{"id":610307,"date":"2026-08-26T09:07:08","date_gmt":"2026-08-26T09:07:08","guid":{"rendered":"https:\/\/www.olympiajournal.com\/news\/story\/610307\/gmi-cloud-runs-enterprise-llm-inference-and-model-training-on-one-platform-across-the-us-apac-and-europe.html"},"modified":"2026-08-26T09:07:08","modified_gmt":"2026-08-26T09:07:08","slug":"gmi-cloud-runs-enterprise-llm-inference-and-model-training-on-one-platform-across-the-us-apac-and-europe","status":"publish","type":"post","link":"https:\/\/www.olympiajournal.com\/news\/story\/610307\/gmi-cloud-runs-enterprise-llm-inference-and-model-training-on-one-platform-across-the-us-apac-and-europe.html","title":{"rendered":"GMI Cloud Runs Enterprise LLM Inference and Model Training on One Platform Across the U.S., APAC and Europe"},"content":{"rendered":"
\"GMI<\/a><\/div>\n

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Figure 1. GMI Cloud positions compute, inference, and agent runtime on a single platform.<\/em><\/p>\n

MOUNTAIN VIEW, Calif. – August 26, 2026 –<\/strong> GMI Cloud<\/a>, a leading AI-native cloud provider delivering high-performance GPU infrastructure and inference services, operates model training and production inference as one platform for enterprise AI teams across the United States, Asia-Pacific, and Europe. Teams rent NVIDIA GPU capacity by the hour or reserve it for long training runs, then serve the resulting models from dedicated inference endpoints on the same platform rather than moving to a second provider. The platform processed approximately 2.5 trillion tokens per week as of July 2026.<\/p>\n

One platform for the training run and the endpoint it feeds<\/strong><\/p>\n

Training and serving usually live in different places, which is how teams end up rebuilding their stack at the handoff. GMI Cloud sells both from the same platform, with consistent pricing across regions under unified billing.<\/p>\n

For training, post-training, and fine-tuning, capacity comes in three shapes. Bare Metal GPU gives full root access and hardware-level control, and GMI Cloud lists large-scale model training and fine-tuning as its primary fit. Managed GPU Cluster, currently in early access, covers fully managed multi-node clusters for distributed training with centralized lifecycle management, including clusters a team already owns. Container Service provides Kubernetes-based GPU environments for teams that want orchestration handled. The platform runs RDMA-ready networking with isolated VPC networking, and GPU capacity is available on demand or through reserved capacity plans.<\/p>\n

For serving, Prime Inference<\/strong> provides dedicated single-tenant GPUs with runtimes tuned per model<\/strong>, using pre-optimized engines including vLLM, TensorRT-LLM, and SGLang. A model that finished training on reserved H200 capacity can be served from a Prime Inference endpoint on the same platform, including custom and fine-tuned weights rather than open-source checkpoints only.<\/p>\n

“Teams don’t experience training and inference as two separate purchases, so it makes no sense to sell them that way,” said Alex Yeh, CEO of GMI Cloud. “Our aim is that reserving capacity, training, and serving feel like one continuous path. For a platform team, that continuity is worth more than any single benchmark number.”<\/p>\n

GMI Cloud is an NVIDIA Cloud Platform Partner and an NVIDIA Reference Architecture Provider.<\/p>\n

Serving inference without operating the GPUs<\/strong><\/p>\n

Running your own inference tier means owning capacity planning, engine tuning, and the cold start problem. Prime Inference takes on the tuning and the warm capacity.<\/p>\n

Endpoints are warm by default, so there’s no cold start penalty on the first request after a quiet period<\/strong>. That matters for latency-sensitive traffic, where a cold path can cost more than the entire rest of the request budget. Throughput reaches up to 500K tokens per minute per GPU, and runtimes are tuned per model rather than applied as one generic serving configuration.<\/p>\n

Teams that begin on serverless public APIs and later need production guarantees move to dedicated endpoints on the same platform, which makes the step a capacity decision rather than a change of vendor.<\/p>\n

Autoscaling and the uptime commitment behind it<\/strong><\/p>\n

Prime Inference sets its uptime commitment by GPU series and deployment configuration, and most production deployments fall in the 99.9% range.<\/strong> Committed SLAs vary by contract. Reserved capacity carries burstable headroom above the reservation, which absorbs spikes without provisioning peak capacity year-round, and quiet hours bill under what GMI Cloud calls pay-as-you-rest.<\/p>\n

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Figure 2. Reserved capacity tracks demand, and burst absorbs the peak.<\/em><\/p>\n

Single-tenant isolation is part of the reliability story rather than a separate premium tier. GPUs are reserved only for one workload, which is how GMI Cloud describes avoiding noisy neighbors and contention under load.<\/p>\n

Regional coverage matters to enterprises with data residency obligations. Prime Inference runs in Asia-Pacific from Tokyo, Singapore, and Taiwan, in North America from U.S. West, East, Central, and South, and in Europe through partner data centers built for residency requirements. Pricing is consistent across regions under unified billing, so a multi-region deployment doesn’t require reconciling separate rate cards.<\/p>\n

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Figure 3. Regional coverage across North America, Europe, and Asia-Pacific.<\/em><\/p>\n

What it costs<\/strong><\/p>\n

GMI Cloud publishes hourly GPU rates rather than quoting them per deal.<\/p>\n\n\n\n\n\n\n\n\n
\n

NVIDIA GPU<\/strong><\/p>\n<\/td>\n

\n

Starting rate<\/strong><\/p>\n<\/td>\n

\n

Availability<\/strong><\/p>\n<\/td>\n<\/tr>\n

\n

H100<\/p>\n<\/td>\n

\n

from $2.00 per GPU-hour<\/p>\n<\/td>\n

\n

Available now<\/p>\n<\/td>\n<\/tr>\n

\n

H200<\/p>\n<\/td>\n

\n

from $2.60 per GPU-hour<\/p>\n<\/td>\n

\n

Available now<\/p>\n<\/td>\n<\/tr>\n

\n

B200<\/p>\n<\/td>\n

\n

from $4.00 per GPU-hour<\/p>\n<\/td>\n

\n

Limited availability<\/p>\n<\/td>\n<\/tr>\n

\n

GB200 NVL72<\/p>\n<\/td>\n

\n

from $8.00 per GPU-hour<\/p>\n<\/td>\n

\n

Available now<\/p>\n<\/td>\n<\/tr>\n

\n

GB300 NVL72<\/p>\n<\/td>\n

\n

Pre-order<\/p>\n<\/td>\n

\n

Pre-order<\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

Those are the on-demand starting rates a team can size a training run against before talking to anyone, and long-term reserved commitments reduce the per-unit cost below them.<\/p>\n

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Figure 4. Published per-GPU-hour rates, with availability labels per GPU family.<\/em><\/p>\n

On the serving side, GMI Cloud publishes worked per-token examples instead of a rate card alone. At 8K input and 1K output tokens in FP4, measured at full GPU utilization with idle time excluded, one million output tokens costs $0.40 on DeepSeek V4 Pro running on B200, and $0.20 on GLM-5.1 running on GB200 NVL72.<\/p>\n

Those figures come in 56% and 80% below the same models served on the platform’s serverless tier, and dedicated capacity overtakes serverless on cost somewhere in the 35% to 45% sustained utilization range<\/strong>, with the exact crossover depending on traffic shape. At full utilization, dedicated endpoints deliver up to 5.6 times more tokens per dollar than serverless on DeepSeek V4 Pro.<\/p>\n

Teams building on the platform<\/strong><\/p>\n

Reflection AI<\/strong> uses GMI Cloud for large-scale training with 24\/7 access to global GPU capacity and reports accelerated training speed.<\/p>\n

Mirelo.ai <\/strong>reports 22% faster iteration cycles and 15% lower long-term costs across research, training, and generative media workloads.<\/p>\n

LegalSign.ai <\/strong>runs enterprise training and inference on the platform and reports 20% savings on total compute spend alongside a 15% increase in inference accuracy.<\/p>\n

Higgsfield<\/strong> serves real-time generative media inference and reports a 65% reduction in inference latency.<\/p>\n

GMI Cloud is also a top-three token provider on OpenRouter as of July 2026, serving teams that want U.S. and APAC token endpoints.<\/p>\n

Where the capacity is going<\/strong><\/p>\n

GMI Cloud is expanding its data center footprint from the United States into Taiwan and Thailand, with further build-out underway across Asia-Pacific alongside partners including Macnica, Compal, and Taiwan Mobile. Teams can review current rates and regional availability at gmicloud.ai.<\/p>\n

About GMI Cloud<\/strong><\/p>\n

GMI Cloud is an AI-native cloud infrastructure company powering the next generation of AI applications. The company provides high-performance GPU infrastructure, Model-as-a-Service, dedicated endpoints, and AI workload deployment solutions for developers and enterprises building production AI systems. GMI Cloud helps teams move from experimentation to production with scalable compute, flexible infrastructure, and an ecosystem built for modern AI builders.<\/p>\n

For more information, visit gmicloud.ai<\/a>.<\/p>\n

Media Contact<\/span>
Company Name:<\/strong> GMI Cloud Inc.<\/a>
Contact Person:<\/strong> Yujie Du
Email:<\/strong> Send Email<\/a>
Country:<\/strong> United States
Website:<\/strong> https:\/\/www.gmicloud.ai\/en<\/a><\/p>\n

\"\"<\/p>\n","protected":false},"excerpt":{"rendered":"

Figure 1. GMI Cloud positions compute, inference, and agent runtime on a single platform. MOUNTAIN VIEW, Calif. – August 26, 2026 – GMI Cloud, a leading AI-native cloud provider delivering high-performance<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"_links":{"self":[{"href":"https:\/\/www.olympiajournal.com\/news\/wp-json\/wp\/v2\/posts\/610307"}],"collection":[{"href":"https:\/\/www.olympiajournal.com\/news\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.olympiajournal.com\/news\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.olympiajournal.com\/news\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.olympiajournal.com\/news\/wp-json\/wp\/v2\/comments?post=610307"}],"version-history":[{"count":0,"href":"https:\/\/www.olympiajournal.com\/news\/wp-json\/wp\/v2\/posts\/610307\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.olympiajournal.com\/news\/wp-json\/wp\/v2\/media?parent=610307"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.olympiajournal.com\/news\/wp-json\/wp\/v2\/categories?post=610307"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.olympiajournal.com\/news\/wp-json\/wp\/v2\/tags?post=610307"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}