Choose the accelerator that meets your model’s memory, software, latency and throughput requirements at the lowest practical cost—not the one with the highest headline benchmark. Define the production workload first, eliminate hardware that cannot run it, benchmark the remaining options under identical conditions, and price only deployments that satisfy the service-level objective (SLO).
There is no category-wide winner. A GPU may be the safer choice for a changing model portfolio or a mature framework stack; a custom inference chip can be compelling when its compiler, operators, memory and scale-out design fit a stable workload. The result is workload-specific and must be demonstrated with a matched test.
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What you are actually choosing
This is not simply a comparison of a GPU against an application-specific chip. You are choosing an entire inference deployment: accelerator memory, host and network resources, compiler and runtime, kernels, model-conversion path, serving framework, monitoring, capacity margin and operating cost. Two systems serving the same model can require different infrastructure when their prompt lengths, output lengths, concurrency or latency objectives differ.
A useful decision has three gates:
- Can it run the model? The architecture, operators, precision, quantization and model state must fit the available memory and be supported by the software stack.
- Can it meet the SLO? Measure time to first token (TTFT), generated-token speed, end-to-end latency, tail behavior, concurrency and sustained request rate under representative traffic.
- Can you afford the qualifying deployment? Compare the complete instance or server count, capacity headroom, networking and operational costs—not an isolated accelerator price or peak score.
Only candidates that pass all three gates belong in the final comparison.
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1. Define the production workload before looking at chips
Write down the request distribution you expect to serve. A benchmark based on one convenient prompt length can select the wrong hardware for real traffic.
Record the model and execution shape
- Model architecture and parameter size, including any mixture-of-experts routing or other dynamic behavior.
- Weights and activation precision. AWS sizing guidance identifies FP16, FP8 and INT4 as common inference choices; record the precision you will actually deploy, not a higher-precision model used only for a quick test.
- Quantization method and any calibration or conversion steps.
- Typical and worst-case input (prompt) token lengths.
- Typical and worst-case generated output lengths.
- Whether requests use features such as batching, streaming, speculative decoding, long context or retrieval-augmented prompts.
Record traffic and service objectives
- Average and peak request rate.
- Target concurrency, including bursts rather than only the daily average.
- TTFT target, end-to-end latency target and the percentile that defines success, such as a p95 or p99 objective.
- Required generated tokens per second for an individual stream.
- Sustained requests per second at the required concurrency.
- Availability, failover and geographic-placement requirements that may force additional instances.
Keep these values as a test profile. If production has several materially different request classes, benchmark each class or create a weighted mix; do not let an easy short-prompt case stand in for a long-context workload.
2. Check memory and model support before benchmarking price
Calculate the complete memory requirement
Model weights are only one component. Account for runtime allocations, temporary buffers, activations, attention key-value cache, batching state and other workload state. The chosen precision changes both the weight footprint and often the achievable throughput, so memory testing must use the intended precision and serving configuration.
Reject a candidate that needs unsupported partitioning, excessive off-chip transfers or an impractical number of accelerators to fit the model. A nominal weight fit is not enough if the runtime cannot reserve space for the largest production request or the planned concurrency.
Verify operators and precision
List every operation in the model graph and check whether the accelerator supports it at the required precision. Determine whether unsupported operations fall back to a host processor, require custom kernels or force a graph rewrite. Measure the resulting path; a model that technically loads can still miss its SLO because of frequent transfers or unoptimized operators.
Map the software path
For each candidate, document the compiler, runtime, kernel library and serving framework versions, plus the exact conversion commands or code changes. Establish who owns fixes when a new model layer or framework release is unsupported. AWS documents Neuron integration with PyTorch and TensorFlow; that is an AWS-published capability description, not an independent compatibility audit, so validate your own model and versions.
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Include engineering effort in the decision. A custom chip may require graph partitioning, compiler tuning or a different batching API, while a GPU deployment may use an existing serving stack with less porting. Neither assumption should substitute for a proof-of-concept on the target model.
3. Build a fair, representative benchmark
Google Cloud’s accelerator guidance recommends combining microbenchmarks, roofline analysis and model benchmarks. AWS similarly warns that public results using different workload shapes, quantization or serving frameworks are not directly comparable. Use the same model artifact, precision, input and output distributions, concurrency, backend and serving behavior on every candidate.
Control the variables
- Use identical model weights and tokenizer behavior.
- Use the same maximum sequence length, batching policy, streaming mode and stopping criteria.
- Keep framework, serving-server and compiler settings as equivalent as the platforms allow; record unavoidable differences.
- Warm up each deployment and measure steady state separately from startup and compilation time.
- Run enough repetitions to expose variance and tail latency, not just a best single run.
- Test the traffic mix at low, target and peak concurrency.
Measure every metric that affects the user and the bill
| Metric | What it tells you | How to use it |
|---|---|---|
| Time to first token (TTFT) | Delay before a streaming response begins, including prompt processing and queueing. | Check it at the required concurrency; a good single-request result can deteriorate under load. |
| Generated tokens per second | Per-stream decode speed after the first token. | Compare against the responsiveness needed for an individual user or application. |
| End-to-end latency | Total time from request arrival to completion. | Use the production output-length distribution and the required percentile. |
| Concurrent requests | Number of in-flight requests the deployment sustains. | Find the point where queueing or memory pressure violates the SLO. |
| Sustained requests per second | Capacity over a stable interval, not a short burst. | Use it to size the number of instances at peak load. |
| Tail latency | Worst-user experience at high percentiles. | Report the percentile and concurrency; a matched current GPU-versus-custom-chip tail test is not established by the available sources, so measure it yourself. |
Do not collapse these into one score. AWS Prescriptive Guidance states, "No single metric is sufficient on its own." Aggregate throughput can hide unacceptable TTFT, while excellent single-request TTFT can coexist with insufficient capacity.
Use microbenchmarks to explain the result
Measure compute throughput, memory bandwidth, transfer overhead and communication separately where the platform exposes those counters. A roofline analysis helps show whether the model is limited by arithmetic or by moving data. Model-level tests then reveal the effects of real operators, caching, batching and scheduler behavior. For multi-accelerator deployments, include interconnect and network traffic: synchronization and partitioning overhead can erase a single-chip advantage.
Test scaling, not only one accelerator
Run one-, two- and multi-chip configurations when the model or traffic requires them. Record how throughput changes, when latency rises, and how much capacity is lost to communication. Include the exact topology and host count. AWS describes EC2 Inf2 instances as supporting scale-out distributed inference; that describes an available design path, not proof that it will scale efficiently for your model.
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4. Compare complete cost at the required SLO
The relevant question is not which accelerator has the lowest hourly rate or highest tokens-per-second figure. It is which qualifying deployment provides enough capacity, with headroom, at the lowest practical total cost. AWS states the goal as identifying "the lowest-cost accelerator that satisfies the application’s service-level objectives (SLOs)."
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Include the deployment size
- Accelerator and host charges for the required instance or server count.
- Additional replicas needed for peak demand, failover and maintenance.
- Network and storage charges associated with model distribution, requests and multi-chip communication.
- Capacity headroom so normal bursts do not turn queueing into an SLO violation.
- Engineering and operations time for compilation, porting, monitoring and upgrades.
- For owned infrastructure, sustained power, cooling and thermal-management requirements.
Prices vary by region and configuration; no live regional price comparison is established here. Build the calculation from current quotes for the exact deployment that passed the benchmark. A useful internal measure is cost per sustained request or generated-token capacity while the SLO remains satisfied, with the required instance count included.
Account for utilization and idle capacity
A chip that is faster at saturation may be poorer value if your traffic is intermittent and forces you to keep large instances running. Conversely, a lower peak score can win when it supports finer-grained scaling or keeps TTFT within target at your normal load. Compare the utilization profile over the whole day and the capacity required for bursts, not only a laboratory maximum.
5. Understand what current custom-chip examples do—and do not—prove
AWS Inferentia2
AWS describes Inferentia2 as delivering up to 4× the throughput and up to 10× lower latency than first-generation Inferentia. The same AWS product information states that each chip provides 32 GB of HBM and that EC2 Inf2 instances support scale-out distributed inference. These are AWS-published comparisons with AWS’s own first-generation Inferentia and are not comparisons with a GPU. Treat them as reasons to test the platform, not as evidence of a universal advantage.
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Google announced Ironwood on April 9, 2025, calling it the company’s seventh-generation TPU and its first designed specifically for inference. Google reports 192 GB of HBM per chip, 7.37 TB/s of HBM bandwidth, 1.2 TB/s of bidirectional inter-chip interconnect bandwidth and 4,614 TFLOPs of peak compute per chip. Announced configurations include 256-chip and 9,216-chip systems.
Those figures are dated Google specifications and vendor claims. They do not establish latency, throughput or cost for your model, precision, serving framework or traffic distribution. Verify memory availability, compiler support and measured SLO performance in the configuration you would actually deploy.
Why the original TPU result is historical context only
Jouppi and colleagues’ 2017 paper compared the first Google TPU with contemporary CPU and NVIDIA K80 GPU deployments on Google datacenter neural-network workloads. It reported roughly 15–30× higher speed and 30–80× higher TOPS per watt for the TPU on those workloads. The comparison is valuable evidence that a specialized design can excel on a defined workload, but the hardware, models and software are historical. It cannot predict a modern GPU-versus-ASIC result.
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GPU or custom chip: decision criteria
| Question | Favors a custom inference chip when… | Favors a GPU when… |
|---|---|---|
| Model stability | The architecture and operators are stable enough to justify compiler and kernel tuning. | Models, frameworks or operators change frequently and portability has high value. |
| Memory fit | The chip’s memory and interconnect fit the model and planned concurrency without awkward partitioning. | The GPU configuration offers the needed memory or a simpler scaling path. |
| Software effort | Required operators and precision are supported with acceptable conversion and maintenance work. | Your existing serving stack already supports the model and team expertise. |
| Latency and throughput | Matched tests show the required TTFT, token rate, tail latency and sustained capacity. | Matched tests show better SLO performance or easier scaling for the actual traffic. |
| Economics | The complete qualifying deployment costs less after instance count and headroom. | Utilization, pricing or operational simplicity makes the total deployment cheaper. |
| Ownership model | Power and thermal efficiency matter for owned infrastructure and are demonstrated under sustained load. | Cloud flexibility or an established operations path outweighs potential hardware efficiency. |
Use this table to frame experiments, not to award a category victory in advance. A “custom chip” is not a single design, and “GPU” does not describe one memory size, generation or software stack.
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A practical selection procedure
- Freeze the workload profile. Record model, architecture, parameter size, precision, quantization, prompt and output distributions, concurrency, peak request rate, TTFT and end-to-end SLOs.
- Inventory each candidate’s limits. Verify memory for weights, runtime state and key-value cache; list supported operators, precision modes, compiler requirements and serving-framework integrations.
- Remove non-viable options. Exclude any candidate that cannot fit the model, requires unacceptable host fallback or needs porting work that the project cannot support.
- Run matched tests. Use identical model behavior and traffic controls. Measure TTFT, generated-token speed, end-to-end and tail latency, concurrent requests, sustained requests per second, scaling behavior and resource utilization.
- Explain bottlenecks. Use microbenchmarks and roofline analysis to distinguish compute, memory, transfer, network, power and thermal limits.
- Size peak deployment. Convert measured sustained capacity into the accelerator and instance count required for peak traffic, then add explicit headroom and failover capacity.
- Price only qualifying configurations. Include cloud or owned-infrastructure costs, operations, networking, power and cooling where applicable. Select the lowest practical cost that still meets every SLO.
- Set a retest trigger. Repeat the comparison after a material model, quantization, runtime, instance or traffic change. A result from one request shape should not be carried over to another without evidence.
Common mistakes and how to avoid them
Choosing from peak TOPS
Peak arithmetic does not reveal memory pressure, unsupported operators, queueing or interconnect overhead. Require model-level and traffic-level measurements.
Using a vendor’s cross-category chart as proof
Label every vendor number with its owner, date, comparison baseline and workload scope. Inferentia2’s published multipliers compare with first-generation Inferentia; Ironwood’s specifications are Google announcements. Neither is a matched GPU comparison for your service.
Benchmarking one request at a time
Single-request results can conceal queueing, batching effects and memory exhaustion. Test the concurrency and peak request mix that define your SLO.
Reporting throughput without TTFT
High aggregate throughput can coexist with a slow first token. Report TTFT, end-to-end latency, generated-token speed and sustained requests per second together.
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Comparing different software stacks
A faster result obtained with a different quantization, backend, prompt length or batching policy is not an apples-to-apples result. Align the stack or disclose and quantify the difference.
Ignoring the cost of porting
Compiler integration, custom kernels, model conversion and future framework upgrades are deployment costs. Include them when a platform requires them.
Overlooking thermal and network behavior
Owned systems need sustained power and thermal measurements, not just a short benchmark. Multi-chip serving needs communication measurements, because synchronization and transfers can dominate at scale.
How to interpret a benchmark result
A credible result should let another engineer reproduce the comparison: model revision, precision and quantization; input and output distributions; concurrency and request rate; serving framework and backend versions; warm-up procedure; hardware topology; measurement interval; latency percentiles; throughput definition; and the instance count used.
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Re-run the test when the model, quantization, runtime, instance type or request distribution changes. The available guidance supports a decision framework and benchmarking method, not an independent current head-to-head performance or cost verdict for an unspecified workload.
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