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Project Overview
Inference is adistributed GPU cluster for LLM inference built on Solana. Inference.net is a global network of data centers serving fast, scalable, pay-per-token APIs for models like DeepSeek V3 and Llama 3.3.
Morgan Stanley: CoreWeave Adds Record 500MW Capacity in Single Quarter, High Debt and Customer Concentration Weigh on Valuation
According to TechFlow Research, Morgan Stanley's August Q2 earnings report indicated that CoreWeave added 500MW of net active power in a single quarter, exceeding any quarter in history, more than three times year-over-year. Management reaffirmed the target of reaching at least 8GW before 2030. FY26 revenue guidance midpoint was raised 2% to $12.4 billion to $13.2 billion, ARR midpoint was raised 3% to $18.5 billion to $19.5 billion. The company raised full-year capital expenditure guidance midpoint by 12% to $35.5 billion to $39.0 billion, Q3 capital expenditure guidance is $11.5 billion to $13.5 billion, higher than the market expectation of $10 billion. Managed Inference Platform (managed inference platform) ARR grew from $1 million to over $100 million, expected to reach at least $250 million by year-end. The research report judges that Q2 adjusted operating margin was about 8%, higher than expected, but Q3 margin guidance of 5.8% to 7.2% is lower than market expectations, Q4 margin needs to increase significantly to achieve full-year guidance. Morgan Stanley expects CoreWeave FY27 operating margin to be 15.9%, FY28 to be 22.4%, free cash flow to remain negative until 2028, and debt is expected to increase to approximately $38 billion by the end of 2026. Morgan Stanley maintains Equal-weight (in line with the market) rating and $99 price target.
AI Reasoning Startup General Compute Secures $400 Million Loan Backed by ASIC Chips for Inference
: AI reasoning cloud startup General Compute has obtained a $400 million loan from Upper90. This deal is the world’s first financing project to use dedicated inference chips as collateral. The company has built a proprietary AI reasoning cloud platform based on SambaNova’s self-developed ASIC chips, primarily targeting Agent-type AI computing workloads. Compared to traditional GPU clouds, it offers faster token processing speeds and lower operational latency. The hardware requires no water cooling and can be directly deployed in traditional data centers and idle cryptocurrency mining facilities.
DeepSeek Self-Develops AI Inference Chip, Plans to Break Dependence on NVIDIA and Huawei
According to Reuters, Chinese AI startup DeepSeek is developing its own AI chips, three informed sources revealed. The chip is designed specifically for inference scenarios, rather than for model training. The project was launched approximately one year ago and remains in the early stages. The company has engaged with chip design, wafer foundry, and storage enterprises, and has quietly increased recruitment of chip design engineers without publicly posting job listings. If successfully developed, DeepSeek will reduce its reliance on Nvidia and Huawei Ascend chips, following the trend of global AI giants such as OpenAI and Anthropic developing their own hardware. Affected by U.S. export controls, DeepSeek previously shifted from Nvidia H800 to Huawei chips. This self-developed chip is regarded as a significant strategic transformation. Meanwhile, DeepSeek also plans to complete its first round of external financing, with a fundraising scale of approximately $7 billion, and a valuation reaching $52 billion to $59 billion.
AMD launches AI programming platform Instinct Coder, which can reduce enterprise AI coding costs by 70%
Odaily News AMD, the semiconductor giant, announced the launch of its enterprise-grade AI programming platform, AMD Instinct Coder. The platform combines AMD chips, Supermicro servers, and Spectro Cloud software, aiming to help enterprises deploy AI coding assistants locally, reduce the cost of cloud-based AI models, and protect code and data security.AMD stated that Instinct Coder is an "out-of-the-box" end-to-end AI development platform, integrating AMD EPYC processors, AMD Instinct GPUs, Supermicro AI servers, Spectro Cloud PaletteAI Inference Launchpad software, and the AMD-optimized GLM-5.2 model. It can be used for software development scenarios such as code generation, application modernization, automated testing, and code review.AMD said that compared to relying on cutting-edge cloud-based AI models, Instinct Coder can help enterprises reduce total cost of ownership (TCO) by up to 70%, with the fastest payback period shortened to 6 months.AMD noted that more and more enterprises are looking to leverage AI to improve development efficiency, but face two major challenges: on one hand, the cost of invoking top-tier cloud models continues to rise; on the other hand, entrusting enterprise source code, intellectual property, and sensitive data to third-party services poses security and compliance risks.Through a local deployment model, Instinct Coder allows enterprises to maintain control over their data and code while providing more predictable infrastructure costs. The platform supports development tools such as Claude Code, OpenAI Codex, Visual Studio Code, and Cursor, with each node supporting up to 50 users (30 concurrent users).Additionally, the PaletteAI Inference Launchpad provided by Spectro Cloud enables AI workload management, model routing, request auditing, and cost monitoring, and supports invoking external models such as Anthropic, OpenAI, Google, or xAI when needed.AMD stated that Instinct Coder aims to help enterprises break free from the high costs of cloud-based AI services, accelerate AI-driven software development processes while ensuring data security and autonomous control.
Serenity: US AI Companies Lower Inference Costs to Counter the "Model Distillation" Challenge
"White-Haired Stock Guru" Serenity stated that while some observations in the UBS report hold anecdotal truth, the more noteworthy trend is the increasing number of Chinese-language reports regarding the distillation of Anthropic's models. Currently, many US startups and tech companies are opting to use cheaper Chinese models (such as DeepSeek) in their AI applications, as their unit task costs are significantly lower than those of inference models from Gemini, OpenAI, and Anthropic.Serenity believes this trend, driven by capitalism, creates a "typical paradox"—companies naturally gravitate towards lower-cost solutions, thereby eroding the leading advantage of US models. He proposes that the US needs to address this on two fronts:First, build stronger access control and authentication systems, such as "heavy KYC frontier models" for domestic US use and tiered access mechanisms for allies, to reduce the risk of model distillation and misuse. This could also be accompanied by introducing an identity verification system akin to "AI-grade banking authentication" (e.g., biometrics + short-lived permission tokens) to raise the barrier for model calls, and using regulatory measures to restrict account sharing and access resale.Second, enhance the cost efficiency of inference models, allowing them to comprehensively outperform competitors like DeepSeek in both price and performance.Serenity also noted that some high-end models are currently frequently targeted for "distillation exploitation." Ideally, access to models nearing the AGI level should involve increased friction costs. In summary, the core challenge for the US AI industry lies in achieving both "low-cost inference capabilities" and establishing model access security mechanisms comparable to those in the financial system.
Sources: NVIDIA plans to pitch Vera AI CPU to Chinese clients, some cloud providers eyeing test deployment
sources say NVIDIA has begun pitching its first independent central processing unit (CPU) product, Vera, to Chinese clients. Designed specifically for Agentic AI systems, the chip has entered mass production, marking NVIDIA's attempt to further expand its presence in the Chinese market with a CPU offering.According to sources, some Chinese clients have already shown interest in Vera. One major Chinese cloud computing company plans to procure over 300 servers equipped with dual Vera CPUs for testing, and will decide whether to expand procurement after the tests are completed.Built on the Arm Holdings architecture, Vera is NVIDIA's first independent CPU product. NVIDIA has previously stated that Vera's performance in AI agent-related computing tasks is 1.8 times that of comparable competitor products, and expects the product to contribute approximately $20 billion in revenue by the end of this fiscal year (ending January next year).The report notes that as the AI industry's focus gradually shifts from model training to inference computing, CPUs and custom chips are gaining more attention. Vera also positions NVIDIA to directly compete with Intel and Advanced Micro Devices (AMD), which have long dominated the server CPU market.Sources indicate that due to strict U.S. export restrictions on high-end GPUs, CPUs face relatively smaller regulatory hurdles in the Chinese market compared to GPU products. Currently, some Chinese clients plan to first deploy Vera chips for testing in overseas data centers. Meanwhile, software ecosystem compatibility and existing domestic AI chip deployment frameworks may still impact the subsequent large-scale adoption of Vera. (Reuters)
Anthropic Upgrades Claude with Budget Controls, Regional Inference, Skill Loading, and Model Advisor Features
Odaily News – Anthropic has upgraded Claude this week, introducing four significant updates to Claude Managed Agents to further enhance the controllability, deployment flexibility, and task execution capabilities of enterprise-grade AI agents, including:1. New session budget control feature: Users can now set budget limits for agent sessions to achieve more precise cost management. When a session reaches its budget limit, the system will trigger an event and pause operations; users can increase the budget to resume task execution.2. Regional inference control capability: Users can now select where Claude Managed Agents run, with scheduling across globally available resources billed at standard rates. If restricted to run within US regions to meet regional deployment requirements, fees are 1.1x the standard price.3. Support for automatically loading Skills modules from user repositories;4. New model advisor feature: Users can configure a more powerful model as an "advisor," allowing task-executing agents to call upon the advisor model for second-opinion analysis during sessions, improving the quality of complex task handling.
AMD launches AI programming platform Instinct Coder, which can reduce enterprise AI coding costs by 70%
Odaily News AMD, the semiconductor giant, announced the launch of its enterprise-grade AI programming platform, AMD Instinct Coder. The platform combines AMD chips, Supermicro servers, and Spectro Cloud software, aiming to help enterprises deploy AI coding assistants locally, reduce the cost of cloud-based AI models, and protect code and data security.AMD stated that Instinct Coder is an "out-of-the-box" end-to-end AI development platform, integrating AMD EPYC processors, AMD Instinct GPUs, Supermicro AI servers, Spectro Cloud PaletteAI Inference Launchpad software, and the AMD-optimized GLM-5.2 model. It can be used for software development scenarios such as code generation, application modernization, automated testing, and code review.AMD said that compared to relying on cutting-edge cloud-based AI models, Instinct Coder can help enterprises reduce total cost of ownership (TCO) by up to 70%, with the fastest payback period shortened to 6 months.AMD noted that more and more enterprises are looking to leverage AI to improve development efficiency, but face two major challenges: on one hand, the cost of invoking top-tier cloud models continues to rise; on the other hand, entrusting enterprise source code, intellectual property, and sensitive data to third-party services poses security and compliance risks.Through a local deployment model, Instinct Coder allows enterprises to maintain control over their data and code while providing more predictable infrastructure costs. The platform supports development tools such as Claude Code, OpenAI Codex, Visual Studio Code, and Cursor, with each node supporting up to 50 users (30 concurrent users).Additionally, the PaletteAI Inference Launchpad provided by Spectro Cloud enables AI workload management, model routing, request auditing, and cost monitoring, and supports invoking external models such as Anthropic, OpenAI, Google, or xAI when needed.AMD stated that Instinct Coder aims to help enterprises break free from the high costs of cloud-based AI services, accelerate AI-driven software development processes while ensuring data security and autonomous control.
AI Reasoning Startup General Compute Secures $400 Million Loan Backed by ASIC Chips for Inference
: AI reasoning cloud startup General Compute has obtained a $400 million loan from Upper90. This deal is the world’s first financing project to use dedicated inference chips as collateral. The company has built a proprietary AI reasoning cloud platform based on SambaNova’s self-developed ASIC chips, primarily targeting Agent-type AI computing workloads. Compared to traditional GPU clouds, it offers faster token processing speeds and lower operational latency. The hardware requires no water cooling and can be directly deployed in traditional data centers and idle cryptocurrency mining facilities.
Meta's self-developed AI chip "Iris" is planned to start mass production in September, with a 2027 compute target of 14 gigawatts.
According to Reuters, Meta plans to mass-produce its self-developed data center AI chip "Iris" starting from September, as part of its fourth-generation Meta Training and Inference Accelerators project, to enhance the AI capabilities of platforms such as Facebook and Instagram and reduce reliance on external GPUs such as those from Nvidia and AMD. Internal memos show that Iris completed testing in just 6 weeks with no major defects; Meta plans to deploy 7 gigawatts of computing power this year and increase it to 14 gigawatts by 2027, with its AI infrastructure spending in 2024 potentially reaching up to $145 billion. To secure expansion, the company has signed long-term supply agreements with Samsung Electronics, Sandisk, and Sumitomo Electric to cope with "price increases" and shortages of memory and AI chips.
DeepSeek Self-Develops AI Inference Chip, Plans to Break Dependence on NVIDIA and Huawei
According to Reuters, Chinese AI startup DeepSeek is developing its own AI chips, three informed sources revealed. The chip is designed specifically for inference scenarios, rather than for model training. The project was launched approximately one year ago and remains in the early stages. The company has engaged with chip design, wafer foundry, and storage enterprises, and has quietly increased recruitment of chip design engineers without publicly posting job listings. If successfully developed, DeepSeek will reduce its reliance on Nvidia and Huawei Ascend chips, following the trend of global AI giants such as OpenAI and Anthropic developing their own hardware. Affected by U.S. export controls, DeepSeek previously shifted from Nvidia H800 to Huawei chips. This self-developed chip is regarded as a significant strategic transformation. Meanwhile, DeepSeek also plans to complete its first round of external financing, with a fundraising scale of approximately $7 billion, and a valuation reaching $52 billion to $59 billion.
Sources: NVIDIA plans to pitch Vera AI CPU to Chinese clients, some cloud providers eyeing test deployment
sources say NVIDIA has begun pitching its first independent central processing unit (CPU) product, Vera, to Chinese clients. Designed specifically for Agentic AI systems, the chip has entered mass production, marking NVIDIA's attempt to further expand its presence in the Chinese market with a CPU offering.According to sources, some Chinese clients have already shown interest in Vera. One major Chinese cloud computing company plans to procure over 300 servers equipped with dual Vera CPUs for testing, and will decide whether to expand procurement after the tests are completed.Built on the Arm Holdings architecture, Vera is NVIDIA's first independent CPU product. NVIDIA has previously stated that Vera's performance in AI agent-related computing tasks is 1.8 times that of comparable competitor products, and expects the product to contribute approximately $20 billion in revenue by the end of this fiscal year (ending January next year).The report notes that as the AI industry's focus gradually shifts from model training to inference computing, CPUs and custom chips are gaining more attention. Vera also positions NVIDIA to directly compete with Intel and Advanced Micro Devices (AMD), which have long dominated the server CPU market.Sources indicate that due to strict U.S. export restrictions on high-end GPUs, CPUs face relatively smaller regulatory hurdles in the Chinese market compared to GPU products. Currently, some Chinese clients plan to first deploy Vera chips for testing in overseas data centers. Meanwhile, software ecosystem compatibility and existing domestic AI chip deployment frameworks may still impact the subsequent large-scale adoption of Vera. (Reuters)
Related news
Morgan Stanley: CoreWeave Adds Record 500MW Capacity in Single Quarter, High Debt and Customer Concentration Weigh on Valuation
According to TechFlow Research, Morgan Stanley's August Q2 earnings report indicated that CoreWeave added 500MW of net active power in a single quarter, exceeding any quarter in history, more than three times year-over-year. Management reaffirmed the target of reaching at least 8GW before 2030. FY26 revenue guidance midpoint was raised 2% to $12.4 billion to $13.2 billion, ARR midpoint was raised 3% to $18.5 billion to $19.5 billion. The company raised full-year capital expenditure guidance midpoint by 12% to $35.5 billion to $39.0 billion, Q3 capital expenditure guidance is $11.5 billion to $13.5 billion, higher than the market expectation of $10 billion. Managed Inference Platform (managed inference platform) ARR grew from $1 million to over $100 million, expected to reach at least $250 million by year-end. The research report judges that Q2 adjusted operating margin was about 8%, higher than expected, but Q3 margin guidance of 5.8% to 7.2% is lower than market expectations, Q4 margin needs to increase significantly to achieve full-year guidance. Morgan Stanley expects CoreWeave FY27 operating margin to be 15.9%, FY28 to be 22.4%, free cash flow to remain negative until 2028, and debt is expected to increase to approximately $38 billion by the end of 2026. Morgan Stanley maintains Equal-weight (in line with the market) rating and $99 price target.
Anthropic Upgrades Claude with Budget Controls, Regional Inference, Skill Loading, and Model Advisor Features
Odaily News – Anthropic has upgraded Claude this week, introducing four significant updates to Claude Managed Agents to further enhance the controllability, deployment flexibility, and task execution capabilities of enterprise-grade AI agents, including:1. New session budget control feature: Users can now set budget limits for agent sessions to achieve more precise cost management. When a session reaches its budget limit, the system will trigger an event and pause operations; users can increase the budget to resume task execution.2. Regional inference control capability: Users can now select where Claude Managed Agents run, with scheduling across globally available resources billed at standard rates. If restricted to run within US regions to meet regional deployment requirements, fees are 1.1x the standard price.3. Support for automatically loading Skills modules from user repositories;4. New model advisor feature: Users can configure a more powerful model as an "advisor," allowing task-executing agents to call upon the advisor model for second-opinion analysis during sessions, improving the quality of complex task handling.
AMD to Acquire AI Inference Chip Startup Taalas, Strengthening Inference Market Presence
According to Reuters, AMD announced it will acquire AI inference chip startup Taalas, with the transaction amount undisclosed. This acquisition aims to enhance AMD's technical capabilities in the AI inference chip sector, with plans to integrate Taalas' technology into its Instinct GPU accelerator roadmap to further advance system-level solution development.
AMD Acquires AI Inference Chip Company Taalas
Odaily News: AMD has announced that it has reached a definitive agreement to acquire AI inference chip company Taalas, aiming to strengthen its technological competitiveness in the rapidly growing AI inference market. The acquisition is intended to reinforce AMD's technological edge in the fast-expanding AI inference sector and further advance its AI computing roadmap.AMD stated that it plans to integrate Taalas technology into its AMD Instinct GPU and AI accelerator roadmap to develop system-level AI computing solutions.
AMD launches AI programming platform Instinct Coder, which can reduce enterprise AI coding costs by 70%
Odaily News AMD, the semiconductor giant, announced the launch of its enterprise-grade AI programming platform, AMD Instinct Coder. The platform combines AMD chips, Supermicro servers, and Spectro Cloud software, aiming to help enterprises deploy AI coding assistants locally, reduce the cost of cloud-based AI models, and protect code and data security.AMD stated that Instinct Coder is an "out-of-the-box" end-to-end AI development platform, integrating AMD EPYC processors, AMD Instinct GPUs, Supermicro AI servers, Spectro Cloud PaletteAI Inference Launchpad software, and the AMD-optimized GLM-5.2 model. It can be used for software development scenarios such as code generation, application modernization, automated testing, and code review.AMD said that compared to relying on cutting-edge cloud-based AI models, Instinct Coder can help enterprises reduce total cost of ownership (TCO) by up to 70%, with the fastest payback period shortened to 6 months.AMD noted that more and more enterprises are looking to leverage AI to improve development efficiency, but face two major challenges: on one hand, the cost of invoking top-tier cloud models continues to rise; on the other hand, entrusting enterprise source code, intellectual property, and sensitive data to third-party services poses security and compliance risks.Through a local deployment model, Instinct Coder allows enterprises to maintain control over their data and code while providing more predictable infrastructure costs. The platform supports development tools such as Claude Code, OpenAI Codex, Visual Studio Code, and Cursor, with each node supporting up to 50 users (30 concurrent users).Additionally, the PaletteAI Inference Launchpad provided by Spectro Cloud enables AI workload management, model routing, request auditing, and cost monitoring, and supports invoking external models such as Anthropic, OpenAI, Google, or xAI when needed.AMD stated that Instinct Coder aims to help enterprises break free from the high costs of cloud-based AI services, accelerate AI-driven software development processes while ensuring data security and autonomous control.
Marvell Launches AI Infrastructure Memory Solution Portfolio, Targeting Agentic AI Inference Bottlenecks
Data infrastructure semiconductor giant Marvell Technology announced the launch of a new generation portfolio of memory solutions for AI infrastructure, covering server-level AI storage, rack-level CXL memory expansion and pooling, and multi-cabinet optical interconnect shared memory, aimed at addressing the growing memory capacity and bandwidth bottlenecks during Agentic AI inference. Marvell stated that as AI model scales expand, context windows lengthen, and KV Cache demands grow, traditional tightly coupled compute and memory architectures are limiting AI inference efficiency. Through memory disaggregation, memory resources can be scaled more independently from compute resources, improving GPU utilization and reducing data movement latency.