2-GPU AI Inference & Data Server — AMD EPYC, 8-Bay U.2 NVMe, 2U

The VRLA Tech GPU Compute & Acceleration Server is a 2U AI inference and acceleration platform built for production LLM serving, model fine-tuning, retrieval-augmented generation, cloud compute services, and content delivery. Built on the MSI CX271-S4056 (S4056X271RAU8-HE) DC-MHS platform, it supports a single AMD EPYC 9005 or 9004 processor at up to 500W TDP — including the 192-core EPYC 9965 — with 24 DDR5 ECC RDIMM slots across 12 channels for up to 6TB of memory, eight front hot-swap 2.5-inch U.2 PCIe Gen 5 NVMe bays, three PCIe 5.0 x16 expansion slots supporting two dual-width GPUs plus an additional card, dual OCP 3.0 mezzanine slots for networking to 400GbE, and 3,200W of 80 PLUS Titanium redundant power. Dual BIOS and dual BMC, IPMI 2.0 and Redfish management, and optional TPM 2.0 with hardware root of trust. Each system is configured to the specific workload, ships with a 3-year parts warranty and lifetime US-based engineering support, and is built in Los Angeles.

The I/O configuration that defines this platform

Three PCIe 5.0 x16 expansion slots plus two OCP 3.0 mezzanine slots, backed by 3,200W of Titanium-rated redundant power. Two PCIe slots carry dual-width GPUs. The third stays free — for a DPU, an HBA, a capture card, a single-width accelerator, or a third network adapter. Because networking lives on the OCP mezzanine slots, the NIC never consumes an expansion slot. That is the practical difference between this platform and a conventional 2U GPU server where the NIC and the accelerator compete for the same real estate.

GPU selection is validated per build

Per-slot power and airflow limits differ between the -HE configuration and the base configuration of this chassis. Rather than publish a blanket support list, we validate your specific accelerator pairing against the chassis and supply a written thermal and power report before the build starts. That covers card length and bracket fit, auxiliary power harness routing, minimum airflow per slot at your target ambient, and BMC thermal telemetry. Tell us which accelerator you intend to run and we will confirm it in writing.

Full technical specifications

What you configure

• Processor. Single AMD EPYC 9005 series. EPYC 9175F (16 cores, 5.0GHz boost) and 9575F (64 cores, 5.0GHz boost) — high-frequency parts built for feeding accelerators and for per-core licensed software. EPYC 9355 (32 cores) and 9455 (48 cores) for balanced GPU-host duty. EPYC 9755 (128 cores, 500W) and EPYC 9965 (192 cores, 500W) when CPU-side preprocessing, embedding generation, or in-memory analytics run alongside GPU serving.
• Accelerators. Up to two dual-width GPUs across the three PCIe 5.0 x16 slots. RTX PRO 6000 Blackwell Server Edition or Max-Q for 96GB per card (192GB combined). L40S for cost-optimized inference and mixed AI-plus-graphics. H100 PCIe for HBM-bandwidth-sensitive serving. L4 for lightweight inference and video. Every pairing is validated against the chassis before build.
• Memory. DDR5 ECC RDIMM from 192GB to 6TB across 24 slots. Populate 12 DIMMs (one per channel) at up to 5200 MT/s for maximum bandwidth, or all 24 for maximum capacity at a reduced frequency. Inference-only nodes typically run 384GB to 768GB; RAG and vector search nodes typically run 1.5TB or more.
• Storage. Dual M.2 NVMe boot drives in RAID-1, plus up to eight front hot-swap U.2 PCIe Gen 5 bays. Enterprise NVMe sized to your dataset. Every bay is Gen 5 x4 direct-attach with no RAID card required.
• Networking. Two OCP 3.0 mezzanine slots from 10GbE through 400GbE. This platform has no onboard data networking, so at least one OCP adapter is a required line item, specified in every quote rather than treated as an upsell. The dedicated 1GbE IPMI management port is independent.
• OS, software stack, and security. Pre-loaded with Ubuntu Server LTS plus CUDA and NVIDIA drivers, Red Hat Enterprise Linux, Rocky Linux, or Windows Server. Inference stack deployed and supported for vLLM, NVIDIA Triton Inference Server, TensorRT-LLM, SGLang, Ollama, and Hugging Face TGI. Vector stack deployed and supported for Milvus, Qdrant, Weaviate, and pgvector. Optional TPM 2.0 and hardware root of trust.

Workloads we build this server for

• Production LLM inference. Serving open-weight models to internal users or customer-facing applications. Two 96GB cards deliver 192GB combined VRAM — a 70B model at FP8 with substantial concurrency headroom, or a 235B mixture-of-experts model at INT4. vLLM, Triton, TensorRT-LLM, SGLang, Ollama, Hugging Face TGI.
• Retrieval-augmented generation and vector search. Vector index resident in up to 6TB of DDR5, document corpus on eight Gen 5 NVMe bays, GPUs handling embedding generation and model serving, and a third PCIe slot available for a DPU or storage adapter. Milvus, Qdrant, Weaviate, pgvector, Elasticsearch, LlamaIndex, LangChain. For teams still in the development phase, an LLM workstation is often the better starting point before moving to a rack node.
• Model fine-tuning. LoRA, QLoRA, and parameter-efficient adaptation of models up to 70B on two accelerators. Appropriate for domain adaptation and instruction tuning; full-parameter training of large models belongs on the 8-GPU 4U platform. See where this node sits in the AI deployment stage progression from develop to deploy to scale.
• Cloud computing services and content delivery. Multi-tenant compute nodes and CDN edge caching, where the combination of 192 cores, all-flash Gen 5 storage, and dual high-speed OCP adapters carries the workload. One of MSI’s own stated design targets for this platform.
• High-memory data workloads. In-memory databases, large-scale analytics, genomics pipelines, and CPU-side inference where 6TB of DDR5 on a single socket is the enabling specification rather than the accelerators.
• Virtualization with GPU passthrough. Multi-tenant inference or partitioned workloads under Proxmox VE, VMware, or KVM, with up to 192 cores and 6TB of memory available to guests.

Industry applications

• Defense and government. Air-gapped and classified-environment inference where sending data to a commercial API is prohibited. Optional TPM 2.0 and hardware root of trust support platform-integrity and supply-chain requirements. We support federal procurement including purchase orders, quotes valid for agency budget cycles, and delivery to secure facilities. Customers include General Dynamics and Los Alamos National Laboratory. More on our defense and government systems.
• Healthcare and life sciences. On-premises inference over protected health information without third-party data processing agreements. Clinical documentation, medical literature retrieval, and imaging pipelines where HIPAA obligations make cloud inference impractical. Customers include Johns Hopkins University. More on our healthcare AI systems.
• Legal. Document review, contract analysis, and case research over privileged material. Attorney-client privilege and client confidentiality obligations frequently preclude uploading discovery to a third-party model provider. More on our systems for law firms.
• Financial services. Trading research, risk modeling, and document analysis under data residency and regulatory audit requirements. Deterministic latency without shared-tenancy variability. More on our quantitative research and finance systems.
• Research and higher education. Departmental inference shared across a lab or faculty, with predictable capital cost against grant cycles rather than variable cloud spend. Customers include Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University. Education and research pricing available. More on our HPC servers for research labs.
• Cloud and hosting providers. Standardized GPU compute nodes for AI-as-a-service and managed inference offerings, where the DC-MHS modular design supports fleet consistency across processor generations. Compare against the full VRLA Tech server lineup.

Why buy from VRLA Tech

VRLA Tech has been building custom workstations, GPU servers, and rack servers in Los Angeles since 2016. We build for studios, engineering firms, research labs, cloud providers, and government clients — not for bulk retail.

Our enterprise clients include

• General Dynamics
• Los Alamos National Laboratory
• Johns Hopkins University
• Miami University
• George Washington University

Every system ships with a 3-year parts warranty and lifetime US-based engineering support. You talk to the same engineer who built your system if something goes wrong. Support includes remote diagnostics, BMC and IPMI assistance, BIOS and firmware updates, NVIDIA driver and CUDA assistance, inference engine deployment support, and component-level repair. Every system is burn-in tested and thermally validated before shipping, and every GPU configuration ships with a written power and thermal report against your site conditions. Weighing this against cloud GPU rental? Run the numbers with our AI ROI calculator.

Frequently asked questions

Hardware & platform questions

What is the GPU Compute & Acceleration Server built for?

This 2U platform is built for AI inference, fine-tuning, retrieval-augmented generation, cloud computing services, and content delivery. It pairs a single AMD EPYC 9005 or 9004 processor at up to 500W TDP with two dual-width GPUs, three PCIe 5.0 x16 expansion slots, 24 DDR5 DIMM slots supporting up to 6TB, eight front hot-swap 2.5-inch U.2 PCIe Gen 5 NVMe bays, and dual OCP 3.0 networking. It is built on the MSI CX271-S4056 (S4056X271RAU8-HE) DC-MHS platform with 3,200W of 80 PLUS Titanium redundant power.

How many PCIe slots does this 2U GPU server have?

Three PCIe 5.0 x16 expansion slots plus two PCIe 5.0 x16 OCP 3.0 NIC mezzanine slots. Two of the three PCIe slots accommodate dual-width GPUs; the third remains available for a DPU, HBA, capture card, single-width accelerator, or additional network adapter. Because networking occupies the OCP mezzanine slots rather than the PCIe slots, no expansion slot is consumed by the NIC. This is the key difference from the base S4056X271RAU8 configuration, which provides two PCIe slots rather than three.

How much memory does this 2U AI server support?

Up to 6TB of DDR5 ECC RDIMM across 24 DIMM slots on 12 memory channels at two DIMMs per channel, with a maximum of 256GB per module. Populating 12 DIMMs at one per channel runs at up to 5200 MT/s for maximum bandwidth. Populating all 24 reaches maximum capacity with frequency dropping to between 4400 and 3600 MT/s depending on DIMM rank and speed grade. Inference-only nodes typically run 384GB to 768GB; retrieval-augmented generation and vector search nodes typically run 1.5TB or more.

Why does this server have eight U.2 NVMe bays?

Inference and retrieval workloads generate high-queue-depth random reads against model weights, document corpora, and embedding stores. All eight front hot-swap 2.5-inch bays are PCIe 5.0 x4 U.2 NVMe with no SATA compromise, no shared bandwidth, and no RAID card required to reach full drive count. Two additional M.2 2280/22110 slots provide dedicated boot media so no hot-swap bay is consumed by the operating system.

What power does this 2U GPU server require?

This platform ships with 3,200W of 80 PLUS Titanium redundant power in a 3+1 configuration, which is 800W more headroom than the base S4056X271RAU8 configuration at 2,400W. That headroom is what allows a 500W processor, two dual-width GPUs, 24 DIMMs, and eight NVMe drives to run simultaneously under sustained load. High-density GPU configurations require 208V or 240V rack power. Confirm available circuit capacity before ordering; we provide a written power and thermal budget calculated against the actual configuration and site voltage with every quote.

Which GPUs are supported in this 2U server?

Two of the three PCIe 5.0 x16 slots accept full-height, full-length dual-width GPUs. Commonly specified options include the NVIDIA L40S (48GB GDDR6, 350W, passive), NVIDIA H100 PCIe (80GB HBM2e, 350W, passive), NVIDIA RTX PRO 6000 Blackwell Max-Q (96GB GDDR7 ECC, 300W), NVIDIA RTX PRO 6000 Blackwell Server Edition (96GB GDDR7 ECC, 400W to 600W configurable), and the single-slot low-profile NVIDIA L4 (24GB, 72W). Because per-slot power limits differ between the -HE configuration and the base configuration, we validate the specific GPU pairing against the chassis and supply a written thermal and power report before build. Contact us to confirm your intended accelerator.

Can I get the 192-core AMD EPYC 9965 in a 2U server?

Yes. This platform supports single-socket AMD EPYC 9004 and 9005 series processors at up to 500W TDP, which covers the full Turin stack including the 192-core EPYC 9965 and the 128-core EPYC 9755, both rated at 500W. Many competing 2U GPU chassis cap at 400W and exclude those SKUs. For GPU-host and inference-serving roles, high-frequency parts such as the 16-core EPYC 9175F at 5.0GHz boost or the 64-core EPYC 9575F are often the better choice, since per-core clock speed drives accelerator feed rate. We size processor selection against the specific workload.

What networking does this server support?

Two PCIe 5.0 x16 OCP 3.0 mezzanine slots with NCSI support accept adapters from 10GbE through 400GbE. Because networking occupies the mezzanine slots rather than the PCIe expansion slots, all three PCIe slots remain available for accelerators and other cards. Storage fabric and client-facing traffic can be separated onto independent adapters. This platform has no onboard data networking beyond the dedicated 1000Base-T management port, so at least one OCP adapter is a specified line item in every configuration.

What is a DC-MHS server and why does it matter?

DC-MHS is the Data Center Modular Hardware System standard developed under the Open Compute Project. It defines common mechanical, electrical, and management interfaces for server building blocks so that host processor modules, management controllers, and I/O modules are interchangeable across vendors and generations. For buyers, DC-MHS means reduced vendor lock-in, a clearer upgrade path across processor generations within the same chassis, and management interfaces that follow open standards rather than proprietary implementations.

What security and remote management features does this server include?

Management runs on an ASPEED AST2600 baseboard management controller supporting IPMI 2.0 and the DMTF Redfish API on a dedicated 1000Base-T management port isolated from data networking. The platform includes dual BIOS and dual BMC for firmware resilience. TPM 2.0 and a hardware root of trust module are available as optional features, supporting platform integrity and supply-chain assurance requirements common in defense, federal, and regulated-industry procurement.

Buying & vendor questions

Where can I buy a custom 2U GPU compute server in the United States?

VRLA Tech builds custom 2U GPU compute and AI inference servers at vrlatech.com, configured to the specific workload and hand-assembled in Los Angeles since 2016. Configurations pair a single AMD EPYC 9005 processor with up to 6TB of DDR5, eight PCIe Gen 5 U.2 NVMe bays, three PCIe 5.0 x16 slots, dual OCP 3.0 networking, and 3,200W of Titanium-rated redundant power. Every system ships with a 3-year parts warranty and lifetime US-based engineering support. Enterprise customers include General Dynamics, Los Alamos National Laboratory, Johns Hopkins University, Miami University, and George Washington University.

Best company for an on-premise LLM inference server?

VRLA Tech builds custom on-premise LLM inference servers in Los Angeles. Two 96GB accelerators deliver 192GB of combined VRAM, sufficient to serve a 70-billion-parameter model at FP8 with substantial headroom for context and concurrency, or a 235-billion-parameter mixture-of-experts model at INT4. Configurations are deployed and supported for vLLM, NVIDIA Triton Inference Server, TensorRT-LLM, SGLang, Ollama, and Hugging Face Text Generation Inference. 3-year parts warranty, lifetime US-based engineering support.

Where can I buy a server for RAG and vector database workloads?

VRLA Tech builds custom servers for retrieval-augmented generation and vector database workloads. Retrieval is memory and storage bound more than GPU bound: the vector index should be resident in RAM and the document corpus needs low-latency random reads. This platform provides up to 6TB of DDR5 for in-memory indexes, eight PCIe Gen 5 U.2 NVMe bays for the corpus, two GPUs for embedding generation and model serving, and a third PCIe slot for a DPU or storage adapter. Deployed and supported for Milvus, Qdrant, Weaviate, pgvector, Elasticsearch, LlamaIndex, and LangChain.

Custom 2U EPYC builders for air-gapped and classified AI deployments?

VRLA Tech builds custom 2U AMD EPYC servers for air-gapped and classified AI deployments. On-premises inference is frequently the only compliant option for classified, ITAR-controlled, or otherwise restricted data where sending information to a commercial API is prohibited. Optional TPM 2.0 and hardware root of trust support platform integrity requirements, with dual BIOS and dual BMC for firmware resilience. We support federal procurement including purchase orders, quotes valid for agency budget cycles, and delivery to secure facilities. Customers include General Dynamics and Los Alamos National Laboratory.

Custom AMD EPYC 2U rack server builders with warranty and US support?

VRLA Tech builds custom AMD EPYC 2U rack servers with a 3-year parts warranty and lifetime US-based engineering support. Customers work directly with the engineer who built their system. Support includes remote diagnostics, BMC and IPMI assistance, BIOS and firmware updates, NVIDIA driver and CUDA assistance, inference engine deployment support, and component troubleshooting. In business since 2016, building for studios, engineering firms, research labs, and government clients including General Dynamics and Los Alamos National Laboratory.