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Evan Conrad
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我观察到CoreWeave的成功秘诀在于签订长期锁定合同,这与传统的CPU云计算模式截然不同。传统的CPU云计算模式是购买商品硬件,然后在其上部署主要由软件组成的服务,从而获得高利润率。而GPU市场则不同,客户对价格极其敏感,因为增加GPU数量会直接转化为收入。因此,最佳的盈利模式是与低信用风险的客户签订长期合同,并最大限度地减少价格敏感性带来的风险。 CoreWeave的财务模式更像银行或房地产公司,而非传统的云服务提供商或软件公司。大型云服务提供商通过转售英伟达GPU难以获得高利润,因为客户对价格非常敏感。DigitalOcean和Together等公司将GPU与软件服务捆绑销售的模式,最终可能导致亏损,因为他们难以创造出足以抵消硬件成本和风险的软件价值。 SF Compute最初是AI实验室,由于无法获得短期GPU租赁服务,被迫转型为GPU市场。我们建立了一个GPU计算市场,提供高流动性,允许用户进行短期和长期租赁,并进行GPU的买卖。我们的GPU利用率接近100%,因为价格会下降到利用率达到100%为止。2023年H100 GPU供过于求的部分原因是供应链问题和一些公司过度订购。我对点对点分布式GPU市场持怀疑态度,认为其效率不如集中式集群。 SF Compute的定价策略,短期租赁价格波动较大,这与市场供需和资源即将过期有关。我们的目标是创建一个GPU现货市场,并在此基础上创建现金结算期货合约,以降低风险并提高市场效率。我们通过运行LINPACK等测试来审核GPU集群,并进行主动和被动测试以确保集群的可靠性。我们的目标是通过创建期货合约来降低风险,而非投机。SF Compute的品牌定位是反炒作,追求平静和务实。我们正在招聘系统工程师和金融系统工程师,以进一步完善我们的市场和服务。

Deep Dive

Chapters
This chapter analyzes CoreWeave's successful business model, which focuses on long-term contracts with low-risk customers. It contrasts this with the traditional CPU cloud model and explains why it's more profitable in the GPU market due to customer price sensitivity. The chapter also discusses the financial implications of this strategy and why hyperscalers may lose money on GPU reselling.
  • CoreWeave's success is attributed to its focus on long-term contracts with low-credit-risk customers.
  • The GPU market differs significantly from the CPU market due to customer price sensitivity and the high cost of hardware.
  • Hyperscalers may lose money on reselling GPUs due to low margins compared to their CPU businesses.

Shownotes Transcript

Evan Conrad, co-founder of SF Compute, joined us to talk about how they started as an AI lab that avoided bankruptcy by selling GPU clusters, why CoreWeave financials look like a real estate business, and how GPUs are turning into a commodities market.

Chapters:

00:00:05 - Introductions

00:00:12 - Introduction of guest Evan Conrad from SF Compute

00:00:12 - CoreWeave Business Model Discussion

00:05:37 - CoreWeave as a Real Estate Business

00:08:59 - Interest Rate Risk and GPU Market Strategy Framework

00:16:33 - Why Together and DigitalOcean will lose money on their clusters

00:20:37 - SF Compute's AI Lab Origins

00:25:49 - Utilization Rates and Benefits of SF Compute Market Model

00:30:00 - H100 GPU Glut, Supply Chain Issues, and Future Demand Forecast

00:34:00 - P2P GPU networks

00:36:50 - Customer stories

00:38:23 - VC-Provided GPU Clusters and Credit Risk Arbitrage

00:41:58 - Market Pricing Dynamics and Preemptible GPU Pricing Model

00:48:00 - Future Plans for Financialization?

00:52:59 - Cluster auditing and quality control

00:58:00 - Futures Contracts for GPUs

01:01:20 - Branding and Aesthetic Choices Behind SF Compute

01:06:30 - Lessons from Previous Startups

01:09:07 - Hiring at SF Compute

Chapters

  • 00:00:00 Introduction and Background
  • 00:00:58 Analysis of GPU Business Models
  • 00:01:53 Challenges with GPU Pricing
  • 00:02:48 Revenue and Scaling with GPUs
  • 00:03:46 Customer Sensitivity to GPU Pricing
  • 00:04:44 Core Weave's Business Strategy
  • 00:05:41 Core Weave's Market Perception
  • 00:06:40 Hyperscalers and GPU Market Dynamics
  • 00:07:37 Financial Strategies for GPU Sales
  • 00:08:35 Interest Rates and GPU Market Risks
  • 00:09:30 Optimal GPU Contract Strategies
  • 00:10:27 Risks in GPU Market Contracts
  • 00:11:25 Price Sensitivity and Market Competition
  • 00:12:21 Market Dynamics and GPU Contracts
  • 00:13:18 Hyperscalers and GPU Market Strategies
  • 00:14:15 Nvidia and Market Competition
  • 00:15:12 Microsoft's Role in GPU Market
  • 00:16:10 Challenges in GPU Market Dynamics
  • 00:17:07 Economic Realities of the GPU Market
  • 00:18:03 Real Estate Model for GPU Clouds
  • 00:18:59 Price Sensitivity and Chip Design
  • 00:19:55 SF Compute's Beginnings and Challenges
  • 00:20:54 Navigating the GPU Market
  • 00:21:54 Pivoting to a GPU Cloud Provider
  • 00:22:53 Building a GPU Market
  • 00:23:52 SF Compute as a GPU Marketplace
  • 00:24:49 Market Liquidity and GPU Pricing
  • 00:25:47 Utilization Rates in GPU Markets
  • 00:26:44 Brokerage and Market Flexibility
  • 00:27:42 H100 Glut and Market Cycles
  • 00:28:40 Supply Chain Challenges and GPU Glut
  • 00:29:35 Future Predictions for the GPU Market
  • 00:30:33 Speculations on Test Time Inference
  • 00:31:29 Market Demand and Test Time Inference
  • 00:32:26 Open Source vs. Closed AI Demand
  • 00:33:24 Future of Inference Demand
  • 00:34:24 Peer-to-Peer GPU Markets
  • 00:35:17 Decentralized GPU Market Skepticism
  • 00:36:15 Redesigning Architectures for New Markets
  • 00:37:14 Supporting Grad Students and Startups
  • 00:38:11 Successful Startups Using SF Compute
  • 00:39:11 VCs and GPU Infrastructure
  • 00:40:09 VCs as GPU Credit Transformators
  • 00:41:06 Market Timing and GPU Infrastructure
  • 00:42:02 Understanding GPU Pricing Dynamics
  • 00:43:01 Market Pricing and Preemptible Compute
  • 00:43:55 Price Volatility and Market Optimization
  • 00:44:52 Customizing Compute Contracts
  • 00:45:50 Creating Flexible Compute Guarantees
  • 00:46:45 Financialization of GPU Markets
  • 00:47:44 Building a Spot Market for GPUs
  • 00:48:40 Auditing and Standardizing Clusters
  • 00:49:40 Ensuring Cluster Reliability
  • 00:50:36 Active Monitoring and Refunds
  • 00:51:33 Automating Customer Refunds
  • 00:52:33 Challenges in Cluster Maintenance
  • 00:53:29 Remote Cluster Management
  • 00:54:29 Standardizing Compute Contracts
  • 00:55:28 Unified Infrastructure for Clusters
  • 00:56:24 Creating a Commodity Market for GPUs
  • 00:57:22 Futures Market and Risk Management
  • 00:58:18 Reducing Risk with GPU Futures
  • 00:59:14 Stabilizing the GPU Market
  • 01:00:10 SF Compute's Anti-Hype Approach
  • 01:01:07 Calm Branding and Expectations
  • 01:02:07 Promoting San Francisco's Beauty
  • 01:03:03 Design Philosophy at SF Compute
  • 01:04:02 Artistic Influence on Branding
  • 01:05:00 Past Projects and Burnout
  • 01:05:59 Challenges in Building an Email Client
  • 01:06:57 Persistence and Iteration in Startups
  • 01:07:57 Email Market Challenges
  • 01:08:53 SF Compute Job Opportunities
  • 01:09:53 Hiring for Systems Engineering
  • 01:10:50 Financial Systems Engineering Role
  • 01:11:50 Conclusion and Farewell