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Estimate an Azure GPU baseline from the region, VM size, billable hours, and rate that apply to your account, then add the supporting resources the workload consumes. You cannot calculate a reliable public Maia customer price from Microsoft’s published material cited here: it describes Maia deployments and development tools, but does not establish a generally available customer VM offer or per-hour rate. Compare the platforms by the cost of delivering the same workload—not by accelerator-hour price alone.
What should you compare?
First define one workload and one service target for both platforms. A useful comparison holds constant the model and version, quality target, precision or quantization, input and output lengths, batch size, concurrency, and required throughput or latency. Specify whether the job is training, fine-tuning, batch inference, or online inference; those workloads have different runtime and supporting-resource needs.
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Use a representative pilot to estimate runtime and utilization where possible. Record setup and idle time if it is billed, along with retries, scaling behavior, host resources, and any material engineering work needed to run the workload. Without equivalent conditions and output targets, a price comparison can be misleading.
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- Choose the VM and region. Select an Azure GPU VM family that fits the workload, and record its accelerator count and memory. Microsoft’s Azure Machine Learning pricing page lists examples including ND40rs v2 with eight V100 GPUs and ND96asr A100 v4 with eight A100 GPUs.
- Get the applicable rate. Check the Azure pricing calculator using the customer’s account and intended region, or obtain an offer-specific quote. The displayed price is not automatically the price for your agreement, currency, or purchase date.
- Estimate billable compute. Multiply the applicable hourly VM rate by the number of billable VM hours and instances. Use observed provisioned time, including billed setup or idle time, rather than assuming only productive accelerator time is charged.
- Add consumed services. Include relevant storage, data transfer, container or registry, monitoring, orchestration, and other Azure services. Microsoft says Azure Machine Learning itself has no additional charge in the described pricing context; separate Azure services used can still add charges.
- Apply only eligible discounts or commitments. Model pay-as-you-go, savings-plan, or reservation pricing only if the workload schedule and commitment meet the applicable terms. Microsoft notes that estimates can vary by agreement, purchase date, and currency; consult its Azure pricing context and the Machine Learning pricing page for the relevant offer.
Microsoft’s pricing page displayed monthly examples of $19,853.810 for ND96asr A100 v4 and $16,083.36 for ND40rs v2 when accessed in 2026. These are page-specific listings, not universal quotes; the retrieved listings do not establish the geography or agreement needed to treat either amount as your likely bill. Confirm current region-, currency-, and offer-specific pricing before using a number in a budget.
#1 Best Overall
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- Professional Intel Arc Pro B70 GPU: Built on the Intel Xe2-HPG architecture, it features 32 Xe cores and 256 XMX engines, designed to accelerate AI, rendering, and complex visualization workloads.
- Massive 32GB GDDR6 VRAM: Equipped with 32GB of high-speed GDDR6 memory on a 256-bit bus, running at 19 Gbps, which allows for handling large AI models and complex datasets locally.
- High-Performance Engine Clock: Delivers an engine clock of 2540 MHz, providing the compute power needed for demanding professional applications and AI inference.
What can you price for Maia?
Do not assign Maia an assumed hourly rate. In an announcement dated October 12, 2026, Microsoft said Maia 200 was serving Microsoft production workloads, including Copilot, and described Maia 300 as planned for 2027. That deployment status does not establish customer eligibility, a public Maia VM SKU, or a customer price. Microsoft’s statements about Maia performance are vendor claims, not independent, directly comparable customer benchmarks. See Microsoft’s announcement.
Microsoft’s Maia 100 technical article describes a purpose-built accelerator system and software stack. Its Maia 200 announcement describes an SDK with a simulator and cost calculator. Those tools can support workload exploration, but they do not supply a generally available customer rate in the cited material.
Rank #2
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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.
Ask Microsoft to confirm, for your intended workload, region, and account:
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- The applicable billing unit and rate, including any minimum commitment.
- Any platform, host, storage, networking, or other charges attached to the offer.
- The commitment and discount terms, if any.
Until those details are confirmed, leave Maia’s price as an unknown variable. An illustrative scenario is useful only when it labels the assumed rate clearly; it is not a quote or a finding that Maia is cheaper.
Rank #3
- System Compatibility Note: 2-slot card, 271x112x39mm, single 8-pin power, 200W TDP. Verify chassis clearance and PSU capacity before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- 24GB GDDR6 on 192-Bit Bus: Massive 24GB memory with 456 GB/s bandwidth – ideal for LLMs, AI inference, 3D rendering, and generative design.
- Intel Xe2-HPG Architecture: Built on Intel's next-gen architecture with 20 Xe cores and 160 XMX engines for AI acceleration (197 INT8 TOPS).
- PCIe 5.0 Support: PCI Express 5.0 x16 interface for maximum bandwidth with the latest workstation platforms.
How to compare cost per useful output
For each platform, calculate:
Workload cost = compute rate × billable accelerator or VM time + billed supporting resources + data movement and service charges.
Then divide by successfully delivered workload units: cost per workload unit = workload cost ÷ successful workload units delivered. Choose a unit that matches the job and service objective. For inference, cost per million successfully completed output tokens or cost per request can work if latency and quality requirements are also met. For training, compare the full cost of a completed run at the target quality.
Rank #4
- 【High-Performance APU】The MS-S1 MAX features an AMD Ryzen AI Max+ 395 APU, integrating a Zen 5 architecture CPU (up to 5.1GHz, 16C/32T, 64M L3 Cache), an RDNA 3.5 GPU, and an NPU (50 TOPS). The total system output is 126 TOPS. It provides powerful parallel computing capabilities for demanding AI workflows. It is ideal for running local LLMs, multimodal models, and computationally intensive tasks
- 【128GB UMA Memory】Equipped with up to 128GB of LPDDR5x-8000MT/s unified memory, it enables the CPU and GPU to access a shared, high-bandwidth memory pool with extremely low latency. Ideal for large-scale AI inference, 3D workloads, and complex timelines in video editing. It eliminates traditional VRAM bottlenecks, ensuring smoother data transfer during high-intensity computations. The UMA design maximizes performance stability under high loads
- 【Flexible Expansion】The MS-S1 MAX features USB4 V2 (up to 80Gbps), dual 10GbE LAN, HDMI 2.1 (up to 8K60), a full-length PCIe x16 expansion slot, and dual M.2 slots supporting up to 16TB RAID 0/1. Wi-Fi 7 provides stronger signal coverage and a more stable wireless experience. The slide-out design facilitates upgrades and maintenance. It easily adapts to personal, studio, or rack-mount enterprise environments
- 【High-Efficiency Cooling System】Utilizing an aerospace-grade aluminum alloy chassis, copper base plate, six heat pipes, dual turbine fans, and advanced PCM thermal conductive material, it maintains stable cooling performance even under continuous load. This system supports 130W continuous power and 160W peak power operation, with a built-in 320W power supply. It boasts multiple global certifications including CCC, FCC, UL, CE, and UKCA, ensuring stable and reliable operation in various environments
- 【Cluster Design】Two MS-S1 MAX units can be configured as a dual-unit cluster to run a large 235B Q4 model locally, achieving an output speed of 10.87 tok/s. Supporting 2U rack deployment, multiple MS-S1 MAX units can be cascaded into a distributed cluster to create a high-efficiency AI computing center. A cluster of four MS-S1 MAX units successfully ran a DeepSeek-R1 671B Q4 large model. A reserved cluster power-on interface allows for unified start-up and shutdown
Use the same workload unit on both sides. If public performance figures do not use comparable models, settings, and test conditions, treat them as background—not as a measured cost-per-output result. A fair conclusion requires both an applicable Maia rate and equivalent workload measurements.
Quick Recap
What the available numbers do—and do not—show
| Option | What is established | What is not established by the cited material |
|---|---|---|
| Azure GPU VM examples | Microsoft’s pricing page lists ND40rs v2 with eight V100 GPUs and ND96asr A100 v4 with eight A100 GPUs. Retrieved monthly listings in 2026 were $16,083.36 and $19,853.810, respectively. | A universal rate or the reader’s region-, currency-, agreement-, and offer-specific bill. |
| Maia | Microsoft said Maia 200 was serving Microsoft production workloads in its October 12, 2026 announcement; Microsoft also describes Maia development tools. | A generally available customer VM SKU, customer eligibility, a public per-hour rate, or an equivalent customer workload-cost result. |
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