As revealed during a recent Q1 earnings call, Intel leadership mentioned that "external clients are getting their ASICs designs tested." The company's foundry business is working towards the finalization of its much discussed 18A node process, with alleged trial samples receiving an "impressive performance rating." According to Ctee Taiwan, Team Blue's foundry service has submitted test subjects to the likes of NVIDIA, Broadcom and Faraday Technology. The latter organization has (reportedly) disclosed that the 18A platform tape-out was completed last October—since then, received samples have been "successfully connected." Industry moles believe that NVIDIA and Broadcom are in the middle of conducting manufacturing tests. Additional whispers suggest the delivery of 18A prototypes chez IBM and several other unnamed partner companies. Insiders have indicated impressive/good "verification results." Contrary to reports from other sources, Ctee has picked up on insider chatter about Intel's next-gen Nova Lake compute tile design being "not entirely outsourced." Further conjecture points to Team Blue becoming increasingly confident in its own manufacturing techniques.

Intel just reported its Q1 revenue results, and there are several interesting highlights from the earnings call. Intel Foundry, long touted for a comeback, is generating most of its revenue from a single customer, and it isn't the latest node. Intel's biggest customer is actually itself, predominantly using the "Intel 7" node (Intel's name for its 10 nm SuperFin process), which underpins the Alder Lake and Raptor Lake consumer CPU generations as well as the Sapphire Rapids Xeon server generation. As Intel ramps up 18A-node production and external clients begin testing their ASIC designs, 18A still isn't the Foundry division's primary revenue driver. Instead, demand for Intel 7 wafers is being fueled by massive orders for Intel's 13th- and 14th-generation Raptor Lake processors.

During the Q1 earnings call, Intel CFO Dave Zinsner noted, "Intel Foundry delivered revenue of $4.7 billion, up 8% sequentially on pull-ins of Intel 7 wafers and increased advanced packaging services." He also commented on the Q1 Foundry operating loss of $2.3 billion, attributing it to "startup costs associated with the ramp of products on Intel 18A." While the 18A node is gradually scaling to volume production for upcoming internal and external products, older nodes continue to fuel the revenue stream. Zinsner further confirmed that "we have a lot of important building blocks in place, including the ramp of Intel 18A in the second half of 2025 to support the launch of our first Panther Lake SKU by year-end, with additional SKUs coming in the first half of 2026."

Global Unichip Corp. (GUC), the Advanced ASIC Leader, announced today that it has successfully taped-out the world's first HBM4 controller and PHY IP. This test chip was implemented using TSMC's cutting-edge N3P process technology and CoWoS -R advanced packaging technology.

The HBM4 IP supports data rates of up to 12 Gbps under all operating conditions. By leveraging a proprietary interposer layout, GUC has optimized signal integrity (SI) and power integrity (PI) to achieve these high speeds for all types of CoWoS technology. Comparing with HBM3, GUC's HBM4 PHY delivers 2.5x bandwidth while improving 1.5x power efficiency and 2x area efficiency. In line with previous GUC HBM, GLink, and UCIe IPs, this HBM4 IP integrates proteanTecs' interconnect monitoring solution to provide high visibility for testing and characterizing the PHY while improving in-field performance and reliability for end products.

NVIDIA has developed co-packaged optics (CPO) technology with TSMC for its upcoming Quantum-X InfiniBand and Spectrum-X Ethernet switches, integrating silicon photonics directly onto switch ASICs. The engineering approach reduces power consumption by 3.5x. It decreases signal loss from 22 dB to 4 dB compared to traditional pluggable optics, addressing critical power and connectivity limitations in large-scale GPU deployments, especially in 10,000+ GPU systems. The architecture incorporates continuous wave laser sources within the switch chassis, consuming 2 W per port, compared to the 10 W required by conventional externally modulated lasers in pluggable modules. This configuration, combined with integrated optical engines that use 7 W versus 20 W for traditional digital signal processors, reduces total optical interconnect power from approximately 72 MW to 21.6 MW in a 400,000 GPU data center scenario.

Specifications for the Quantum 3450-LD InfiniBand model include 144 ports running at 800 Gb/s, delivering 115 Tb/s of aggregate bandwidth using four Quantum-X CPO sockets in a liquid-cooled chassis. The Spectrum-X lineup features the SN6810 with 128 ports at 800 Gb/s (102.4 Tb/s) and the higher-density SN6800 providing 512 ports at 800 Gb/s for 409.6 Tb/s total throughput. The Quantum-X InfiniBand implementation uses a monolithic switch ASIC with six CPO modules supporting 36 ports at 800 Gb/s, while the Spectrum-X Ethernet design employs a multi-chip approach with a central packet processing engine surrounded by eight SerDes chiplets. Both architectures utilize 224 Gb/s signaling per lane with four lanes per port. NVIDIA's Quantum-X switches are scheduled for availability in H2 2025, with Spectrum-X models following in H2 2026.

Semiconductor intellectual property core provider CAST today announced a new IP core that implements the SNOW-V stream cipher algorithm to meet the security and performance demands of modern communication systems. Available now for ASICs or FPGAs, the company believes it to be the first such commercial IP core.

The new SNOW-V Stream Cipher Engine provides a flexible and reusable hardware implementation of the official SNOW-V mechanism as published in 2019 by the IACR Transactions on Symmetric Cryptology. SNOW-V revises the SNOW 3G stream cipher algorithm to help satisfy the high-speed, low-latency security requirements of 5G, 6G, and future mobile networks. The core:

Report with market evolution powered by AI—The global artificial intelligence (AI) chips market size is estimated to grow by USD 902.6 billion from 2025-2029, according to Technavio. The market is estimated to grow at a CAGR of over 81.2% during the forecast period. Increased focus on developing AI chips for smartphones is driving market growth, with a trend towards convergence of AI and IoT. However, dearth of technically skilled workers for ai chips development poses a challenge. Key market players include Advanced Micro Devices Inc., Baidu Inc., Broadcom Inc., Cerebras, Fujitsu Ltd., Google LLC, Graphcore Ltd., Huawei Technologies Co. Ltd., Intel Corp., International Business Machines Corp., MediaTek Inc., Microchip Technology Inc., NVIDIA Corp., NXP Semiconductors NV, Qualcomm Inc., SambaNova Systems Inc., Samsung Electronics Co. Ltd., SenseTime Group Inc., Taiwan Semiconductor Manufacturing Co. Ltd., and Tesla Inc.

Here is the first picture of what is very likely the GeForce RTX 5090 "Blackwell," the successor to the RTX 4090 "Ada." The picture, its massive GPU, and layout appear to confirm the weekend's bare PCB leak. The RTX 5090 is powered by the "GB202" silicon, the largest gaming GPU based on the "Blackwell" graphics architecture. The silicon in the picture has the ASIC code "GB202-300-A1." From this ASIC code, we can deduce that the RTX 5090 may not max out the silicon (i.e. enable all SM present on it), as maxed-out NVIDIA ASICs tend to have the variant designation "450."

The "GB202" ASIC is surrounded by sixteen GDDR7 memory chips, which reportedly make the 32 GB memory size of the RTX 5090. The chip count, coupled with the large GPU package size (high pin-count), confirm that the "GB202" features a 512-bit wide memory bus. Assuming a memory speed of 28 Gbps, this memory bus should yield a stellar memory bandwidth of 1,792 GB/s. The GPU and memory are surrounded by the card's 24-phase VRM solution. This draws power from a single 16-pin 12V-2x6 power connector. NVIDIA will likely max out the 600 W continuous power-delivery capability of the connector, and give the card a TGP of around 500-550 W, if not more.

MultiDimension Technology Co., Ltd. (MDT), a global leader in magnetic sensors specializing in Tunneling Magnetoresistance (TMR) technology, introduces its groundbreaking TMR2615/ TMR2617 series at Electronica and SPS. These cutting-edge TMR sensors are poised to revolutionize gaming experience with exceptional performance and innovative features.

Game-Changing Technology for Consumer Electronics
TMR2615/TMR2617 are powered by MDT's proprietary TMR technology, integrated with a programmable ASIC offering factory-preset tailored to user-defined parametric targets. This ensures high consistency and optimal performance across large-scale production, addressing diverse demands for thumbsticks and trigger keys in gaming controllers.

The gaming industry is about to get massively disrupted. Instead of using game engines to power games, we are now witnessing an entirely new and crazy concept. A startup specializing in designing ASICs specifically for Transformer architecture, the foundation behind generative AI models like GPT/Claude/Stable Diffusion, has showcased a demo in partnership with Decart of a Minecraft clone being entirely generated and operated by AI instead of the traditional game engine. While we use AI to create images and videos based on specific descriptions and output pretty realistic content, having an AI model spit out an entire playable game is something different. Oasis is the first playable, real-time, real-time, open-world AI model that takes users' input and generates real-time gameplay, including physics, game rules, and graphics.

An interesting thing to point out is the hardware that powers this setup. Using a single NVIDIA H100 GPU, this 500-million parameter Oasis model can run at 720p resolution at 20 generated frames per second. Due to limitations of accelerators like NVIDIA's H100/B200, gameplay at 4K is almost impossible. However, Etched has its own accelerator called Sohu, which is specialized in accelerating transformer architectures. Eight NVIDIA H100 GPUs can power five Oasis models to five users, while the eight Sohu cards are capable of serving 65 Oasis runs to 65 users. This is more than a 10x increase in inference capability compared to NVIDIA's hardware on a single-use case alone. The accelerator is designed to run much larger models like future 100 billion-parameter generative AI video game models that can output 4K 30 FPS, all thanks to 144 GB of HBM3E memory, yielding 1,152 GB in eight-accelerator server configuration.

According to a recent Reuters report, OpenAI is continuing with its moves in the custom silicon space, expanding beyond its reported talks with Broadcom to include a broader strategy involving multiple industry leaders. Broadcom is a fabless chip designer known for a wide range of silicon solutions spanning from networking, PCIe, SSD controllers, and PHYs all the way up to custom ASICs. The company behind ChatGPT is actively working with both Broadcom and TSMC to develop its first proprietary AI chip, specifically focused on inference operations. Getting a custom chip to do training runs is a bit more complex task, and OpenAI leaves that to its current partners until the company figures out all details. Even with an inference chip, the scale at which OpenAI works and serves its models makes financial sense for the company to develop custom solutions tailored to its infrastructure needs.

This time, the initiative represents a more concrete and nuanced approach than previously understood. Rather than just exploratory discussions, OpenAI has assembled a dedicated chip team of approximately 20 people, led by former Google TPU engineers Thomas Norrie and Richard Ho. The company has secured manufacturing capacity with TSMC, targeting a 2026 timeline for its first custom-designed chip. While Broadcom's involvement leverages its expertise in helping companies optimize chip designs for manufacturing and manage data movement between chips—crucial for AI systems running thousands of processors in parallel—OpenAI is simultaneously diversifying its compute strategy. This includes adding AMD's Instinct MI300X chips to its infrastructure alongside its existing NVIDIA deployments. Similarly, Meta has the same approach, where it now trains its models on NVIDIA GPUs and serves them to the public (inferencing) using AMD Instinct MI300X.

Marvell Technology, Inc. (NASDAQ: MRVL), a leader in data infrastructure semiconductor solutions, today announced the development of FBNIC, a custom 5 nm network interface controller (NIC) ASIC in collaboration with Meta to meet the company's infrastructure and use case requirements. The FBNIC board design will also be contributed by Marvell to the Open Compute Project (OCP) community. FBNIC combines a customized network controller designed by Marvell and Meta, a co-designed board, and Meta's ASIC, firmware and software. This custom design delivers innovative capabilities, optimizes performance, increases efficiencies, and reduces the average time needed to resolve potential network and server issues.

"The future of large-scale, data center computing will increasingly revolve around optimizing semiconductors and other components for specific applications and cloud infrastructure architectures," said Raghib Hussain, President of Products and Technologies at Marvell. "It's been exciting to partner with Meta on developing their custom FBNIC on our industry-leading 5 nm accelerated infrastructure silicon platform. We look forward to the OCP community leveraging the board design for future innovations."

In the past 13 years, global data production has surged, increasing an estimated 74 times. (1) Looking forward, McKinsey projects AI to spur 35% annual growth in enterprise SSD capacity demand, from 181 Exabytes (EB) in 2024 to 1,078EB in 2030. (2) To address this growing demand, ScaleFlux, a leader in data storage and memory technology, is announcing a significant expansion of its product portfolio. The company is introducing cutting-edge controllers for both NVMe SSDs and Compute Express Link (CXL) modules, reinforcing its leadership in innovative technology for the data pipeline. "With the release of three new ASIC controllers and key updates to its existing lineup, ScaleFlux continues to push the boundaries of SSD and memory performance, power efficiency, and data integrity," points out Hao Zhong, CEO and Co-Founder of the company.

Three New SoC Controllers to Transform Data Center Storage
ScaleFlux is proud to unveil three new SoC controllers designed to enhance data center, AI and enterprise infrastructure:

NVIDIA is possibly launching a more cost-effective variant of its GeForce RTX 4070 graphics card featuring GDDR6 memory, replacing the GDDR6X that it originally launched with. The new SKU would be better differentiated from the RTX 4070 SUPER. When NVIDIA refreshed its RTX 40-series "Ada" product stack in January, it discontinued the RTX 4070 Ti and RTX 4080, which had been replaced in the lineup by the RTX 4070 Ti SUPER and RTX 4080 SUPER, but at the time it didn't tinker with the RTX 4070, which continued to sell at a roughly $50 lower price than the RTX 4070 SUPER. This new SKU could be an attempt by NVIDIA to get further down below the $500-mark in pricing.

The RTX 4070 originally launched with 21 Gbps GDDR6X memory. This new variant sees the memory replaced with 20 Gbps conventional GDDR6. The JEDEC standard GDDR6 chips could be cheaper than GDDR6X, and could very well be the same GDDR6 chip models AMD uses in some of its higher Radeon RX 7000 series SKUs. This, however, comes with a 4.75% drop in memory bandwidth, which NVIDIA probably overcomes with increasing the GPU clocks a touch. The ASIC code for this SKU is AD104-251, compared to the AD104-250 of the original RTX 4070. The core configuration is otherwise unchanged—you get 5,888 CUDA cores across 46 streaming multiprocessors. Galax has a card based on this SKU ready.

Avnet ASIC, a division of Avnet Silica, an Avnet company, today announced that it has launched its new ultra-low-power design services for TSMC's cutting-edge 4 nm and below process technologies. These services are designed to enable customers to achieve exceptional power efficiency and performance in their high-performance applications, such as blockchain and AI edge computing. TSMC is the world's leading silicon foundry and Avnet ASIC division is a leading provider of ASIC and SoC full turnkey solutions.

The new design services leverage a comprehensive approach to address the challenges of operating at extreme low-voltage conditions in the 4 nm and below nodes. This includes recharacterizing standard cells for lower voltages, performing early RTL exploration to optimize power, performance, and area (PPA) tradeoffs, implementing an optimized clock tree, and utilizing transistor-level simulations to enhance the power optimization process.

According to The Information, OpenAI is reportedly in talks with Broadcom about developing a custom AI accelerator to power OpenAI's growing demand for high-performance solutions. Broadcom is a fabless chip designer known for a wide range of silicon solutions spanning from networking, PCIe, SSD controllers, and PHYs all the way up to custom ASICs. The latter part is what OpenAI wants to focus on, but all the aforementioned IP developed by Broadcom is of use in a data center. Suppose OpenAI decides to use Broadcom's solutions. In that case, the fabless silicon designer offers a complete vertical stack of products for inter-system communication using various protocols such as PCIe, system-to-system communication using Ethernet networking with Broadcom Tomahawk 6 and future revisions, alongside storage solutions and many other complimentary elements of a data center.

As a company skilled in making various IPs, it also makes ASIC solutions for other companies and has assisted Google in the making of its Tensor Processing Unit (TPU), which is now in its sixth generation. Google TPUs are massively successful as Google deploys millions of them and provides AI solutions to billions of users across the globe. Now, OpenAI wants to be part of the AI chip game, and Broadcom could come to the rescue with its already-established AI success and various other data center componentry to help make a custom AI accelerator to power OpenAI's infrastructure needed for the next generation of AI models. With each new AI model released by OpenAI, compute demand spikes by several orders of magnitude, and having an AI accelerator that exactly matches their need will help the company move faster and run even bigger AI models.

Tenstorrent is launching their next generation Wormhole chip featuring PCIe cards and workstations designed for developers who are interested in scalability for multi-chip development using Tenstorrent's powerful open-source software stacks.

These Wormhole-based cards and systems are now available for immediate order on tenstorrent.com:

• Wormhole n150, powered by a single processor
• Wormhole n300, powered by two processors
• TT-LoudBox, a developer workstation powered by four Wormhole n300s (eight processors)

TrendForce's latest industry report on AI servers reveals that high demand for advanced AI servers from major CSPs and brand clients is expected to continue in 2024. Meanwhile, TSMC, SK hynix, Samsung, and Micron's gradual production expansion has significantly eased shortages in 2Q24. Consequently, the lead time for NVIDIA's flagship H100 solution has decreased from the previous 40-50 weeks to less than 16 weeks.

TrendForce estimates that AI server shipments in the second quarter will increase by nearly 20% QoQ, and has revised the annual shipment forecast up to 1.67 million units—marking a 41.5% YoY growth.

ByteDance, TikTok's parent company, is reportedly working with American chip designer Broadcom to develop a cutting-edge AI processor. This collaboration could secure a stable supply of high-performance chips for ByteDance, according to Reuters. Sources claim the joint project involves a 5 nm Application-Specific Integrated Circuit (ASIC), designed to comply with U.S. export regulations. TSMC is slated to manufacture the chip, though production is not expected to begin this year.

This partnership marks a significant development in U.S.-China tech relations, as no public announcements of such collaborations on advanced chips have been made since Washington implemented stricter export controls in 2022. For ByteDance, this move could reduce procurement costs and ensure a steady chip supply, crucial for powering its array of popular apps, including TikTok and the ChatGPT-like AI chatbot "Doubao." The company has already invested heavily in AI chips, reportedly spending $2 billion on NVIDIA processors in 2023.

A new startup emerged out of stealth mode today to power the next generation of generative AI. Etched is a company that makes an application-specific integrated circuit (ASIC) to process "Transformers." The transformer is an architecture for designing deep learning models developed by Google and is now the powerhouse behind models like OpenAI's GPT-4o in ChatGPT, Anthropic Claude, Google Gemini, and Meta's Llama family. Etched wanted to create an ASIC for processing only the transformer models, making a chip called Sohu. The claim is Sohu outperforms NVIDIA's latest and greatest by an entire order of magnitude. Where a server configuration with eight NVIDIA H100 GPU clusters pushes Llama-3 70B models at 25,000 tokens per second, and the latest eight B200 "Blackwell" GPU cluster pushes 43,000 tokens/s, the eight Sohu clusters manage to output 500,000 tokens per second.

Why is this important? Not only does the ASIC outperform Hopper by 20x and Blackwell by 10x, but it also serves so many tokens per second that it enables an entirely new fleet of AI applications requiring real-time output. The Sohu architecture is so efficient that 90% of the FLOPS can be used, while traditional GPUs boast a 30-40% FLOP utilization rate. This translates into inefficiency and waste of power, which Etched hopes to solve by building an accelerator dedicated to power transformers (the "T" in GPT) at massive scales. Given that the frontier model development costs more than one billion US dollars, and hardware costs are measured in tens of billions of US Dollars, having an accelerator dedicated to powering a specific application can help advance AI faster. AI researchers often say that "scale is all you need" (resembling the legendary "attention is all you need" paper), and Etched wants to build on that.

D-Central Technologies proudly announces the launch of its latest innovations in Bitcoin mining hardware, the Bitaxe Supra v401 and the NerdAxe Ultra. These cutting-edge devices are set to revolutionize the Bitcoin mining landscape with their advanced features and unparalleled performance.

"The latest Bitaxe Supra v401 and NerdAxe Ultra set a new standard in Bitcoin mining accessibility", Jonathan Bertrand, CEO, D-Central Technologies

Today at Microsoft Build, AMD (NASDAQ: AMD) showcased its latest end-to-end compute and software capabilities for Microsoft customers and developers. By using AMD solutions such as AMD Instinct MI300X accelerators, ROCm open software, Ryzen AI processors and software, and Alveo MA35D media accelerators, Microsoft is able to provide a powerful suite of tools for AI-based deployments across numerous markets. The new Microsoft Azure ND MI300X virtual machines (VMs) are now generally available, giving customers like Hugging Face, access to impressive performance and efficiency for their most demanding AI workloads.

"The AMD Instinct MI300X and ROCm software stack is powering the Azure OpenAI Chat GPT 3.5 and 4 services, which are some of the world's most demanding AI workloads," said Victor Peng, president, AMD. "With the general availability of the new VMs from Azure, AI customers have broader access to MI300X to deliver high-performance and efficient solutions for AI applications."

Phison is arguably the most popular brand for SSD controllers in the client segment, but is turning more of attention to the vast enterprise segment. The company had been making first-party enterprise SSDs under its main marquee, but decided that the lineup needed its own brand that enterprise customers could better discern from the controller ASIC main brand. We hence have Pascari and Imagin. Pascari is an entire product family of fully built enterprise SSDs from Phison. The company's existing first-party drives under the main brand will probably migrate to the Pascari catalog. Imagin, on the other hand, is a design service for large cloud and data-center customers, so they could develop bespoke tiered storage solutions at scale.

The Pascari line of enterprise SSDs are designed completely in-house by Phison, feature their latest controllers, firmware, PCB, PMIC, and on-device power-failure protection on select products. The third-party components here are the NAND flash and DRAM chips, which have both been thoroughly evaluated by Phison for the best performance, endurance, and reliability, at their enterprise SSD design facility in Broomfield, Colorado. Phison already had a constellation of industry partners and suppliers to go around with, and the company's drives even power space missions; but the Pascari brand better differentiates the fully-built SSD lineup from the ASIC make. Pascari makes its debut with the X200 series high-performance SSDs for high-access heat data. The drive leverages Phison's latest PCIe Gen 5 controller technology, the most optimized memory components, and availability in all contemporary server storage form-factors.

During the Vision 2024 event, Intel announced its latest Gaudi 3 AI accelerator, promising significant improvements over its predecessor. Intel claims the Gaudi 3 offers up to 70% improvement in training performance, 50% better inference, and 40% better efficiency than Nvidia's H100 processors. The new AI accelerator is presented as a PCIe Gen 5 dual-slot add-in card with a 600 W TDP or an OAM module with 900 W. The PCIe card has the same peak 1,835 TeraFLOPS of FP8 performance as the OAM module despite a 300 W lower TDP. The PCIe version works as a group of four per system, while the OAM HL-325L modules can be run in an eight-accelerator configuration per server. This likely will result in a lower sustained performance, given the lower TDP, but it confirms that the same silicon is used, just finetuned with a lower frequency. Built on TSMC's N5 5 nm node, the AI accelerator features 64 Tensor Cores, delivering double the FP8 and quadruple FP16 performance over the previous generation Gaudi 2.

The Gaudi 3 AI chip comes with 128 GB of HBM2E with 3.7 TB/s of bandwidth and 24 200 Gbps Ethernet NICs, with dual 400 Gbps NICs used for scale-out. All of that is laid out on 10 tiles that make up the Gaudi 3 accelerator, which you can see pictured below. There is 96 MB of SRAM split between two compute tiles, which acts as a low-level cache that bridges data communication between Tensor Cores and HBM memory. Intel also announced support for the new performance-boosting standardized MXFP4 data format and is developing an AI NIC ASIC for Ultra Ethernet Consortium-compliant networking. The Gaudi 3 supports clusters of up to 8192 cards, coming from 1024 nodes comprised of systems with eight accelerators. It is on track for volume production in Q3, offering a cost-effective alternative to NVIDIA accelerators with the additional promise of a more open ecosystem. More information and a deeper dive can be found in the Gaudi 3 Whitepaper.

Samsung debuted its Mach-1 generation of AI processors during a recent shareholder meeting—the South Korean megacorp anticipates an early 2025 launch window. Their application-specific integrated circuit (ASIC) design is expected to "excel in edge computing applications," with a focus on low power and efficiency-oriented operating environments. Naver Corporation was a key NVIDIA high-end AI customer in South Korea (and Japan), but the leading search platform firm and creator of HyperCLOVA X LLM (reportedly) deliberated on an adoption alternative hardware last October. The Korea Economic Daily believes that Naver's relationship with Samsung is set to grow, courtesy of a proposed $752 million investment: "the world's top memory chipmaker, will supply its next-generation Mach-1 artificial intelligence chips to Naver Corp. by the end of this year."

Reports from last December indicated that the two companies were deep into the process of co-designing power-efficient AI accelerators—Naver's main goal is to finalize a product that will offer eight times more energy efficiency than NVIDIA's H100 AI accelerator. Naver's alleged bulk order—of roughly 150,000 to 200,000 Samsung Mach-1 AI chips—appears to be a stopgap. Industry insiders reckon that Samsung's first-gen AI accelerator is much cheaper when compared to NVIDIA H100 GPU price points—a per-unit figure of $3756 is mentioned in the KED Global article. Samsung is speculated to be shopping its fledgling AI tech to Microsoft and Meta.

Tenstorrent and MosChip Technologies announced today that they are partnering on design for Tenstorrent's cutting-edge RISC-V solutions. In selecting MosChip Technologies, Tenstorrent stands to strongly advance both its own and its customers' development of RISC-V solutions as they work together on Physical Design, DFT, Verification, and RTL Design services.

"MosChip Technologies is special in that they have unparalleled tape out expertise in design services, with more than 200 multi-million gate ASICs under their belt", said David Bennett, CCO of Tenstorrent. "Partnering with MosChip enables us to design the strongest RISC-V solution we can to serve ourselves, our partners, and our customers alike."