Choose an inference accelerator by testing your actual model and serving requirements, not by comparing peak compute numbers. NVIDIA is a sensible baseline when your models and production stack already fit its ecosystem; AMD Instinct, Intel Gaudi, AWS Inferentia2, and Google Cloud TPUs are alternatives to evaluate when their software paths, memory, deployment options, and measured cost fit the same workload.
Start with the inference workload
Before comparing accelerator brands, write down what the system must serve. An option that cannot fit the model or meet the response-time target is not a contender, no matter how strong its peak specifications look.
- Model: Record the exact model and architecture, checkpoint, quality target, and any quantization you intend to use.
- Memory demand: Account for model weights, runtime overhead, and the memory needed for the context lengths and concurrent requests you expect. Check usable memory in the proposed system, not only a chip’s headline capacity.
- Traffic shape: Estimate concurrent users, batch size, prompt and output lengths, and how those requests vary over time.
- Service objective: Set a latency or interactivity target as well as a throughput target. A system’s tokens per second is not meaningful for your decision unless you know the response time and quality at which it achieves that rate.
- Deployment: Decide whether the workload must run in your own facilities, in a particular cloud, or in either setting. Include regional availability and operational constraints.
Memory and communication between accelerators can rule out a configuration before compute throughput becomes relevant. Google Cloud’s inference guidance separates small-model, single-host large-model, and multi-host large-model cases; its 260 GB model example illustrates why the size of a model can change the required deployment shape.
Compare the whole serving system, not just the chip
The result users experience comes from an assembled system: accelerator, host CPU and memory, interconnect, serving framework, model kernels, scheduler, precision, and the way requests are batched. For an owned system, power, cooling, utilization, and support also affect the economics. For a cloud system, include the instance family, region, network and host capacity, and billing model.
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Software support is part of the hardware decision. A framework name alone does not prove that your exact model, operators, precision, and serving engine have a supported, optimized production path. Check the versions you plan to deploy, then measure porting and maintenance effort as well as runtime performance. NVIDIA Triton documentation, for example, shows that backend support varies by platform.
For a game studio or game-service team evaluating model inference, apply the same test to the actual service: whether it serves players or supports an internal workflow. The right deployment depends on its model, request pattern, latency target, and operational boundaries—not on the fact that it is connected to a game.
Which alternatives are worth evaluating?
The options below are not interchangeable product categories. Inferentia2 and the TPU examples are provider-specific cloud paths, while Instinct, Gaudi, and NVIDIA accelerators can be evaluated as hardware within a broader system decision. The listed figures describe different things—device memory, bandwidth, or deployment context—and should not be combined into a performance ranking.
Rank #2
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| Option | What the available evidence establishes | What to verify for your workload |
|---|---|---|
| NVIDIA GPUs | A reasonable baseline when the model and serving path already fit NVIDIA’s ecosystem. Google Cloud’s guidance uses L4 for small-model inference and H100 and B200 for progressively larger hosted cases; it lists 24 GB of memory per L4 GPU. | Exact device memory and server topology; model and runtime support; target-market or regional availability and price; and latency and throughput at your own concurrency. |
| AMD Instinct | AMD describes ROCm as a programming, compiler, library, tool, and runtime stack for Instinct. AMD lists MI325X with 256 GB HBM3E and 6 TB/s peak theoretical memory bandwidth; the product page dates the calculation basis for that specification to 2024. | Support in ROCm for the exact model and serving stack, system availability, porting effort, and performance on a matched workload. The memory and bandwidth specifications are fit indicators, not an end-to-end guarantee. |
| Intel Gaudi | Intel provides model references, libraries, containers, tools, and performance material for deploying generative AI and LLMs on Gaudi. | Request model-specific inference results for your required workload and configuration. An overview of the software offering alone does not establish parity or a cost advantage over GPUs. |
| AWS Inferentia2 | AWS offers this purpose-built inference accelerator through EC2 Inf2 and its Neuron software path. AWS documentation lists 32 GiB of HBM per chip and up to 12 chips in an Inf2 instance. Current AWS Neuron architecture documentation also lists 820 GiB/s memory bandwidth per chip. | Check Neuron support for the model, required operators, and serving engine; confirm Inf2 availability and current regional pricing. This is an AWS-specific deployment path, so account for the implications of tying the workload to it. |
| Google Cloud TPU | Google Cloud lists TPU v5e and v6e for small and multi-host inference scenarios and describes differing workload specializations and cost/performance considerations. | Confirm that your model code and serving stack map to the chosen generation, and that the required region, scale, and measured service objective are available. |
Run a fair benchmark against your service objective
A useful comparison changes the accelerator while holding the workload steady. Choose representative production requests and keep the model checkpoint, quality, precision or quantization, input and output distribution, batch and concurrency, and latency target constant. Where relevant, record prompt-processing and generation behavior separately. Report throughput alongside latency and quality rather than as a standalone headline.
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- Define the test: Select the model and request mix that represent the service, including the longest contexts and busiest concurrency you need to support.
- Freeze the configuration: Record model and software versions, precision, serving engine, scheduler settings, host and network components, accelerator type, and device count.
- Measure the required operating point: Capture quality, latency, and throughput together. If a system misses the latency or quality target, its peak throughput does not make it a valid substitute.
- Measure deployment economics: For owned equipment, disclose utilization and power assumptions and include facility and support costs. For cloud, record instance family, region, and billing assumptions, as well as host and network needs.
- Repeat under representative load: Include low-utilization periods and realistic traffic variation in the cost model, not only a continuously saturated test.
MLPerf Inference provides standardized results when its models, datasets, scenarios, and submitted configurations resemble your needs. It cannot represent every deployment, so inspect the individual result configuration and use the benchmark as a reference point before testing your own workload. MLPerf Inference v6.0, released in 2026, added GPT-OSS 120B and expanded interactive DeepSeek-R1 testing, among other changes; 24 organizations submitted results. Those facts describe the suite, not a ranking of the options in this article.
Read vendor benchmark figures in context
AMD’s May 2026 comparison illustrates how much a result depends on the chosen software stack and configuration. At a stated DeepSeek-R1 operating point of 129 tokens per second per user, AMD reported the following:
Rank #3
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| Configuration reported by AMD | Reported cost per million tokens | Reported throughput per GPU | Devices reported |
|---|---|---|---|
| MI355X with MoRI/SGLang | $0.173 | 2,378 tokens/second/GPU | 24 GPUs |
| B200 with Dynamo/TRT-LLM | $0.178 | 3,128 tokens/second/GPU | 28 GPUs |
| B200 with Dynamo/SGLang | $0.284 | 1,945 tokens/second/GPU | 48 GPUs |
These are AMD-published figures for a particular workload, target, and set of configurations—not independent proof that one vendor is always faster or cheaper. The two B200 rows alone use different serving stacks and device counts, which is why a price-per-token figure should not be separated from its setup. Treat vendor results as leads for candidates to reproduce, not as a substitute for a matched test of your service.
Calculate cost per delivered output
Use the cost of meeting your target latency and quality as the comparison unit. A low hardware or instance price can be offset if the system needs more devices, host capacity, engineering time, or operational support to serve the same traffic.
- Owned infrastructure: Include accelerator and server procurement, power, cooling, facilities, networking, maintenance, and staff. Model the utilization you can realistically sustain.
- Cloud infrastructure: Include accelerator instances, host and network capacity, region, billing assumptions, and periods when provisioned capacity is underused.
- Porting and operation: Account for engineering work to adapt kernels, operators, quantization, deployment tools, and monitoring, plus the ongoing cost of maintaining that path.
- Service quality: Compare cost only after confirming the systems deliver the same model quality and meet the same latency objective.
Current prices and regional availability change. The available evidence does not establish a live, apples-to-apples price comparison across NVIDIA, AMD, Intel, Inferentia2, and TPUs, so use current quotes or provider pricing for the region and configuration you would actually deploy.
Rank #4
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Make the decision by deployment type
Choose a familiar path when switching adds risk without a measured benefit
If your model and production stack already run well on NVIDIA, retain it as the baseline. Compare alternatives only after fixing the workload and service objective; a new accelerator’s theoretical specifications do not justify porting work by themselves.
Consider a cloud accelerator when the provider path fits the model
Inferentia2 and TPU deployments can be candidates when managed cloud operation is acceptable and the model maps to the provider’s supported software stack. Include region and capacity availability, portability, and the operational consequences of using a provider-specific path alongside measured cost.
Evaluate owned systems as infrastructure, not a chip purchase
For datacenter deployments, include procurement, facility readiness, power and cooling, achievable utilization, networking, and the staffing needed to operate the system. A chip-level specification cannot answer whether a complete server deployment is practical for your organization.
Best Value
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Use a proof of concept to settle close decisions
When more than one option appears to fit, run a same-version proof of concept with the production-like model and request distribution. Require the vendor or systems provider to identify the full configuration and demonstrate the latency, throughput, quality, and cost assumptions you will use in production. A benchmark that changes the model, precision, serving stack, or operating point is useful for exploration, but it cannot settle a direct buying comparison.
Recheck software support, product availability, and cloud pricing near the decision date: the cited Google Cloud guidance, AWS Neuron documentation, and vendor product material are volatile. The strongest recommendation is the option that meets your measured service objective at acceptable total cost with a supportable production path—not a universal winner inferred from one number.
Quick Recap
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