NVIDIA Benchmarks Vera CPU Storage Performance in BlueField-4 STX

NVIDIA Benchmarks Vera CPU Storage Performance in BlueField-4 STX NVIDIA released benchmark data demonstrating that its Vera CPU, integrated into the BlueField-4 STX Storage Processor, accelerates core storage tasks including encryption, recovery, integrity checking, and compression compared to an x86 processor. The Vera CPU architecture contains 88 Armv9.2 Olympus cores supporting 176 Spatial Multithreading threads, paired with a 164 MB unified L3 cache offering up to 3.4 TB/s bisection bandwidth and SOCAMM2 LPDDR5X memory delivering up to 1.2 TB/s aggregate bandwidth. Against a comparison x86 CPU in memory-resident tests, Vera achieved up to 1.43x higher AES-128 encryption throughput, 1.29x decryption throughput, 3.26x Reed-Solomon recovery throughput, and 3.67x CRC32C throughput. In data reduction and sequential processing, Vera reached up to 3.29x higher compression throughput, 1.72x higher concurrent decompression throughput, and 3.21x higher throughput in a two-stage compression and encryption pipeline. Hardware Architecture and Benchmark Methodology The Vera CPU includes 88 Olympus cores compatible with the Armv9.2 instruction set and supports 176 threads via NVIDIA Spatial Multithreading. It incorporates the NVIDIA Scalable Coherency Fabric with 3.4 TB/s of bisection bandwidth, a 164 MB unified L3 cache, and modular SOCAMM2 LPDDR5X memory providing up to 1.2 TB/s aggregate bandwidth (up to 14 GB/s per core). To isolate processor efficiency from SSD, network, or external device bottlenecks, the benchmarks evaluated single-process, memory-resident tasks using standard libraries such as OpenSSL, Zstandard, and LZ4 under identical software controls on both Vera and x86 platforms. Data Security, Integrity, and Recovery Performance In security and resilience microbenchmarks, Vera delivered up to 1.43x higher throughput for AES-128 encryption and up to 1.29x higher throughput for AES-128 decryption over the baseline x86 CPU. For erasure coding, Vera showed up to 3.26x higher throughput in Reed-Solomon recovery tasks, which reconstruct damaged or missing data during repairs and degraded reads. In integrity validation, Vera achieved up to 3.67x higher throughput executing CRC32C checksum calculations, reducing processing overhead across data read and write paths. Compression and Multi-Stage Pipeline Execution Evaluating capacity-reduction workloads, Vera posted up to 3.29x higher compression throughput and up to 1.72x higher decompression throughput under concurrency relative to the x86 processor. When evaluating a representative multi-stage write pipeline that sequentially compresses and then encrypts in-memory data buffers, Vera demonstrated up to 3.21x higher aggregate throughput. NVIDIA notes that while individual primitives and sequential write paths showed substantial gains, full end-to-end testing is still required to quantify complete storage-system or GPU-level results.

NVIDIA公布Vera CPU存储基准测试：全面加速AI原生存储加密、压缩与恢复 NVIDIA发布针对AI原生存储的Vera CPU基准测试数据，展示了该芯片在BlueField-4 STX存储处理器中应对智能体AI（Agentic AI）高并发存储负载时的性能优势，在加密、纠错恢复、完整性校验及压缩等场景中显著超越对比的x86 CPU。 Vera CPU采用Armv9.2架构的88个自研Olympus核心，支持176个并发线程，搭配3.4 TB/s对分带宽的SCF总线与1.2 TB/s带宽的SOCAMM2 LPDDR5X内存。 基准测试显示，Vera在AES-128加密和解密吞吐上分别达到对比x86 CPU的1.43倍和1.29倍。 在数据保护与校验方面，Vera的Reed-Solomon纠错恢复吞吐量达到对比x86的3.26倍，CRC32C完整性检查吞吐量达到3.67倍。 在数据缩减与多阶段写入流程中，Vera的压缩吞吐达到3.29倍，解压达到1.72倍，压缩加加密双阶段流水线吞吐达到3.21倍。 架构针对存储双重需求优化 针对存储流水线单流低延迟与多并发高带宽的双重需求，Vera CPU集成了88个Olympus核心，提供宽指令吞吐与向量及加密资源，配备164MB统一L3缓存，以及总带宽达1.2 TB/s（每核心最高14 GB/s）的模块化SOCAMM2 LPDDR5X内存，确保在多并发场景下减少线程干扰并维持可预测的访问延迟。 基础存储原语基准测试 测试基于内存驻留环境进行微基准评估，消除了外部I/O瓶颈，使用OpenSSL、Zstandard和LZ4等通用库。测试显示，Vera在AES-128加密/解密、Reed-Solomon恢复与CRC32C校验中均展现出更低的CPU开销与更高的吞吐能力，能够加速存储数据重建、读取降级修复及安全写入。 压缩及复合存储流水线加速 在数据缩减测试中，Vera的压缩与高并发解压吞吐大幅领先x86。同时，在模拟真实存储写入路径的“压缩后加密”复合流水线测试中，Vera实现了3.21倍于x86的吞吐量，为AI工厂降低存储占用、节省网络带宽并防止CPU成为存储数据流的性能瓶颈。

Storage is an active part of every agentic AI workflow. As agents retrieve enterprise knowledge, access persistent memory, reuse key-value (KV) cache data, execute tools, and generate new results, storage systems must continuously supply and preserve the data that moves the agent reasoning loop. Each agent step can trigger multiple storage operations, and those operations can repeat across thousands of concurrent agents with increasingly larger context windows. Supplying and preserving this data requires more than basic reads and writes. AI inference runs on GPUs, but agentic processes, tool calls, data management tasks, and the storage services that support them run on CPUs. During writes, storage may compress and encrypt data, calculate checksums, and calculate redundancy. During reads, it may validate, decrypt, decompress, or reconstruct data before returning it to the application. These functions are essential to the security and resilience of AI systems. Each function also requires additional CPU processing as data moves through the storage path. As agent concurrency (multiple users, AI agents, or tasks running in parallel) and context volumes grow, storage must perform more of this work without constraining application responsiveness or token generation; it must supply data at the rate required for accelerated computing. Many of these functions sit directly in the data path; one delayed operation can slow the broader data flow. Scaling them with conventional CPUs can require more cores, power, and cooling, increasing infrastructure cost while still leaving performance dependent on the slowest step. Faster SSDs and networks cannot deliver their full potential if the processor securing, protecting, and preparing the data cannot keep pace. Closing the storage processing gap The NVIDIA Vera BlueField-4 STX Storage Processor , a key component of the NVIDIA STX foundation for AI-native data platforms, brings the NVIDIA Vera CPU performance directly into the storage data path. The same Vera CPU architecture designed to keep NVIDIA Rubin GPUs fed also accelerates the CPU-side storage processing. The benchmark results show Vera outperforming the x86 CPU across encryption and decryption, recovery, integrity checking, compression and decompression, and a multi-stage storage pipeline. These gains enable storage platforms to process more data and apply essential enterprise services with less CPU and power overhead, while higher compression throughput helps reduce storage capacity and bandwidth demands. This post explains how the Vera CPU in BlueField-4 STX accelerates the storage processing required by agentic AI, helping AI-native storage platforms secure, protect, validate, and compress more data while increasing storage-processing throughput and efficiency. Vera CPU architecture: Built for storage’s dual demands Vera CPU includes 88 NVIDIA-designed Olympus CPU cores that are fully compatible with the Armv9.2 instruction set. The CPU supports 176 NVIDIA Spatial Multithreading threads. It pairs these cores with the NVIDIA Scalable Coherency Fabric (SCF), Small Outline Compression Attached Memory Module (SOCAMM2) LPDDR5X memory to sustain strong single-thread performance, and high-throughput CPU execution at AI-factory scale. The SCF provides a coherent, on-die data path across the cores, shared cache, memory controllers, and I/O, with up to 3.4 TB/s of bisection bandwidth and a 164 MB unified L3 cache. This gives active cores high-bandwidth, predictable access to shared data as workloads scale across the processor. The SOCAMM2 LPDDR5X memory subsystem complements the fabric with up to 1.2 TB/s of aggregate memory bandwidth, or up to 14 GB/s per core, helping keep the NVIDIA Olympus cores supplied across bandwidth-intensive and highly concurrent workloads. Modular, field-replaceable memory combines LPDDR5X power efficiency with the serviceability and reliability required for datacenter infrastructure. Storage primitives place two distinct demands on a CPU. First, within each data stream, encryption, integrity checking, recovery, compression, and decompression must complete quickly before subsequent storage processing can proceed, making sustained per-core performance important. Second, across the system, these operations run over many concurrent streams and repeatedly move data through caches and memory, making bandwidth and predictable latency equally important. Vera addresses both requirements. The Olympus core combines wide instruction throughput, advanced branch prediction, deep out-of-order execution, and vector and cryptographic resources to help each core sustain instruction throughput across control-heavy and data-processing code. NVIDIA Spatial Multithreading, the monolithic compute die, SCF, unified L3 cache, and high-bandwidth SOCAMM2 memory help keep active cores supplied with data while reducing thread-to-thread interference and supporting more predictable data access under load. Together, these capabilities help explain the measured gains across encryption, integrity checking, parity calculations, compression, and the multi-stage storage pipeline. This enables the BlueField-4 STX Storage Processor to sustain more CPU-side storage processing across concurrent data streams without proportional increases in CPU resources, power, and cooling. Measuring foundational storage performance Storage tasks execute repeatedly across storage read, write, and recovery paths. Their throughput and efficiency help determine whether CPU-side processing keeps pace with SSDs and networks or becomes the limiting stage in the data path. The storage primitive microbenchmarks in this post isolate these functions to measure the processor’s contribution. They show the CPU performance and headroom available in Vera for building higher-throughput, more efficient storage services. Each test runs within a single process using data already held in memory. The tests exclude file I/O, disk performance, networking, command startup, and external-device bottlenecks unless otherwise identified. The benchmark set uses common libraries, including OpenSSL, Zstandard, and LZ4, along with comparable implementations optimized to use the native instructions available on Arm and x86 processors. A purpose-built test framework runs each workload consistently and controls buffer sizes, thread counts, CPU placement, timing, correctness validation, and result collection. The algorithms and many of the software implementations are widely adopted. The test framework applies the same workload definitions and controls across Vera and x86, enabling a consistent processor comparison. The results can be reproduced using the source code, scripts, fixed software versions, configurations, and result files, although the complete benchmark set is not an off-the-shelf public benchmark. These measurements establish Vera’s performance on the storage building blocks that influence secure data movement, resilience, capacity efficiency, and service density. Production storage paths often apply several of these operations to the same data, causing their CPU-processing requirements to accumulate. When combined in production storage software, the performance of these individual operations contributes to aggregate throughput and CPU efficiency. Higher throughput across individual primitives and the multi-stage pipeline gives storage software more CPU headroom to keep pace with SSDs and networks, support concurrent data flows, and apply essential data services efficiently. End-to-end testing is still required to quantify complete storage-system or GPU-performance outcomes. Securing more AI data with faster encryption AI factories process sensitive information, including model assets, enterprise knowledge, agent context, prompts, outputs, and customer data. AES-128 is widely used for data-at-rest and data-in-flight encryption. Encryption sits directly in the write path, where its throughput can determine how much secure data a platform can process each second before encryption becomes a bottleneck. Vera delivers up to 1.43x higher AES-128 encryption throughput than the x86 CPU used for comparison. Figure 1. NVIDIA Vera accelerates AES-128 storage encryption Decryption performs the corresponding operation on the read path andVera delivers up to 1.29x higher AES-128 decryption throughput than the x86 CPU used for comparison. Figure 2. NVIDIA Vera accelerates AES-128 storage decryption Higher encryption throughput enables storage systems to secure more data without constraining writes, while faster decryption reduces the time required to return protected data to agents, applications, or accelerators. These help storage systems protect and return growing volumes of AI data without consuming an increasing share of the storage-performance budget. Protecting and recovering AI data faster Storage platforms use erasure coding to protect data when drives, nodes, or data fragments become unavailable. Reed-Solomon encoding creates redundancy, while recovery uses that redundancy to reconstruct missing or corrupted data. Encoding typically occurs on the write path. Recovery occurs during rebuild, repair, or a degraded read. Their performance results can differ because these operations use different compute and memory-access patterns. Vera delivers up to 3.26x higher Reed-Solomon throughput than the x86 CPU in a recovery workload. Figure 3. NVIDIA Vera accelerates Reed-Solomon storage recovery Higher Reed-Solomon throughput enables storage systems to write protected data and reconstruct missing data faster. In selected efficiency measurements, Vera also completes more protection work within the available CPU power envelope, helping shorten rebuilds and reduce contention with normal data services. Validating data integrity at higher throughput Data must remain correct as it is moved, stored, and retrieved. Cyclic redundancy checks, or CRCs, create checksums that storage systems use to detect accidental corruption. CRC and Reed-Solomon perform complementary roles. CRC detects that data no longer matches the expected result. Reed-Solomon provides the redundancy used to reconstruct missing or corrupted information. A storage system may calculate CRCs on both the write and read paths, including while copying data between buffers. As data volumes grow, these checks can consume a meaningful share of CPU resources. Vera delivers up to 3.67x higher CRC32C throughput than the x86 CPU. Figure 4. NVIDIA Vera accelerates CRC32C storage integrity checking Higher CRC32C throughput enables storage systems to validate more data without integrity checking limiting reads or writes. For agentic workloads, this helps return reliable context, persistent memory, and enterprise data with less CPU-side processing delay. Reducing data footprint with faster compression and decompression Agentic AI creates growing volumes of context, logs, checkpoints, retrieval data, intermediate outputs, and persistent memory. Compression reduces the storage capacity required for this data and the bandwidth needed to move it. Decompression restores the data when it is read. Compression may run inline or after data is written, depending on the storage architecture, while decompression is typically required when compressed data is read. Compression throughput affects how quickly data can be reduced, and decompression throughput can affect how quickly storage systems return data to agentic AI applications. The following benchmarks measure these operations independently. Vera delivers up to 3.29x higher compression throughput than the x86 CPU, while sustaining its advantage across the measured thread counts. Figure 5. NVIDIA Vera accelerates storage compression The decompression benchmark measures the corresponding operation when compressed data is read. Vera delivers up to 1.72x higher decompression throughput than the x86 CPU under concurrency, with its advantage increasing as more worker threads run in parallel. Figure 6. NVIDIA Vera accelerates storage decompression Compression performance varies by algorithm, data characteristics, compression level, buffer size, and thread count, so these results apply specifically to the measured workloads. Higher compression throughput enables storage systems to reduce the amount of data written, stored, and transferred while sustaining CPU-side processing performance. Higher decompression throughput helps storage systems return decompressed data to agents and applications more quickly. Together, these capabilities lower pressure on storage capacity and bandwidth while enabling each processor to compress and decompress more data as agentic AI workloads grow. Accelerating a multi-stage storage write path Storage systems rarely execute data services independently. A secure write path may compress data to reduce its footprint and then encrypt it before it is written. The benchmark set includes a memory-resident pipeline that applies compression followed by encryption to each data buffer. Unlike the preceding benchmarks, which measure individual operations, this test measures total pipeline throughput when two CPU-intensive storage functions execute in sequence. Vera delivers up to 3.21x higher pipeline throughput than the x86 CPU used in the benchmark for the two-stage compression and encryption pipeline. Figure 7. NVIDIA Vera accelerates a multi-stage compression and encryption pipeline. This result shows that Vera’s performance advantage extends beyond the individual operations measured earlier to a multi-stage sequence representative of a storage write path that reduces and protects data. Higher multi-stage pipeline performance enables the storage system to process more data per processor, helping sustain write throughput as agentic AI data volumes grow. Scaling agent execution and storage with Vera Agentic AI makes CPU execution and storage processing part of the same AI factory data path. The CPU runs tools, code, retrieval, analysis, and data-processing steps between model calls. The storage system secures, protects, validates, compresses, and returns the data those steps require. Both must scale within the finite CPU, power, and cooling resources available across the AI factory. Vera was designed to accelerate CPU-dependent work for the agentic AI era. In NVIDIA Vera Rubin, it serves as the host CPU for NVIDIA GPUs and supports agent execution. Standalone Vera delivers up to 1.8x higher performance per core in agentic tools. In BlueField-4 STX, Vera powers the CPU-side processing used by AI-native storage platforms. The benchmark results show Vera accelerating encryption and decryption for secure data access, Reed-Solomon recovery for faster reconstruction of missing or corrupted data during storage rebuilds and repairs, CRC32C for high-throughput integrity validation, and compression and decompression to reduce capacity and bandwidth demands and accelerate data retrieval. By sustaining these functions individually and within a multi-stage write path, Vera helps AI-native storage platforms process and return secure, reliable data with less CPU-side delay. It also supports more concurrent data flows and higher service density without proportional growth in CPU resources, power, and cooling. Across selected workloads, Vera also delivers higher measured performance per watt, enabling more CPU-side storage processing within the available processor power budget. A common Vera CPU architecture and software toolchain across compute and storage provides a consistent foundation for scaling both agent execution and the supporting data infrastructure. Learn more about Vera CPUs and BlueField-4 STX Read NVIDIA Vera CPU: Olympus Cores Built for Maximum Single-Thread Performance in Agentic AI to learn more about the architecture powering these storage-processing results. Download the NVIDIA Vera BlueField-4 STX Storage Processor datasheet and learn how NVIDIA BlueField infrastructure processors accelerate storage, networking, and security for AI factories.