During the first half of 2026, surging demand for KV Cache coupled with constrained memory supply resulted in severe memory bottlenecks. To resolve the KV Cache bottlenecks, industry players are seeking solutions from both the supply side of KV Cache-addressable memory capacity and the demand side. Regarding the expansion of the addressable memory capacity, Penguin Solutions launched the MemoryAI™ KV Cache Server, Marvell introduced the Structera S CXL switch, and Meta developed its proprietary Vistara CXL switch to expand the memory hierarchy. On the demand side, NVIDIA introduced KVTC, and Google launched TurboQuant to compress the KV Cache.

This report provides an in-depth analysis of: (1) the KV Cache bottleneck; (2) expanding available KV Cache capacity through CXL and KV Cache offloading; (3) reducing KV Cache capacity demand via attention mechanisms and KV Cache quantization; (4) methods for improving decode efficiency, specifically MTP and DiffusionGemma; and (5) the broader impact on the memory market. The objective is to evaluate the technical principles, performance metrics, and future development trajectories of various KV Cache debottlenecking technologies.

Key Highlights

• KV Cache demand growth alongside limited memory supply created significant bottlenecks in early 2026.
• Vendors (Penguin Solutions, Marvell, Meta) are expanding addressable memory capacity via CXL-based solutions. 
• NVIDIA and Google are reducing KV Cache demand through compression technologies.
• Report covers CXL and KV Cache offloading, attention/quantization methods, decode efficiency (MTP, DiffusionGemma), and memory market impact. 
• Analysis focuses on technical principles and development trends of debottlenecking approaches. 

Table of Contents

1. The KV Cache Bottleneck
   • Figure 1: Example of KV Cache in Use
   • Figure 2: KV Cache Expansion Relative to Context Window Size (Using Llama 3 70B as an Example)
2. Expanding Available KV Cache Capacity via CXL and KV Cache Offloading
   • Figure 3: Applications of CXL Switch
   • Table 1: Evolution of the Specifications for CXL
   • Figure 4: Applications for ACF-S
   • Figure 5: Applications for EMFASYS
   • Figure 6: MemoryAI KV Cache Server with 8 x 1TB CXL AICs from Penguin Solutions
   • Figure 7: CXL AIC from Penguin Solutions
   • Figure 8: Marvell’s Structera X
   • Figure 9: Architecture of Meta’s Vistara
   • Figure 10: Architecture of Meta’s MemServer
3. Compressing KV Cache Capacity Demand via Attention Mechanism and KV Cache Quantization
   • Figure 11: Principles of Different Multi-Head Attention Mechanisms
   • Figure 12: Workflow of KVTC Compression
   • Figure 13: Process of Recursive Polar Coordinate Transformation
   • Table 2: Comparison of KVTC and TurboQuant Technologies
4. Decode Efficiency Enhancement Methods－MTP and DiffusionGemma
   • Figure 14: Example of Speculative Decoding
   • Figure 15: Operating Principles of Meta’s MTP
   • Figure 16: Operating Principles of DeepSeek’s MTP
   • Table 3: Comparison of MTP Technologies Across Companies
   • Figure 17: Sudoku as an Example of Google’s DiffusionGemma in Use
   • Table 4: Comparison of MTP and DiffusionGemma Technologies
5. Impact on the Memory Market
6. TRI’s View

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