Samsung HBM4 Memory Reportedly Reaches Nearly 80% Yield as AMD Adopts It for MI400 AI GPUs
Samsung is gaining new momentum in the race for next-generation AI memory. The company’s HBM4 memory has reportedly reached a yield rate of nearly 80%, a major milestone that could strengthen its position in the fast-growing high-bandwidth memory market.
This is a significant step for Samsung, which has been working to catch up with key competitors in the HBM space. High-bandwidth memory has become one of the most important components in modern AI accelerators, data center GPUs, and high-performance computing systems. As demand for AI chips continues to rise, reliable HBM production has become just as critical as raw memory performance.
Samsung’s latest progress suggests that its HBM4 roadmap is moving faster than expected. Earlier in the year, when mass production began, the company’s HBM4 yield was reportedly below 60%. Reaching close to 80% within a relatively short period marks a strong improvement in manufacturing stability and production efficiency.
In the memory industry, an 80% yield is often viewed as a major benchmark because it indicates that production is becoming commercially strong and scalable. For Samsung, this achievement could help improve customer confidence, expand supply capacity, and increase revenue from AI-focused DRAM products.
Samsung’s HBM4 memory is designed for demanding AI workloads where bandwidth, capacity, power efficiency, and thermal performance are essential. The company’s current HBM4 solution includes 12-Hi stacks with capacities of up to 36 GB per stack. These modules are said to deliver speeds ranging from 11.7 Gbps to 13.0 Gbps.
The technical foundation of Samsung’s HBM4 is also notable. It uses 10nm-class DRAM process technology, known as 1c, along with a 4nm logic process. The memory offers up to 3.3 TB/s of bandwidth per stack and uses a 2048-pin interface. Samsung has also highlighted major efficiency and cooling gains, including a claimed 40% improvement in power efficiency, 10% better thermal resistance compared with HBM3E, and 30% better heat dissipation compared with HBM3E.
These improvements matter because AI accelerators are becoming more powerful and more power-hungry. As GPU and AI chip makers scale up performance, memory can quickly become a bottleneck. Faster and more efficient HBM allows processors to handle larger AI models, speed up training and inference, and improve overall system performance in AI factories and high-performance computing environments.
Samsung’s progress with HBM4 is also expected to support its revenue growth. The company has reportedly raised its quarterly revenue expectations for HBM4 DRAM by three times for the third quarter. It is also expected that HBM4 will make up more than 60% of Samsung’s HBM product mix in the second half of 2026.
That shift shows how important HBM4 has become to Samsung’s broader memory strategy. Traditional DRAM remains important, but AI-focused memory is now one of the most valuable growth areas in the semiconductor industry. Companies that can deliver high performance, strong yields, and stable supply are likely to benefit as cloud providers, AI startups, and enterprise customers continue investing in large-scale AI infrastructure.
AMD is already using Samsung’s HBM4 memory for its Instinct MI400 platform. The AMD Instinct MI400 series is built for frontier AI, AI factory deployments, and high-performance computing workloads. These GPUs are designed to help organizations deploy, scale, and manage advanced AI systems that require enormous memory bandwidth.
The adoption by AMD is an important validation point for Samsung. AI accelerator makers need memory suppliers that can meet strict performance and reliability requirements. If Samsung can continue improving yield and production capacity, it could become a more competitive supplier for future AI GPU platforms.
Samsung is also making progress with HBM4E, the enhanced version of HBM4. The company has reportedly improved reliability test yield for HBM4E to around 70%. Its HBM4E memory is expected to offer 48 GB capacities and up to 4 TB/s bandwidth per DRAM stack, which would make it even more attractive for next-generation AI accelerators.
Looking ahead, Samsung is aiming to expand its presence across future AI chip platforms, including upcoming GPU architectures from major industry players. With HBM4 now showing stronger yields and HBM4E development moving forward, Samsung appears to be in a much better position than it was during earlier HBM generations.
The broader takeaway is clear: Samsung is becoming a more serious force in the AI memory market. After a slower start in high-bandwidth memory, the company is now showing measurable progress in production, performance, and customer adoption.
If Samsung can maintain this pace, its HBM4 and HBM4E products could play a major role in powering the next wave of AI data centers, supercomputers, and advanced GPU platforms. The near-80% HBM4 yield milestone is not just a manufacturing achievement; it is a sign that Samsung’s AI DRAM strategy is gaining traction at exactly the right time.
