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The right inference accelerator is the least expensive device that fits your model, KV cache, latency target, and serving software at the required utilization. Start with a memory calculation, reject hardware that cannot hold the workload, benchmark the remaining systems at production concurrency, then compare cost per delivered token. A headline specification or vendor benchmark is not enough.
Start with the service-level objective, not the GPU model
Write down the workload before looking at accelerator products. The same language model can favor very different hardware when one service needs interactive responses and another needs maximum offline throughput.
Record these workload variables
- Model: family, parameter count, architecture, and whether it uses mixture-of-experts routing.
- Precision: FP32, FP16, BF16, FP8, INT8, INT4, or another quantization scheme.
- Context: maximum input and output length, including the longest supported conversation or document.
- KV cache: cache size per request and the amount of headroom required at peak concurrency.
- Traffic: requests per second, burst behavior, simultaneous users, and expected batch size.
- Latency: time to first token (TTFT), time per generated token, end-to-end latency, and p95 or p99 limits.
- Throughput: generated tokens per second, input tokens per second, and completed requests per second.
- Serving stack: framework, compiler, kernels, quantization library, scheduler, and monitoring system.
For an interactive assistant, p95 TTFT and tail latency may matter more than peak offline tokens per second. For summarization or embedding jobs, sustained throughput and utilization usually dominate. Put the exact SLOs in the procurement document so every benchmark uses the same acceptance criteria.
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Memory capacity is a hard constraint. If weights, KV cache, runtime buffers, and operating headroom do not fit, a faster accelerator is irrelevant unless you are willing to add tensor or pipeline parallelism.
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Estimate the footprint
Begin with the weight estimate:
weight memory ≈ parameter count × bytes per parameter
As arithmetic examples, a 70-billion-parameter model needs about 140 GB for uncompressed 16-bit weights and about 70 GB for 8-bit weights, before metadata, activations, the KV cache, and framework overhead. Actual allocations vary by architecture and implementation.
Then add the KV cache for the maximum simultaneous requests. Cache consumption rises with context length and concurrency, so a model that fits for one short prompt can fail when many users send long prompts. Add space for temporary activations, CUDA or ROCm allocations, communication buffers, graph capture, and a safety margin for version changes.
Use capacity examples as an elimination filter
AWS’s inference-sizing examples list the following accelerator memory capacities:
| Accelerator example | Memory listed | Immediate implication |
|---|---|---|
| L4 | 22 GB | Suitable only for workloads whose complete runtime footprint stays below that limit. |
| L40S | 44 GB | More headroom than L4, but still limited for large unquantized models. |
| H100 | 80 GB | Broadly capable, yet restrictive for models requiring more than 80 GB on one device. |
| H200 | 141 GB | Designed for larger single-device footprints than an 80 GB H100 example. |
| B200 | 180 GB | Provides additional capacity for large-model inference. |
| B300 | 268 GB | Offers the largest capacity in this example set. |
If the calculated requirement is 90 GB, the single-GPU L4, L40S, and H100 examples are eliminated regardless of their throughput. You must select a larger-memory device or explicitly design and test a multi-GPU deployment.
Decide whether partitioning is acceptable
Tensor parallelism divides layers or matrix operations across GPUs; pipeline parallelism places groups of layers on different devices. Both can make a model fit, but they add synchronization, communication traffic, operational complexity, and failure domains. Treat a multi-GPU solution as a separate architecture, not as a free extension of a single-GPU benchmark.
Compare accelerators on the dimensions that affect inference
Capacity is only the first filter. Compare memory bandwidth, interconnects, software support, operations, and economics under the same workload.
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The H100 is a general-purpose production choice when the team values a mature NVIDIA software ecosystem and scale-out options. NVIDIA specifies 3 TB/s of memory bandwidth per GPU. NVLink and NVSwitch can provide high-bandwidth communication in multi-GPU systems, while TensorRT, Triton, and NIM offer widely used inference paths.
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Its 80 GB class memory can be the limiting factor for larger models or high-concurrency KV caches. Validate current purchase or rental pricing, supply, and the exact server configuration rather than assuming that a familiar model is automatically the lowest-cost option.
NVIDIA H200
The H200 is a higher-memory Hopper option. The AWS example lists 141 GB, making it a candidate for models that exceed an 80 GB H100 footprint but can still run on one device or fewer devices. Confirm that the server’s PCIe layout, firmware, cooling, and software stack support the selected H200 configuration and that the platform is available in your region.
AMD Instinct MI300X
AMD lists 192 GB of HBM3 and 5.325 TB/s of peak memory bandwidth for MI300X in Performance Labs material from 2023. That combination is attractive when model capacity and memory movement are the bottlenecks. AMD’s MLPerf material reports single-GPU support for Llama 2 70B and nearly linear ROCm scaling in its tested configurations.
The trade-off is software validation. Check ROCm versions, framework releases, kernels, operators, quantization support, compiler behavior, and observability for the exact model. A theoretical fit does not prove that your serving stack will reach its target latency.
Intel Gaudi 2
Intel positions Gaudi 2 around price-performance and reports MLPerf Inference v4.0 results of 8,035.0 offline tokens per second and 6,287.5 server tokens per second for Llama 2 70B. The cited submission used Hugging Face Text Generation Inference (TGI), which supports continuous batching and tensor parallelism in that test path.
Before standardizing, confirm model coverage, framework integration, operator support, local availability, networking, and the team’s ability to operate Intel’s software stack. A benchmark result for one model and submission configuration cannot establish performance for every model.
Specification and decision table
| Option | Evidence-based strengths | Decision cautions | Good fit |
|---|---|---|---|
| NVIDIA H100 | 3 TB/s bandwidth; NVLink/NVSwitch; broad TensorRT, Triton, and NIM ecosystem. | 80 GB class capacity may constrain large models; verify price and supply. | Production inference where software maturity and scale-out are priorities. |
| NVIDIA H200 | 141 GB in the AWS example; more memory than the H100 example. | Requires suitable topology and current platform availability. | Larger models that fit with NVIDIA software and fewer GPUs. |
| AMD Instinct MI300X | 192 GB HBM3 and 5.325 TB/s peak bandwidth; reported Llama 2 70B single-GPU support. | Validate ROCm kernels, operators, framework, and quantization for your model. | Memory-heavy inference with ROCm-capable teams. |
| Intel Gaudi 2 | Reported MLPerf Llama 2 70B results and a TGI path with continuous batching and tensor parallelism. | Confirm model coverage, integration, supply, and operational expertise. | Cost-sensitive deployments using supported models. |
Benchmark practical serving performance
After the memory gate, run a controlled benchmark using the production model, precision, tokenizer, compiler, serving engine, and driver versions. Do not substitute a different model or report only a vendor’s best-case number.
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- TTFT: time from request acceptance to the first generated token.
- Inter-token and end-to-end latency: report median, p95, and p99, not just an average.
- Throughput: input and output tokens per second, requests per second, and completed work at the target SLO.
- Batching efficiency: how throughput and latency change as batch size and concurrency rise.
- Memory behavior: peak HBM use, KV-cache growth, fragmentation, and out-of-memory failures.
- Scaling: efficiency when moving from one GPU to two, four, eight, or multiple nodes.
- Reliability: error rate, warm-up time, restart recovery, and behavior during device or node loss.
Use realistic test cases
Build a request mix from production or a representative trace: short and long prompts, different output lengths, cache-hit and cache-miss paths, and the expected burst pattern. Test at the concurrency that corresponds to your SLO. A device can fit the model yet fail the latency or throughput objective; AWS explicitly cautions that memory fit alone does not guarantee service-level performance.
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Record software versions and configuration in every result. Include graph-capture settings, quantization calibration, tensor-parallel degree, batch limits, power mode, host CPU, storage, and network topology. Re-run after driver, compiler, framework, or model changes.
Calculate cost per delivered work
Compare the cost of meeting the SLO, not the purchase price of an idle card. Include accelerator acquisition or rental, host servers, networking, storage, power, cooling, rack space, software support, staffing, and expected utilization.
Use relative figures only as directional evidence
AWS provides this illustrative relative table, with L4 set to 1.0x for both throughput and cost:
| Accelerator | Relative throughput | Relative cost | Interpretation |
|---|---|---|---|
| L4 | 1.0x | 1.0x | Baseline in the example. |
| L40S | 2.5x | 1.7x | Higher modeled throughput with a smaller relative cost increase. |
| H100 | 3.5x | 3.0x | Higher modeled performance and cost than the baseline. |
| H200 | 3.8x | 3.5x | Highest modeled values in this example. |
These ratios are illustrative, not universal rates. Workload shape, precision, software versions, concurrency, and infrastructure pricing can change the ranking. Compute a delivered-work figure such as:
cost per million output tokens = hourly infrastructure cost ÷ (sustained output tokens per hour ÷ 1,000,000)
Apply the formula only to runs that satisfy TTFT, p95 or p99 latency, error-rate, and availability requirements. A cheaper card that needs more replicas, wastes memory, or misses the SLO is not cheaper in production.
Account for system topology and operations
GPU specifications do not describe the complete inference server. NVIDIA’s system guidance recommends balanced two-, four-, or eight-GPU designs, at least twice the aggregate GPU memory in host RAM, PCIe links appropriate to the GPU, and balanced placement across CPU root ports.
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Infrastructure checks
- Host memory: provision at least two times aggregate GPU memory as a starting point, then validate actual framework demand.
- PCIe: confirm generation, lane width, switch placement, and that no GPU is attached through an unexpectedly narrow or shared path.
- CPU placement: distribute GPUs across balanced CPU root ports to avoid NUMA and PCIe bottlenecks.
- Networking: use at least 200 Gbps networking for multi-node inference, subject to the communication pattern and scaling test.
- Storage: use NVMe for model weights, tokenizer files, caches, logs, and fast node recovery.
- Management: require Redfish-capable management, firmware inventory, remote power control, and health telemetry.
- Security: require TPM 2.0 and secure boot, and define how images, drivers, and model artifacts are signed and updated.
- Thermals and power: verify rack power, cooling, sustained clocks, and behavior under the intended duty cycle.
For multi-GPU servers, ask the vendor for a topology diagram and validate peer-to-peer bandwidth with the same placement used in production. A nominal eight-GPU configuration can perform poorly if links, root ports, or network adapters are unbalanced.
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Choose by deployment scenario
Small models and modest concurrency
Start with lower-memory devices such as the 22 GB L4 example when the complete quantized model, KV cache, and runtime margin fit comfortably. Benchmark burst latency and utilization; a low acquisition price does not help if the service requires many replicas to handle peak traffic.
Large models that fit on one device
Favor capacity and memory bandwidth first. The 141 GB H200 example or 192 GB MI300X configuration can avoid tensor-parallel communication for footprints that exceed an 80 GB H100. Confirm that the model and software actually use the available memory efficiently.
Very large models or high concurrency
Plan multi-GPU or multi-node topology from the start. Measure scaling efficiency, all-reduce or collective-communication overhead, network saturation, failure recovery, and rolling-upgrade behavior. Keep enough KV-cache headroom for the peak concurrency rather than sizing only for the average.
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Cost-sensitive deployments
Evaluate Gaudi 2, MI300X, and lower-cost NVIDIA options alongside the incumbent platform when their software paths support the model. Require a reproducible benchmark and a three-year total-cost estimate, including engineering time and operational support.
Interpret benchmark claims correctly
Vendor results are configuration-specific. Intel reports a 1.42x geometric-mean improvement for 5th Generation Xeon over 4th Generation Xeon in MLPerf Inference v3.1; that is an aggregate benchmark result, not a promise for every inference server. AMD’s MLPerf Inference 4.1 analysis places MI300X within 2–3% of the cited DGX H100 configuration in its FP8 server and offline scenarios. The comparison is meaningful only within those tested configurations and software releases.
Intel corporate vice president Zane Ball said: “We continue to improve AI performance on industry-standard benchmarks across our portfolio of accelerators and CPUs. Today’s results demonstrate that we are delivering AI solutions that deliver to our customers’ dynamic and wide-ranging AI requirements. Both Intel Gaudi and Xeon products provide our customers with options that are ready to deploy and offer strong price-to-performance advantages.” Treat the statement as a vendor position, then verify your own workload.
No cited vendor material establishes a neutral, apples-to-apples price-per-token ranking across NVIDIA, AMD, and Intel. Current cloud prices, marketplace availability, supply, and support terms also change by region and date. Obtain quotes and reproduce the target model and serving stack before committing.
A procurement checklist
- Freeze the model version, tokenizer, precision, context limits, and serving software.
- Calculate weights, KV cache, runtime overhead, and safety margin at peak concurrency.
- Remove every accelerator or topology that fails the memory gate.
- Define TTFT, tail latency, throughput, availability, and error-rate acceptance thresholds.
- Benchmark each remaining option with identical traffic, software versions, and measurement methods.
- Verify multi-GPU links, PCIe placement, host memory, network bandwidth, NVMe, cooling, and management features.
- Calculate cost per delivered token or request at the accepted SLO and realistic utilization.
- Run a pilot long enough to expose thermal throttling, memory fragmentation, software regressions, and recovery behavior.
- Document firmware, drivers, container images, model artifacts, monitoring, and rollback procedures.
Bottom line: buy for the bottleneck you can prove
Choose the accelerator that clears the memory gate, meets measured latency and throughput targets, and delivers the lowest total cost at the required utilization. H100 is a strong general-purpose choice when NVIDIA software maturity and scale-out are decisive; H200 and MI300X are compelling when memory capacity is the constraint; Gaudi 2 deserves a serious cost-performance test where its software path supports the model. None is a universal winner. The defensible decision comes from a repeatable benchmark on your model, precision, concurrency, topology, and serving stack.
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