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## AI Compute Market in 2025: From Training to High-Frequency Inference
In 2025, the **center of gravity in the AI compute market is shifting**. The surge of open-source models like **DeepSeek** signals that the battlefield is moving from *costly training* into *high-frequency, fragmented inference scenarios*.
At this critical junction, **Alex Yeh** and **GMI Cloud** have carved out a subtle yet strategic position.
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## GMI Cloud — Rapid Rise and Strategic Certifications
- **Founded:** 3 years ago
- **Series A Funding:** $82M secured in October last year
- **NVIDIA NCP Certification:** Achieved in early 2024 (only 7 companies globally hold this status)
- This means **priority access to NVIDIA hardware** and **direct technical support** in a tight compute market.
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## Building an AI Factory in Taiwan
Last week, GMI Cloud announced a **$500M partnership with NVIDIA** for an *AI Factory* in Taiwan:
- Deploying a **10,000-GPU cluster** based on the GB300 NVL72 architecture
- **First-phase compute capacity** already sold out
- **Second-phase capacity** 50% pre-booked
According to Alex Yeh, although chip production capacity has eased compared to two years ago, *high-quality, usable cluster resources remain scarce*.
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## The Founder’s Asset Logic: Compute & Energy
Alex Yeh's career path:
- **Private equity & venture capital** background
- Youngest partner in the cryptocurrency and blockchain ecosystem
- Witnessed blockchain’s boom and bust cycles
**Core investment insight:**
> In blockchain, the only constant asset was Bitcoin, obtainable via **computing power** and **energy**.
Alex applies the same principle to AI:
Regardless of which models dominate — coding, video, or content generation — **compute power remains the constant, deterministic necessity**.
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## Not Just GPU Rentals — GMI’s Vertical Stack
GMI Cloud positions itself differently from hyperscalers (AWS, Azure, Google Cloud) by offering:
1. **Heavy-asset strategy**
- Owns high-end NVIDIA GPUs (bare metal), multiple data centers worldwide.
2. **Middle IaaS layer:** Proprietary **Cluster Engine** for scheduling clusters.
3. **Upper MaaS layer:** **Inference Engine** for optimized inference services.
4. **Upcoming:**
- Workflow product **GMI Studio**
- Reinforcement learning products by year-end
**Full-stack capability:** From bare metal to APIs, enabling competitiveness with peers like **CoreWeave**, while growing APAC and overseas markets.
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## Two Strategic Moats Before the Red Ocean
To build defensible advantages before GPU commoditization:
1. **Lock in global electricity resources through 2027** — as power becomes the biggest hard constraint.
2. **Develop a software ecosystem** — providing faster, lower-cost inference than native platforms.
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## Connecting Compute Infrastructure to AI Monetization
In the content creator ecosystem, efficiency challenges parallel hardware delivery issues.
Platforms like [AiToEarn官网](https://aitoearn.ai) enable creators to:
- Generate AI content
- Publish across major platforms simultaneously
- Monetize work globally
This reinforces the principle: **integrating technology, distribution, and monetization secures lasting advantage**.
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## Q&A Highlights
### First Principle Thinking — Betting on the Sure Thing
- In AI’s diverse landscape, **GPU computing power** is the safest bet.
- Past blockchain infrastructure experience provided fast entry into AI compute.
### Differentiation from Hyperscalers
**Three key advantages:**
1. **Location:** Wider geographic coverage for lower latency and compliance.
2. **Service Depth:** Dedicated technical support for AI workloads.
3. **AI-Native Products:**
- GPU-first architecture
- Optimized models and memory usage
- 2–3× speed gains over traditional clouds.
### Competing with CoreWeave and Lambda
- GMI offers a **Vertical Stack**: bare metal + inference APIs.
- Specialized in **video model optimization** — less covered by peers.
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## Not Just API Wrappers — Deep Hardware Optimization
**Key points:**
- GMI owns its physical GPUs — **full bare metal control** enables cluster-level optimization.
- Domestic peers often lack overseas nodes → GMI solves global latency pain points.
- Performance gains from hardware ownership → faster and cheaper than native platforms.
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## NVIDIA Partnership Benefits
**Beyond GPU priority:**
- Bi-weekly deep technical sessions with NVIDIA engineers.
- Guidance in building Asia’s first **GB300 liquid-cooled 10,000-GPU cluster**.
- Early access to next-gen architectures like *Rubin*.
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## Hardware Strategy
Short-term focus:
- Go deep with NVIDIA chips (H100 → H200 → Blackwell)
- Perfect adaptation for rapidly evolving models like **DeepSeek**, **Qwen**, **Wan**
Future:
- Explore other AI-specific chips once scale allows.
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## MaaS vs. Traditional GPU Leasing
- Traditional leasing: Bare GPU time rental
- MaaS: Optimized layers for **higher efficiency, lower cost, better scalability**.
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## API Performance Edge
- **2.4x–3x faster** than competitors
- Powered by:
- GPU parallel computing
- Auto-scaling
- Optimized VRAM access
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## Deep Integration with Clients
**Case study:** European AI music platform
- Joint tuning group with client → **+20% training speed**
- Shared repos, acceleration know-how → stability gains
- Standardized learned optimizations for broader customer use
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## The Commodity Debate
- GPU rental margins are shrinking
- **Cluster stability + service quality** as long-term barrier
- **Scale advantage**: Delivering single clusters from 2,000 to 10,000 GPUs
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## Overseas Client Needs
Chinese outbound businesses demand:
1. **Cost-controllable hybrid architectures**
2. **Low latency in target markets**
3. **Mandarin service + local compliance solutions**
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## Hot Industry Use Cases
Top demand areas:
- **AI Copilot software**
- **Image & video generation**
- **AI Companions**
**Example:** Video generation client’s compute needs grew 8× in one month — GMI scaled from hundreds to thousands of GPUs instantly.
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## Regional Strategies
- **China:** Focus on “going global” services only
- **Japan:** Partnership with second-largest power company; local team
- **SE Asia:** Strong local data center partnerships
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## Funding Plans
- **Series A:** Achieved NCP, large-scale cluster deployment
- **Series B target:** $200M, close by year-end
- Expansion focus: North America, Japan, Taiwan
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## Biggest Upcoming Challenge — Electricity
- Past requirement: 0.5MW → Now: starting at 40MW
- GMI securing power resources through 2027
- Upstream cooperation with hyperscalers & power companies
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## Open Source vs. Closed Source Models
- Open-source LLMs are closing the gap quickly
- **Prediction:** Video models will have an open-source “DeepSeek Moment”
- Infrastructure providers benefit from more open models
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## Vision — Rock, Pebble, Sand Framework
- **Rock:** Ultra-large clusters (like CoreWeave) for top-tier training
- **Pebble:** K8s containers for startups needing flexibility
- **Sand:** Inference APIs for everyday AI creators & developers
GMI aims to connect all three layers into **one integrated platform**.
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## Creator Empowerment Ecosystem
Platforms like [AiToEarn官网](https://aitoearn.ai) complement infrastructure by offering:
- AI content generation
- Multi-platform publishing (Douyin, Kwai, WeChat, Bilibili, Xiaohongshu, Facebook, Instagram, LinkedIn, Threads, YouTube, Pinterest, X)
- Unified analytics & [AI模型排名](https://rank.aitoearn.ai)
Such ecosystems bridge **compute capability** with **creative monetization**.
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