terms.
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Interconnect and parallelism
Large models may require tensor, pipeline, or expert parallelism. Measure all-reduce, all-to-all, and synchronization time at the device count you will deploy. MoE models can be especially sensitive to all-to-all bandwidth and load imbalance. A custom fabric may be efficient for the vendor’s topology, while a GPU cluster may offer more mature distributed-serving libraries; only a representative multi-host test reveals the difference.
Failure domains and recovery
Document how a failed device, host, rack, or region affects in-flight requests. Test checkpoint loading, replica replacement, autoscaling, and cross-region failover. Include model reload time and cache warm-up in the recovery objective. A cheaper accelerator that takes hours to obtain or reconfigure during an outage may be a poor availability choice.
Check access, supply, and geography
Capacity is part of hardware selection. Confirm quotas, reservation terms, supported regions, lead times, maintenance windows, and whether the exact accelerator type is available to your account. The UK CMA reports that proprietary accelerators are increasingly offered through cloud environments; that access can be valuable when direct procurement is impractical. Still, a nominally superior chip is useless if you cannot obtain enough capacity where your users and data reside.
Stress the change cases before committing
Run a change-and-failure test plan before signing a long commitment or porting kernels:
- Upgrade the model framework and compiler, then compare output correctness and latency.
- Load a new quantization format and verify numerical quality, memory use, and throughput.
- Roll back to the previous model and serving image without losing availability.
- Introduce a traffic burst, a long-context request, and a mixture of short and long sequences.
- Remove one device or host and measure degraded throughput and P99 latency.
- Simulate regional capacity loss and confirm that another region or provider can serve the model.
- Check monitoring for queue depth, cache usage, compiler fallbacks, throttling, and power anomalies.
Frequent changes generally favor GPUs because the migration path is shorter. A custom accelerator becomes more defensible when these tests pass with limited specialist work and the resulting utilization advantage persists.
A practical selection procedure
- Write the service contract. Specify model, context, token mix, requests per second, concurrency, TTFT, ITL, P95/P99, availability, regions, and data controls.
- Build a candidate matrix. List GPU and custom-accelerator platforms that can run the exact model, precision, operators, and serving engine.
- Reject unsupported paths early. Remove options that require changing weights, unsupported kernels, excessive padding, or an unacceptable model-size and KV-cache fit.
- Benchmark representative traffic. Test prefill, decode, warm and cold starts, several batch sizes, tail latency, memory, power, and scale-out.
- Find the SLA operating point. Select the highest sustained throughput that still meets the required P99 and recovery headroom.
- Calculate full cost per million tokens. Include hardware or rental, host, energy, networking, software, operations, redundancy, idle time, and engineering.
- Run change and failure tests. Verify upgrades, rollback, burst handling, device loss, and regional failover.
- Choose the platform with the lowest risk-adjusted cost. A modest performance lead is not worth a long port, unavailable capacity, or a lock-in risk your roadmap cannot tolerate.
Common mistakes and how to correct them
Comparing peak TOPS or FLOPS
Problem: Peak arithmetic assumes ideal shapes and continuous compute. Correction: compare effective tokens per second, utilization, memory traffic, and SLA-compliant batch size on the exact model.
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Using one blended benchmark
Problem: A fast prefill can hide slow decode, or a high average can hide P99 queueing. Correction: report prefill and decode independently with TTFT, ITL, and tail percentiles.
Ignoring shape and operator mismatches
Problem: Padding, fallback kernels, or CPU work consumes the apparent accelerator advantage. Correction: inspect compiler reports and profile the production shapes before pricing the fleet.
Pricing only the device
Problem: Host CPUs, memory, networking, cooling, software, staff, redundancy, and idle capacity disappear from the estimate. Correction: use the full cost-per-token formula at several utilization levels.
Assuming cloud access guarantees capacity
Problem: A listed accelerator may have quotas, regional limits, or long reservation lead times. Correction: obtain written capacity and geography confirmation and test a fallback.
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Problem: Custom kernels and compiler tuning become permanent maintenance work. Correction: assign an owner, budget upgrade testing, and include that labor in the economic model.
Bottom line for the decision
Use a GPU as the default for evolving models, broad framework needs, portability, and uncertain demand. Select a custom accelerator when measured end-to-end results show a durable advantage on a stable, high-volume workload and the compiler, memory system, interconnect, capacity, and support model all fit your requirements. The winning platform is the one that delivers the required tail latency and sustained tokens per second at the lowest risk-adjusted cost per token—not the one with the largest number on a specification sheet.
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