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There is no universally best AI inference chip or cloud. The right choice is the platform that produces your specific model’s answers at the required quality and p50/p95/p99 latency, for the lowest verified cost after accelerator time, host resources, networking, idle capacity, and engineering work. Compare GPUs, TPUs, and AWS Inferentia with the same model, precision, context length, batch size, concurrency, traffic pattern, and region; then keep a fallback where quota or outage risk warrants it.
Start with the workload, not the chip name
Inference performance is a property of a complete serving configuration. The same accelerator can look excellent for a short-context, highly batched model and poor for a long-context, low-concurrency application. A fair comparison therefore begins by freezing the workload variables that change both speed and cost.
Freeze the model and quality target
- Record the exact model checkpoint, tokenizer, revision, and supported maximum context.
- Choose the numerical format you will test: FP16, BF16, FP8, INT8, INT4, or another production format. Quantization can reduce memory and cost, but it can also change answer quality.
- Define a quality test before benchmarking. Use representative prompts and an acceptance threshold for factual accuracy, task success, safety behavior, or another metric your product depends on.
- Capture prompt and completion length distributions. Average tokens hide the cost of long-tail contexts and unusually large responses.
Freeze traffic and service objectives
- Specify target concurrency, requests per second, and burst shape. Include both steady-state traffic and spikes.
- Set service-level objectives for time to first token (TTFT), inter-token latency (ITL), and total response time. Measure p50, p95, and p99 rather than an average alone.
- Choose the batch policy you will operate: static batching, continuous or in-flight batching, or single-request execution.
- State availability, data-residency, security-boundary, and region requirements before looking at prices. A cheap region you cannot use is not a candidate.
Keep this test definition unchanged across providers. Changing the context length, quantization, or concurrency for one platform turns a comparison into a collection of unrelated demonstrations.
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Metrics that determine a useful comparison
Latency
Measure TTFT and ITL separately. TTFT reflects request queuing, prompt processing, and scheduling; ITL reflects token generation once streaming begins. Record p50 for typical behavior and p95/p99 for the tail users experience during contention. Run the measurements at several concurrency levels, because a configuration that wins at one request can lose when queues form.
#1 Best Overall
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- [Double-Flow-Through Design] The RTX PRO 6000 Blackwell features a double-flow-through cooling design, optimizing efficiency and airflow to sustain peak performance under 600W power loads. | [5th Gen Tensor Cores] Deliver up to 3X the performance of the previous generation and support for FP4 precision for faster AI model processing times with reduced memory usage, enabling local fine-tuning of LLMs and generative AI | [4th Gen Ray Tracing Cores] Double the ray-triangle intersection rate of the previous generation to create photoreal, physically accurate scenes and immersive 3D designs with RTX Mega Geometry, which enables up to 100X more ray-traced triangles.
- [PCIe Gen 5] Support for PCIe Gen 5 provides double the bandwidth of PCIe Gen 4, improving data-transfer speeds from CPU memory and unlocking faster performance for data-intensive tasks like AI, data science, and 3D modeling. | [GDDR7 Memory] With 96 GB of GPU memory and 1.8 TB ps bandwidth, it can tackle massive 3D and AI projects, fine-tune AI models locally, explore large-scale VR environments, and drive larger multi-app workflows.
- [DisplayPort 2.1] Achieve unparalleled visual clarity and performance, driving high resolution displays at up to 8K at 240 Hz and 16K at 60 Hz. Increased bandwidth enables seamless multi-monitor setups while HDR and higher color depth support ensures superior color accuracy for precision work, such as video editing, 3D design, and live broadcasting.
- [NVIDIA Blackwell Streaming Multiprocessor] The new SM features increased processing throughput, and new neural shaders that integrate neural networks inside of programmable shaders | DLSS 4: Multi Frame Generation ensures ultra-smooth frame pacing for lifelike simulations. | [Universal MIG] Divide a single RTX PRO 6000 Blackwell into multiple isolated instances, each with dedicated resources, allowing for concurrent execution of multiple workloads, optimized GPU utilization, and secure isolation of different applications or users. [WARRANTY] 3 YR Manufacturer's Warranty. Bulk OEM Packaging. Retail Packaging is NOT included.
Throughput
Report output tokens per second, requests per second, and throughput per accelerator at the batch size used. State whether the figure counts generated tokens only or prompt plus generated tokens. A high aggregate number is not useful if it requires a batch size that violates your latency target.
Memory fit and scaling
Account for model weights, activations, the key-value (KV) cache, runtime buffers, and headroom. KV-cache use grows with context length and concurrent sequences, so a model that fits for a short test can fail in production. For multi-chip serving, include memory bandwidth and interconnect behavior; communication can dominate when layers or tensor-parallel shards cross devices.
Software fit
Check framework and operator coverage, compiler maturity, quantization support, batching, paged KV caching, optimized kernels, profiling, monitoring, and portability. A theoretical accelerator advantage disappears if an unsupported operator forces a slow fallback or prevents the model from compiling.
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Compare cost per useful output token or per successful request, not just an hourly accelerator rate. Include the accelerator, host CPU and memory, storage, networking, egress, load balancers, idle headroom, reservations or other commitments, support, and engineering labor. “Useful” means output that meets the quality and latency thresholds; rejected or failed work should not improve the denominator.
Operations and lock-in
Evaluate quota, regional capacity, autoscaling, health checks, failure recovery, observability, security controls, data residency, and support. Also document portability of model code and weights, provider-specific SDKs, contract commitments, and the accelerator roadmap. A slightly higher measured cost can be rational if it substantially reduces migration or outage risk.
How the main options differ
NVIDIA data-center GPUs
NVIDIA’s TensorRT documentation lists A100, H100, and H200 as supported data-center GPUs. TensorRT transforms trained neural networks into optimized runtime engines for low-latency, high-throughput inference. TensorRT-LLM supports multi-GPU and multi-node execution, in-flight batching, paged KV caching, and FP8, INT8, and INT4 quantization.
Those capabilities make NVIDIA a strong baseline when you need broad model coverage, mature serving tools, and the option to move among cloud providers. Test the actual GPU generation and memory configuration: “GPU” is not a single performance tier. H100 and H200 capacity, for example, can differ materially from A100 for memory-heavy or communication-heavy workloads.
Most H100 capacity is rented through cloud or specialist infrastructure providers. Direct Amazon retail availability for H100 hardware was not verified, so treat a hardware-purchase assumption separately from a cloud-capacity plan.
AWS Inferentia and Inferentia2
AWS documents Inf1 and Inf2 EC2 families and the Neuron SDK for compiling, running, and profiling models on Inferentia. AWS states that first-generation Inferentia delivers up to 2.3× higher throughput and up to 70% lower cost per inference than comparable EC2 instances. AWS also states that Inferentia2 delivers up to 4× higher throughput and up to 10× lower latency compared with Inferentia.
These are AWS claims tied to the comparisons and conditions on its product material, not universal cross-vendor results. Reproduce the benchmark with your model and traffic before using either figure in a business case.
Rank #2
- NVIDIA Ampere Architecture-based CUDA Cores - Double-speed processing for single-precision floating point (FP32) operations and improved power efficiency provide significant performance improvements for graphics and simulation workflows, such as complex 3D computer-aided design (CAD) and computer-aided engineering (CAE), on the desktop.
- Second-Generation RT Cores - With up to 2X the throughput over the previous generation and the ability to concurrently run ray tracing with either shading or denoising capabilities, second-generation RT Cores deliver massive speedups for workloads like photorealistic rendering of movie content, architectural design evaluations, and virtual prototyping of product designs. This technology also speeds up the rendering of ray-traced motion blur for faster results with greater visual accuracy.
- Third-Generation Tensor Cores - New Tensor Float 32 (TF32) precision provides up to 5X the training throughput over the previous generation to accelerate AI and data science model training without requiring any code changes. Hardware support for structural sparsity doubles the throughput for inferencing. Tensor Cores also bring AI to graphics with capabilities like DLSS, AI denoising, and enhanced editing for select applications.
- Third-Generation NVIDIA NVLink - Increased GPU-to-GPU interconnect bandwidth provides a single scalable memory to accelerate graphics and compute workloads and tackle larger datasets.
- 48 Gigabytes (GB) of GPU Memory - Ultra-fast GDDR6 memory, scalable up to 96 GB with NVLink, gives data scientists, engineers, and creative professionals the large memory necessary to work with massive datasets and workloads like data science and simulation.
The practical gate is Neuron compatibility. Compile the complete model, inspect unsupported operators, and profile the generated graph. A migration that requires custom kernels or frequent fallbacks can erase an instance-rate advantage. Include compilation time and maintenance of Neuron-specific code in the engineering-cost line.
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Google publishes TPU chip, memory, bandwidth, and pod-scaling specifications. Its comparison guidance recommends microbenchmarks, roofline analysis, and model benchmarks, then dividing measured throughput by accelerator cost to obtain throughput per dollar.
Google reported a 2–4× inference-performance improvement and more than 2× cost-efficiency improvement for the offerings in its 2023 comparison. Google labels these as its own comparison results and notes that performance per dollar is not an official MLPerf metric and is not verified by the MLCommons Association. Treat the figures as directional until your own model, software version, and pricing test confirms them.
TPU selection should include pod topology and interconnect requirements, not only an individual chip’s advertised memory or bandwidth. Measure scaling efficiency as replicas or model partitions increase; communication and synchronization can change the cost curve.
Microsoft Azure managed compute and token services
Azure AI Foundry Managed Compute lists A100, H100, H200, and MI300 GPU families with managed deployment and automatic scaling. This can reduce the platform work required to provision endpoints, roll versions, and scale replicas, but you still need to measure cold-start behavior, scale-out delay, and the amount of idle capacity held for your latency target.
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Mixed-platform serving with Triton
NVIDIA Triton is documented as a cloud inference server optimized for NVIDIA GPUs. Its backend matrix also documents an AWS Inferentia path through a Python backend that invokes the Neuron SDK. This can be useful when a system needs a common serving layer while routing different models or versions to different accelerators, but test observability, batching behavior, and failure handling for each backend rather than assuming identical semantics.
Documented vendor figures and how to use them
| Option or service | Published figure or capability | How to interpret it |
|---|---|---|
| First-generation AWS Inferentia | Up to 2.3× higher throughput and up to 70% lower cost per inference than comparable EC2 instances | AWS claim for specified comparisons; validate with your workload |
| AWS Inferentia2 | Up to 4× higher throughput and up to 10× lower latency versus Inferentia | AWS claim; not a universal result across models |
| Google Cloud comparison, 2023 | 2–4× inference-performance improvement and more than 2× cost-efficiency improvement | Google’s comparison result; performance per dollar is not MLPerf-verified |
| Azure OpenAI Batch API | 50% discount on Global Standard pricing | Azure pricing-page statement; recheck current model, region, and eligibility |
| NVIDIA TensorRT-LLM | Multi-GPU and multi-node execution, in-flight batching, paged KV caching, FP8, INT8, and INT4 quantization | Documented software capabilities; performance depends on model and engine build |
A reproducible benchmark workflow
- Freeze the test contract. Write down the model revision, tokenizer, precision, context-length distribution, quality test, traffic trace, latency objectives, and acceptable error rate.
- Choose comparable configurations. Select one representative chip or instance configuration per provider. Record region, host shape, accelerator count, driver or runtime version, compiler version, serving server, and model-engine settings.
- Warm the stack. Load weights, compile engines, populate kernels, and reach steady state before timing requests. Record cold-start and scale-out behavior separately instead of mixing it with warm latency.
- Run concurrency sweeps. Test single-request operation and several production-like concurrency levels. Capture p50, p95, and p99 TTFT, ITL, total latency, throughput, error rate, queue time, and accelerator utilization.
- Check quality after optimization. Repeat the quality suite after quantization, compilation, tensor parallelism, or speculative decoding. Reject any configuration that misses the threshold, even if its cost is attractive.
- Convert output to a unit cost. Use measured useful tokens or successful requests as the denominator and current on-demand or committed rates as the numerator. Include host, storage, network, egress, and idle capacity.
- Add non-compute costs. Price observability, support, security controls, data transfer, migration engineering, model conversion, and ongoing maintenance. Separate one-time migration cost from recurring serving cost.
- Stress the assumptions. Vary traffic growth, batch size, context length, replica count, and region. Recalculate when quotas or regional prices differ. Keep a fallback provider if the expected loss from outage or quota failure exceeds the savings from a single-provider design.
Calculate cost per token correctly
For a measured time window, start with the fully loaded serving cost:
fully loaded cost = accelerator cost + host cost + storage + networking + egress + observability + support + allocated engineering cost
Then divide by useful output:
cost per useful output token = fully loaded cost ÷ accepted output tokens
Rank #3
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
For request pricing, use:
cost per successful request = fully loaded cost ÷ requests that met quality and latency targets
Keep prompt and completion tokens separate when a provider prices them differently. For self-hosted accelerators, calculate tokens produced during the measured interval and subtract failed, timed-out, or quality-rejected responses from the useful total. If a reservation lowers the hourly rate but leaves capacity idle, allocate the idle portion to the traffic that required it. Run the calculation for steady-state and burst windows; a configuration that is cheapest at 90% utilization may be expensive when you must hold spare replicas for p99 latency.
Use throughput-per-dollar only after quality and latency gates
Throughput divided by accelerator cost is a useful screening metric, and Google recommends this style of calculation. It is not a substitute for an end-to-end unit cost. A faster device can require more host memory, a larger replica floor, more expensive egress, or additional engineering. Rank candidates only after they pass quality, latency, availability, and governance requirements.
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Memory and parallelism checks for large models
Estimate the complete memory footprint
- Weights: model parameter count multiplied by bytes per parameter, plus format-specific metadata.
- KV cache: grows with context length, active sequences, layers, and the number of key and value channels. Continuous batching can make this the dominant variable.
- Activations and temporary buffers: include workspace requested by kernels and compiler-generated intermediates.
- Headroom: reserve space for allocator fragmentation, runtime services, and traffic spikes. A configuration that uses every byte in a lab is not production-safe.
Test multi-chip behavior
When a model is sharded, benchmark both single-device and multi-device paths. Measure scaling efficiency, synchronization time, interconnect utilization, and tail latency. Memory capacity may improve while tokens per second per dollar worsens if communication becomes the bottleneck. Record the topology and placement rules so another region or instance family does not silently change the result.
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GPU-to-accelerator migration
Export the model and run a compiler coverage report before promising a migration date. For Inferentia, test Neuron’s supported operators, graph partitioning, quantization path, dynamic-shape behavior, and profiling output. For TPUs, verify framework versions, XLA or compiler behavior, input pipelines, and the serving API. Keep a known-good GPU path while the alternative is validated.
Serving features matter as much as kernels
Confirm that the candidate stack supports the batching and cache policy your application needs. In-flight batching and paged KV caching can change utilization dramatically for LLM traffic. Verify streaming, cancellation, request priorities, health checks, metrics, tracing, and model hot-swaps. A benchmark that omits these production controls can overstate usable throughput.
Portability checklist
- Can the model weights and tokenizer move without conversion to a proprietary format?
- Are custom operators isolated behind a replaceable interface?
- Can the same evaluation and load-test harness run on every provider?
- Are precision and quantization settings explicit and reproducible?
- Can traffic fail over without violating data-residency rules?
Operations, governance, and availability
Ask each provider for quota limits, regional capacity, accelerator reservation behavior, and escalation paths. Verify autoscaling triggers and the time required to add capacity. Measure what happens when an accelerator, host, zone, or model replica fails: requests should retry safely, queues should remain bounded, and partial rollouts should be observable.
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Map data flows for prompts, completions, logs, traces, caches, and backups. Global deployment can improve capacity but may conflict with residency requirements; data-zone or regional deployment can satisfy governance while changing price and available capacity. Document encryption, identity boundaries, private networking, retention, and support obligations as part of the technical comparison.
Choosing by workload pattern
Broad model portfolio or frequent model changes
Start with NVIDIA GPUs and a mature serving stack when operator coverage, quantization choices, and portability are the dominant risks. Use the benchmark to decide the GPU generation and replica shape rather than assuming the newest device is cheapest.
Stable model with very high volume
Evaluate Inferentia2 or TPU configurations alongside GPUs. Their economics can be compelling when the model compiles cleanly, traffic is predictable, and utilization remains high. Require a quality-equivalent result and include migration maintenance in the payback calculation.
Rank #4
- Discrete graphics card memory 40 GB
- Memory bandwidth (max) 1555 GB/s
- Graphics processor family NVIDIA
- Graphics processor A100
Variable traffic and limited platform staff
Managed compute with automatic scaling can reduce operational work. Compare scale-out delay, minimum replica requirements, cold-start behavior, and managed-service premiums against the labor required to operate raw instances.
Strict residency or regulated workloads
Filter by eligible regions and deployment types first. Then benchmark only the capacity you can contractually and legally use. A global endpoint or a discounted batch mode is irrelevant if it violates your governance boundary.
Latency-sensitive interactive generation
Prioritize p95 and p99 TTFT and ITL under your real concurrency, then examine memory headroom and queueing. Batch discounts and maximum throughput are secondary if they make streaming responses miss the user-facing objective.
Common comparison mistakes and fixes
Comparing vendor peak numbers
Failure: treating “up to” throughput or cost claims as cross-vendor facts. Fix: reproduce one model, one quality test, one traffic trace, and one accounting method on every candidate.
Using average latency
Failure: reporting a mean that hides queueing and tail failures. Fix: publish p50, p95, and p99 TTFT and ITL at each tested concurrency.
Ignoring context and KV-cache growth
Failure: sizing memory with weights only. Fix: replay long-context and high-concurrency requests, then reserve headroom for production bursts.
Counting raw tokens instead of useful output
Failure: allowing failed, timed-out, or low-quality responses to reduce apparent cost. Fix: divide by accepted tokens or successful requests that meet the service contract.
Leaving engineering out of the business case
Failure: comparing instance rates while omitting compiler work, custom kernels, monitoring, and migration. Fix: price one-time and recurring engineering explicitly and run a sensitivity analysis for traffic growth and provider price changes.
Assuming capacity is interchangeable
Failure: selecting a region or accelerator that cannot be obtained under your quota. Fix: verify regional availability, quota, reservation terms, and fallback capacity before launch.
Decision record to keep after the benchmark
Store the workload contract, software versions, region, instance shape, engine build, raw latency and throughput logs, quality results, utilization, price assumptions, and sensitivity analysis. Record why rejected candidates failed: quality, p99 latency, memory, quota, governance, or total cost. Re-run the suite when the model, compiler, serving server, accelerator generation, pricing, or traffic mix changes. This turns a one-time purchase decision into a repeatable engineering process.
Quick Recap
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