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Choose inference hardware by starting with the service you must deliver, not by ranking accelerator chips. Define the model, context and output lengths, request rate, concurrency, first-token and end-to-end latency targets, throughput objective, quality level, and facility limits. Then test complete systems—accelerator, host, networking, runtime, power and cooling—against that workload. A configuration that wins a theoretical benchmark can lose when your model does not fit in memory, your latency target prevents batching, or the rack cannot supply and remove its heat.
The practical decision is usually among GPU servers, managed accelerator platforms such as cloud TPUs, and CPU systems for bounded workloads. No source establishes a universal platform winner. The defensible choice is the system that meets your service-level objectives at an acceptable cost per useful token, with enough operational and facility headroom.
1. Turn the service into measurable hardware requirements
Before requesting quotations, write a workload specification that another team could reproduce. Inference behavior changes dramatically with sequence length, batching and concurrency, so an average request rate is not enough. NVIDIA’s cluster guidance treats throughput, latency, power and token economics as one design problem rather than separate scorecards (NVIDIA inference cluster hardware decisions).
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- Model identity: name, version, parameter count or mixture-of-experts structure, tokenizer and any adapters.
- Sequence distribution: input tokens, requested output tokens, maximum context and the percentile values that matter to your service.
- Traffic: requests per second, concurrency, burst pattern, geographic distribution and expected growth.
- Service objectives: time to first token, inter-token latency, end-to-end latency, tail percentile (such as p95 or p99), and minimum sustained output-token rate.
- Quality and policy: acceptable quantization or compression, determinism requirements, safety filters and data-residency constraints.
- Workload class: interactive chat and agent requests, online scoring, or offline and batch jobs. Interactive traffic generally values predictable latency; batch traffic can trade latency for utilization.
Separate the latency budget into queueing, prefill (processing the prompt), decode (generating tokens), network transfer and any post-processing. This prevents a fast accelerator from being blamed for time spent waiting in a queue or moving data between services.
Define capacity and failure targets
State whether the cluster must survive an accelerator, server, rack or site failure, and how much spare capacity is required during maintenance. A design that exactly meets peak throughput has no room for failover or rolling software upgrades. Specify the utilization level at which service-level objectives must still hold; the sustainable point is often below the device’s headline maximum.
2. Make memory fit the first hard gate
For large-language-model serving, device memory holds model weights and the key-value (KV) cache used by active sequences. Google Cloud’s guidance emphasizes checking both when selecting GPUs for LLM serving (Selecting GPUs for LLM serving on GKE). A model can have enough room for weights and still fail when context length or concurrency expands the KV cache.
Estimate the complete footprint
- Weights: calculate the serialized model size at the precision you will deploy, then allow room for framework metadata and loading overhead.
- Runtime allocations: include activation buffers, temporary workspaces, communication buffers and allocator fragmentation.
- KV cache: size it for the maximum simultaneous sequences and their context plus generated tokens. Long-context and high-concurrency services can make this term larger than expected.
- Operational headroom: reserve capacity for batching variation, rolling updates and model versions loaded together.
Reject a candidate when this total cannot fit without an unacceptable sharding, paging or host-offload strategy. Offloading may reduce device-memory pressure but adds data movement and can make tail latency unpredictable. If a model is split across devices, account for the interconnect bandwidth and topology, not just the sum of capacities.
Check bandwidth and movement, not capacity alone
Decode repeatedly reads weights and KV data, so memory bandwidth and the path between accelerators can matter as much as arithmetic throughput. Document whether tensor or pipeline parallelism crosses a high-speed accelerator link, PCIe, or the network. A nominally larger device can deliver worse service if traffic is forced through a slower path or if host memory becomes the bottleneck.
3. Compare compute, precision and software as one system
Theoretical operations per second are useful for narrowing options, but the runtime determines whether those operations become delivered tokens. Compare the exact model, kernels, batching behavior, communication libraries, compiler path and observability tools you will operate.
Evaluate precision choices with quality gates
Quantized formats such as FP8, INT8 and INT4 can improve inference throughput per dollar when the model and runtime support them. Google’s GPU documentation describes quantized formats as an inference option (Understand GPUs). Measure answer quality, calibration behavior, safety performance and long-context accuracy before accepting a lower-precision configuration. A cheaper token is not useful if it causes retries, human review or a quality breach.
Verify runtime coverage
- Confirm that your model architecture, custom operators and tokenizer are supported by the intended serving framework.
- Check that the framework exposes the precision modes you plan to use and that kernels are optimized for the selected accelerator.
- Test continuous batching, speculative decoding or prefix caching only when your traffic shape benefits from them; each changes memory use and latency.
- Measure startup and model-load time, checkpoint format compatibility, health checks and rolling-upgrade behavior.
- Include monitoring for queue depth, first-token latency, inter-token latency, KV-cache occupancy, device utilization, throttling and errors.
Software maturity is a procurement attribute. A device with excellent silicon but immature drivers, compiler support or orchestration can cost more engineering time than a less novel platform that your team can maintain.
4. Choose the scale of the system
Single accelerator or multi-device server
A single device is simplest when the model and KV cache fit with headroom and the required throughput is modest. A multi-GPU server is appropriate when memory capacity, throughput or redundancy requires it. With multiple devices, determine whether the workload is replicated across devices for independent requests or sharded across them for one model instance. Replication simplifies failure handling; sharding can fit larger models but increases communication sensitivity.
Rack-scale deployment
Rack-scale systems can provide the links and network topology needed for large models and high concurrency, but they concentrate power and cooling. NVIDIA’s inference reference architecture documents different assumptions for GPU, rack, network, DPU, power, cooling and tenancy profiles (NVIDIA Inference Reference Architecture). Treat each profile as a complete design envelope rather than transferring a server specification into an existing rack without review.
Host and I/O requirements
The host CPU handles request processing, tokenization, scheduling, data movement and control-plane work. Host memory must hold the model during load, staging buffers and any offloaded state. Check PCIe generation and lane allocation, NUMA placement, storage throughput for checkpoint loading, and the network adapter’s topology relative to the accelerators. NVIDIA’s certified configuration guide cites PCIe Gen5-or-later-capable CPUs and gives Grace, Intel Xeon Scalable and AMD EPYC as example host families (NVIDIA-Certified Systems Configuration Guide). The cited families are examples, not a universal bill of materials.
5. Compare platform paths without assuming a winner
| Platform path | Where it can fit | Questions to validate |
|---|---|---|
| GPU server | Broad range of online and batch inference, including multi-device model serving | Accelerator memory and bandwidth; interconnect and network topology; runtime and precision support; rack power and cooling; system availability and support |
| Cloud TPU or managed accelerator | Provider-hosted inference and reinforcement-learning workloads, including low-latency agentic and mixture-of-experts use cases described for TPU 8i | Model and framework portability; regional availability; cloud price and egress; quota and tenancy behavior; measured latency and throughput |
| CPU-only server | Small-to-mid-sized models, lower or variable utilization, and deployments where accelerator capacity or placement is constrained | Actual model latency, concurrency, vectorization and memory bandwidth; software configuration; power at the required throughput |
| Announced or emerging accelerator | Future evaluations where high memory capacity or energy efficiency is a stated design goal | Release status, production supply, software support, independent benchmarks and serviceability |
GPU servers
GPU servers remain the main physical category to evaluate for a new inference cluster, but “GPU server” is not a single specification. Memory configuration, links, networking, rack profile, power and cooling differ by system. Use certified configurations as compatibility evidence, then benchmark the exact server and software stack for your model.
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Google Cloud describes TPU 8i as designed for inference and reinforcement learning, including low-latency agentic and mixture-of-experts workloads (AI infrastructure at Next ’26). This is a provider announcement, not a cross-platform performance guarantee. Include cloud pricing, quota, availability, data-transfer charges and operational controls in the same test plan as owned hardware.
Rank #3
- Number of Processor Cores: 512
- Processor Core Clock: 1.3GHz
- Memory Clock: 1.8GHz
- Memory Size: 6GB
- Peak single precision floating point performance: 1331 Gigaflops
CPU inference
CPU serving can be sensible when models are small enough, utilization is uneven, or deployment constraints make accelerators impractical. AMD states that modern CPUs can run small-to-mid-sized inference workloads with sub-second latency while cautioning that results vary with system and software configuration (AMD inference infrastructure infographic). Treat that statement as a reason to test CPUs, not as evidence that they match accelerator performance. Measure the intended model, precision, concurrency and tail latency.
Announced accelerators
Intel announced Crescent Island as a planned data-center GPU optimized for inference, with high memory capacity and energy-efficient operation as design goals (Intel accelerator announcement). The announcement does not establish current availability or independent performance. Do not base a deployment schedule on it until production status, specifications, software and support are confirmed directly.
6. Translate the server into rack and facility requirements
Facility planning starts with the complete system’s electrical draw, not the accelerator’s board rating. Add host CPUs, memory, storage, networking, fans, power-conversion losses and any cooling infrastructure. Convert expected draw into rack power at the utilization level required by your service, then check the feed, breaker, busway, redundancy and expansion plan.
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Power and cooling checks
- Calculate sustained and peak power for the tested workload, including startup and model-loading events.
- Confirm rack power density and whether the room supports the required air or liquid-cooling approach.
- Verify inlet-temperature, humidity and altitude limits with the system supplier and facility engineer.
- Reserve capacity for failover, maintenance and future model growth rather than filling every rack to its nameplate limit.
- Measure performance under thermal steady state; short tests can hide throttling that appears during long generations.
NVIDIA’s reference architecture makes clear that rack, network, power, cooling and tenancy assumptions differ by platform profile (Inference Reference Architecture). A server that fits physically may still fail a deployment review because its heat rejection or network design does not match the site.
Network and storage
Scale-out serving needs predictable east-west bandwidth and low tail latency. Map every accelerator link, network adapter and switch hop, and decide whether traffic is model-shard communication, request distribution or storage access. Benchmark model loading and recovery from the storage tier; a cluster that takes too long to reload after failure needs more replicas or a different checkpoint strategy.
7. Build a cost-per-token model that matches the service
Cost economics are meaningful only when quality, latency and workload are held constant. Use a simple model such as (hardware or cloud cost + power + cooling + software + operations) / useful output tokens, and define “useful” as tokens delivered while meeting the service objective. Include idle capacity reserved for bursts and failures, not only accelerator time at peak utilization.
Rank #4
- Memory Size: 16 GB GDDR6 ECC.
- Memory Bus Width: 128-bit.
- Memory Bandwidth: 200 GB/s.
- CUDA Cores: 1280.
- Peak Single Precision floating point performance: 18 Tflops (GPU Boost Clocks).
Separate one-time and recurring inputs
- Capital system price, networking, racks, power delivery and installation.
- Cloud instance, storage, data-transfer and support charges where applicable.
- Electricity, cooling, space, maintenance, warranty and staffing.
- Software subscriptions or support contracts, plus engineering work to port and maintain the runtime.
- Capacity held for redundancy, upgrades and demand growth.
Regional street prices, lead times and energy rates are not established by the available evidence, so obtain current quotations for your location. Do not use a vendor’s price-per-token claim as a budget without reproducing its assumptions.
8. Benchmark the production service shape
Run a bake-off with identical software and test conditions wherever the platform permits. Use representative prompts and output lengths, the same model revision and tokenizer, the same quantization, concurrency, batching policy and request mix. Test both warm steady state and cold start or reload behavior.
Metrics to collect
- Time to first token, inter-token latency and end-to-end latency at p50, p95 and p99.
- Accepted input and output tokens per second, requests per second and sustainable throughput.
- Queueing delay, batch size, accelerator and host utilization, memory occupancy and KV-cache pressure.
- Power at idle, during representative load and at the thermal steady state.
- Error rate, retries, quality scores and degradation under overload or a failed device.
- Capitalized and operating cost assumptions used to derive cost per token.
Use a test matrix, not one headline number
Vary context length, output length, concurrency, prompt mix and precision. A configuration that wins short prompts at low concurrency may lose long-context agent traffic. Test the latency target at the throughput level you will actually sell; maximizing throughput by allowing an unacceptable queue is not a service win.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.9. Interpret published performance claims correctly
Published figures are scoped evidence, not universal rankings. NVIDIA reports up to 50 times higher throughput per megawatt and up to 35% lower cost per token for GB300 NVL72 versus Hopper in low-latency agentic workloads, citing SemiAnalysis InferenceX benchmarks from the first quarter of 2026 (NVIDIA Deep Learning Performance hub). The “up to” values apply to that named comparison and workload class; they do not predict another model, context distribution, utilization or electricity market.
NVIDIA also reports 45% higher DeepSeek-R1 offline-inference throughput for GB300 NVL72 than GB200 NVL72 in a reported MLPerf Inference v5.1 scenario (Blackwell Ultra MLPerf Inference benchmark). This is a vendor report of one benchmark scenario, not a general measure of online service performance. Look for the exact model, batch mode, precision, latency target and system configuration before applying any published result.
10. A procurement workflow that reduces rework
- Freeze the service definition. Record model, context and output distributions, traffic, concurrency, latency percentiles, quality and availability targets.
- Gate on memory. Calculate weights, runtime overhead and KV cache, then remove systems that need unacceptable offload or sharding.
- Shortlist complete platforms. Compare GPU servers, managed accelerators and CPUs with their host, links, network, storage, runtime and support—not accelerator cards alone.
- Model facility impact. Validate rack dimensions, power delivery, cooling, redundancy, network topology and deployment space with the data-center team.
- Run the matched benchmark. Use production-like prompts, lengths, concurrency, precision and serving software; collect latency, throughput, power, quality and failure data.
- Calculate useful-token economics. Include idle and failover capacity, power, cooling, software, operations and regional pricing assumptions.
- Review supply and lifecycle risk. Confirm lead time, support, firmware and driver cadence, spare strategy, warranty and the ability to add capacity without redesigning the rack.
- Approve with an evidence register. Keep measured results separate from vendor claims and document every assumption that affects the decision.
11. Common failure modes and corrective actions
The model fits, but latency misses
Check queueing, prefill cost, KV-cache pressure, interconnect hops and CPU-side tokenization before replacing the accelerator. Reduce batch size, improve placement, or select a topology with faster links only after identifying the measured bottleneck.
Throughput collapses at realistic concurrency
Inspect memory fragmentation, cache eviction, thermal throttling and network contention. Re-run with the production context distribution; a synthetic short-prompt test can conceal long-context limits.
Quantization lowers cost but harms answers
Compare task quality and safety metrics at the same prompts and random seeds where appropriate. Try a different quantization level or mixed-precision policy, and include the cost of retries and human review in the economic model.
The rack cannot support the selected system
Reconcile measured peak draw, cooling method, breaker capacity, rack weight and network cabling with the facility design. A lower-density configuration or additional rack may be cheaper than retrofitting power and cooling after purchase.
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A vendor benchmark cannot be reproduced
Check model revision, precision, software version, batch policy, input and output lengths, latency definition and whether the result is offline or online. If any condition differs, label the number as directional and rely on your matched test for the decision.
12. The decision rule
Select the least complex platform that meets the defined quality, latency, throughput, availability and facility requirements with measured headroom. Choose GPUs when their memory, links and mature runtime ecosystem fit the service; evaluate managed TPUs when provider integration and economics work for your model; consider CPUs when the workload is bounded and accelerator utilization would be poor. Keep emerging parts in a separate evaluation track until availability and independent evidence are real. The winning design is the one that delivers reliable useful tokens in your data center—not the chip with the largest theoretical specification.
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