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A free scan shows the junk files, broken settings and background clutter dragging Windows down - then fixes them in one click.Free scan · Windows 10 & 11Nvidia’s BlueField-4 is a next-generation data processing unit (DPU) designed to handle the networking, storage, security and control-plane work surrounding AI systems. The standard BlueField-4 configuration combines a 64-core Arm Neoverse V2 Grace CPU with ConnectX-9 networking, PCIe Gen6 and up to 800 Gb/s of Ethernet or InfiniBand connectivity.
Nvidia first announced BlueField-4 on October 28, 2025. Later announcements expanded its role into AI-native storage, particularly the movement and reuse of large language model key-value (KV) caches. As of August 16, 2026, the platform remains an announced, staged-availability enterprise technology rather than a broadly available commodity adapter.
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What BlueField-4 does
A DPU is a processor dedicated to infrastructure tasks that would otherwise consume host-CPU resources. In an AI data center, that can include moving network and storage data, enforcing security policies, managing tenants, processing telemetry and connecting GPUs to disaggregated storage.
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Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEScan for outdated or missing drivers - takes under a minuteDriver Scan →Nvidia positions BlueField-4 as the infrastructure-processing layer of an AI factory. The GPU performs model computation, the host CPU runs applications and orchestration, and the DPU handles much of the data movement and infrastructure control close to the network and storage interfaces.
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Its advertised functions include RDMA-accelerated storage, NVMe over Fabrics, NVMe/TCP, inline encryption, service-function chaining, secure provisioning, tenant isolation and programmable services through Nvidia’s DOCA software platform.
BlueField-4 specifications
| Specification | Announced capability |
|---|---|
| Embedded CPU | 64-core Nvidia Grace CPU |
| CPU architecture | Arm Neoverse V2 |
| Networking | Up to 800 Gb/s Ethernet or InfiniBand |
| Network component | Nvidia ConnectX-9 |
| Host interface | PCIe Gen6 |
| Memory | Up to 128 GB |
| Memory bandwidth | Up to 250 GB/s |
| Inline cryptography | Up to 800 Gb/s, according to Nvidia’s technical description |
| Storage support | RDMA-accelerated block, file, object, NVMe-oF and NVMe/TCP access |
These figures come from Nvidia’s published materials, not independent testing. Nvidia’s sources use both “LPDDR5” and “LPDDR5X” descriptions, so the exact memory type may depend on the board, form factor or final OEM configuration. Nvidia also says specifications and availability may change.
Technical details are published in Nvidia’s BlueField-4 and Vera Rubin overview.
Why put 64 Grace cores in a DPU?
The Grace CPU is not intended to replace the server’s main CPU or an AI GPU. Its purpose is to provide more processing capacity for infrastructure services.
- Storage disaggregation: BlueField-4 can process storage requests, metadata and NVMe-oF traffic without sending every operation through the host CPU.
- Security: The DPU can apply encryption, tenant-isolation rules and other policies inline.
- Network services: It can support RDMA, congestion control and service chaining at high data rates.
- AI context movement: It can help move and manage KV-cache data between GPUs, storage tiers and other nodes.
- Cloud infrastructure: Cloud operators can run infrastructure services separately from customer workloads, reducing the host resources exposed to tenants.
In other words, the 64 cores are not 64 general-purpose cores available to an application. They are part of a specialized infrastructure processor intended to keep data-center plumbing from competing with model execution and customer workloads.
BlueField-4 versus BlueField-3
Nvidia’s published comparison claims substantial generational increases:
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| Metric | BlueField-3 | BlueField-4 | Nvidia’s claimed change |
|---|---|---|---|
| Network bandwidth | 400 Gb/s | 800 Gb/s | 2× |
| CPU cores | 16 Arm A78 | 64 Arm Neoverse V2 | 4× core count |
| Compute performance | Baseline | — | Up to 6× |
| Memory bandwidth | 75 GB/s | 250 GB/s | More than 3× |
| Memory capacity | 32 GB | 128 GB | 4× |
| Data-in-transit encryption | 400 Gb/s | 800 Gb/s | 2× |
| NVMe disaggregation | 10 million 4K IOPS | 20 million 4K IOPS | 2× |
The “up to” compute and IOPS figures are Nvidia claims based on its own product comparison. They should not be treated as universal performance improvements. Real-world results will depend on the network fabric, storage media, software, topology and workload.
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BlueField-4’s place in Vera Rubin
BlueField-4 is one component of Nvidia’s broader Vera Rubin AI platform. Nvidia lists the platform’s major components as the Vera CPU, Rubin GPU, NVLink 6 switch, ConnectX-9 SuperNIC, BlueField-4 DPU, Spectrum-6 Ethernet switch and Groq 3 LPU.
That positioning matters. Nvidia is presenting BlueField-4 as part of a co-designed, rack-scale AI factory rather than simply as a faster PCIe networking card. Its value is greatest when the surrounding servers, switches, storage and software can use its bandwidth and offload capabilities.
More detail is available in Nvidia’s Vera Rubin announcement.
The AI-native storage angle
The most important development after the original BlueField-4 reveal is Nvidia’s emphasis on context memory. Large language models create a key-value cache containing intermediate attention data. For long-context and multi-turn agentic workloads, that cache can become too large or too expensive to keep entirely in GPU memory.
Nvidia’s BlueField-4-powered Inference Context Memory Storage Platform is intended to place, store and retrieve KV-cache data more efficiently. Nvidia says the architecture can provide shared KV-cache access across clusters of rack-scale AI systems, hardware-accelerated cache placement, lower metadata overhead and secure, isolated access from GPU nodes.
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Nvidia has claimed up to 5× higher tokens-per-second performance and up to 5× greater power efficiency versus traditional storage for the relevant workloads. A later BlueField-4 STX announcement claimed up to 5× token throughput, up to 4× energy efficiency and 2× faster data ingestion, with partner-built platforms expected in the second half of 2026.
Those are workload-specific vendor claims, not a guarantee that every AI application will become five times faster. Results will depend on model architecture, context length, cache reuse, concurrency, storage media, network topology and whether the system is limited by compute, memory or I/O.
The January 2026 context-memory announcement and March 2026 STX announcement describe these storage developments.
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1Repair Windows errors before they cause bigger problems2Fix the driver behind crashes, sound loss and screen glitches3Clear out junk files and repair common Windows errorsBlueField-4 and BlueField-4 STX are not identical
The 64-core Grace CPU description applies to the standard BlueField-4 DPU. BlueField-4 STX is a storage-focused architecture for context-memory systems. Nvidia describes its processor as combining the Vera CPU with ConnectX-9 SuperNIC technology.
STX should therefore be understood as a modular storage architecture built around BlueField-4 technology, not as a synonym for every BlueField-4 adapter. Its final hardware, memory configuration, storage capacity and commercial availability may vary by partner.
DOCA is central to the value proposition
The hardware is only part of the product. DOCA provides the programming and deployment foundation for networking, storage, security, telemetry and lifecycle management on BlueField and ConnectX hardware.
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Nvidia also links DOCA to KV-cache reuse, storage metadata, congestion control, tenant isolation and secure provisioning. Existing BlueField users may find software continuity valuable, although Nvidia’s statement that applications are intended to run across generations remains a vendor compatibility claim until specific versions and deployments are independently validated.
Availability and pricing
Nvidia originally said BlueField-4 would enter early availability as part of Vera Rubin platforms in 2026. Nvidia later said STX-based systems would be available from partners in the second half of 2026.
As of August 16, 2026, the reviewed announcements do not establish a broad, volume-shipping standalone adapter market. The safest description is that BlueField-4 is entering staged or early availability through selected platforms and partners. Exact delivery dates and configurations must be confirmed with the relevant OEM or cloud provider.
Nvidia has not published a list price in the cited materials. Enterprise buyers should expect quote-based pricing or bundled system and cloud pricing affected by memory, networking, storage, optics, support, software licensing and integration.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Who should consider it?
BlueField-4 is most relevant to organizations operating large AI clusters or infrastructure where networking and storage are meaningful bottlenecks:
- AI clouds and hyperscale data centers
- Long-context or multi-turn inference platforms
- GPU clusters using disaggregated NVMe storage
- RDMA-intensive systems
- Multi-tenant bare-metal AI services
- Security-sensitive infrastructure requiring inline policy enforcement
- Deployments with frequent KV-cache reuse and movement
Before buying, evaluate whether the rest of the infrastructure can use 800 Gb/s networking, PCIe Gen6 and the associated storage protocols. Also assess DOCA integration, firmware and driver lifecycle, Kubernetes or cloud integration, monitoring, security operations and total power and cooling costs.
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For a small AI deployment, ordinary virtualization environment or workload dominated by GPU compute, a BlueField-4-class platform may add cost and operational complexity without solving the main bottleneck.
Trade-offs and alternatives
A DPU adds another processor, firmware stack, operating environment, security boundary and monitoring target. That can improve isolation and offload, but it also requires specialized operational expertise.
Organizations can instead retain a host CPU with a conventional NIC and storage stack. That is simpler and may be adequate when infrastructure traffic is modest, although more work remains on host CPUs.
Existing BlueField-3 users may prefer a measured upgrade path, while buyers standardized on AMD or Intel infrastructure may compare BlueField-4 with AMD Pensando-based infrastructure or Intel IPU offerings. The meaningful comparison is not core count alone; it is software compatibility, security features, storage acceleration, fabric support, performance at the intended workload and total cost of ownership.
Another route is a vendor-integrated AI storage system from a company such as Dell, HPE, NetApp, Pure Storage, VAST Data, WEKA or DDN. Nvidia names many of these companies in its BlueField-4 and STX ecosystem announcements, but being an ecosystem participant does not prove that a particular company is already shipping a purchasable BlueField-4 product.
What buyers should verify
- The exact BlueField-4 SKU, board form factor and memory configuration
- Whether the system supports Ethernet, InfiniBand, RoCE and the required switch fabric
- Actual PCIe topology and storage bandwidth in the proposed server
- DOCA versions, supported APIs, drivers and firmware-update procedures
- Power, cooling and rack-density requirements
- Independent benchmarks using the buyer’s model, cache-reuse pattern and concurrency
- Confirmed shipping date, support terms and price from the OEM or cloud provider
- Whether a proposed STX system uses the storage-focused Vera configuration rather than the standard Grace-based DPU configuration
Bottom line
BlueField-4 is Nvidia’s attempt to make the DPU a core part of AI-factory infrastructure, not merely a networking offload card. Its 64-core Grace CPU, 800 Gb/s networking, larger memory subsystem and storage/security accelerators could matter for large, multi-tenant AI systems—especially those that repeatedly move KV-cache data.
But the announcement does not establish universal performance gains, public list pricing or broad standalone availability. For most buyers, the decision should wait for an exact OEM configuration and workload-specific testing. The technology is most compelling when networking, storage, security or context-memory movement—not GPU arithmetic—is limiting the system.
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