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There is no evidence here to name one universally best GPU cloud for LLM inference: the right choice depends on whether you need an always-on GPU, an API endpoint for intermittent traffic, or a multi-GPU cluster. The five providers considered here—Runpod, Vast.ai, CoreWeave, Lambda, and Modal—do not have equally verifiable pricing or configuration details in the available published information. The clearest dated price point is CoreWeave’s North America rate for an eight-GPU HGX H100 cluster, which is not a single-GPU price.
What this comparison can—and cannot—tell you
This is a comparison of published service models and a small set of provider-listed pricing facts, not a hands-on benchmark. The available evidence does not establish a common test of throughput, latency, cold starts, uptime, or reliability across these providers. It therefore cannot support a performance ranking or a claim that one provider is fastest.
Pricing and inventory can change quickly, particularly for marketplace and spot capacity. The prices below are dated to the provider information available on October 8, 2026; check current regional availability and billing terms before committing.
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| Provider | Documented deployment options | What is established about pricing |
|---|---|---|
| Runpod | Pods for dedicated GPU instances; Serverless for API inference; Clusters for multi-node jobs. | Its pricing page describes workload-specific pricing and was updated September 27, 2026. No exact rate is included here. |
| Vast.ai | GPU Cloud for full control; Serverless inference endpoints; Clusters for multi-node training. Vast.ai says its Serverless endpoints autoscale to zero and charge for compute time. | Marketplace prices respond to supply and demand. Offers can be filtered by GPU, VRAM, price, and availability; no fixed rate is included here. |
| CoreWeave | Public listings include GPU configurations and distinguish on-demand from spot pricing. | Its North America HGX H100 listing gives an eight-GPU cluster rate; see the dated price breakdown below. |
| Lambda | The available official-page information does not establish specific deployment details for this comparison. | The available official pricing-page information does not establish a stable exact rate. |
| Modal | The available official-page information does not establish specific deployment details for this comparison. | The available official pricing-page information does not establish a stable exact rate. |
The Lambda and Modal qualifications describe what can be established from the information available for this comparison, not a claim that either provider lacks a particular product. Confirm current instance details, GPU configuration, and billing basis directly with each provider.
#1 Best Overall
- System Compatibility Note: This 2-slot card measures 271 x 112 x 39 mm and requires a single 12V-2x6-pin power connector. Please verify chassis and PSU compatibility before purchase.
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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.
CoreWeave’s H100 figure is a cluster price
CoreWeave’s North America pricing page, accessed October 8, 2026, lists an HGX H100 configuration with eight GPUs and 80 GB of VRAM per GPU. Its listed rate is $49.24 per hour on demand or $19.71 per hour for spot capacity. Both figures apply to the eight-GPU cluster, not to one GPU. Spot capacity is a separate pricing category and should not be treated as equivalent to on-demand capacity.
CoreWeave’s page separates North America and Europe, and some newer GPU configurations are marked “contact sales.” A public listing therefore does not necessarily mean that every configuration is available for self-serve deployment. Confirm the region and configuration you need before comparing its rate with another provider’s price.
Rank #2
- PLEASE NOTE: Exporting an NVIDIA RTX Pro 6000 GPU outside the US requires strict adherence to the U.S. Export Administration Regulations (EAR) and issuance of an export license from the Bureau of Industry and Security (BIS). Compliance and Know Your Customer (KYC) screening may be required as a condition of order acceptance. [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.
- [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.
- [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.
Choose the deployment model around your traffic
Intermittent API inference
A serverless endpoint can fit workloads that arrive in bursts rather than running continuously. Vast.ai describes its Serverless endpoints as scaling to zero and billing for compute time. That description does not, by itself, establish a cold-start time, a particular latency, or a cost for a specific model; test those against your traffic pattern.
Steady, directly managed inference
A dedicated instance or GPU Pod gives you a provisioned machine to configure and manage. Runpod identifies Pods as dedicated GPU instances, while Vast.ai describes GPU Cloud as offering full control. Compare the total billed time—including idle periods—with an endpoint that charges for compute time, and account for your own deployment and operations work.
Rank #3
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- 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.
Multi-GPU or multi-node serving
For workloads that need several GPUs or nodes, compare cluster configurations rather than single-GPU rates. Runpod identifies Clusters as a multi-node option; CoreWeave’s quoted H100 configuration is already an eight-GPU cluster. GPU count, memory per GPU, and the cluster’s full hourly price all matter when assessing whether the configuration fits your model.
Compare the bill, not just the GPU-hour headline
A useful estimate starts with the price for the exact configuration and billing basis, then adds the other charges that apply to your deployment. A secondary comparison published by Runpod notes that billing granularity, egress, storage units, and minimum charges can change the billed compute cost. Treat those as items to verify in the applicable provider terms rather than assuming the headline hourly rate is the total.
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
- Match the hardware: Check GPU model, GPU count, VRAM per GPU, and whether the quoted price covers one GPU, a node, or a cluster.
- Match the billing type: Keep on-demand, spot, reserved, marketplace, and serverless prices distinct. Record the billing increment and any minimum charge.
- Include non-compute charges: Check storage units and rates, data-transfer or egress charges, and whether stopped or idle resources continue to incur charges.
- Record the listing context: For a marketplace offer, note the date, region, GPU, VRAM, host or instance configuration, availability, and price. Vast.ai says both offer prices and availability respond to marketplace supply and demand.
- Measure your workload: Use the same model, quantization, prompt and context lengths, concurrency, serving stack, region, and warm or cold conditions when comparing providers. The published figures above do not provide a shared performance test.
A practical way to narrow the choice
- Define the traffic shape. Decide whether requests are intermittent, sustained, or large enough to require multiple GPUs or nodes.
- Set the hardware and region requirement. Specify the GPU model or class, VRAM, GPU count, and data region you need.
- Shortlist matching deployment models. Consider serverless for bursty endpoint workloads, provisioned instances for direct control, and clusters when a single GPU or node is insufficient.
- Calculate the complete bill. Apply the provider’s billing increment and minimums, then add storage, egress, and any idle time that applies to your design.
- Run a workload-specific trial. Measure throughput, latency, startup behavior, and availability under your own serving conditions before choosing on performance grounds.
Why the available figures do not settle the winner
The evidence is uneven: it establishes several deployment models, a dynamic marketplace approach, and one dated cluster price, but it does not provide equivalent live prices and configurations for all five providers. Nor does it establish comparable performance or reliability measurements. That makes a universal ranking—and a like-for-like cost winner—unsupported. A decision should turn on a verified configuration and a workload-specific cost and performance check.
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Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
