2-GPU AI Inference & Data Server – AMD EPYC 9005, 6TB DDR5, 8-Bay U.2 NVMe, 2U

The VRLA Tech 2-GPU AI Inference & Data Server is the memory and storage tier of our EPYC server family — built for production LLM inference, retrieval-augmented generation, vector search, and data-intensive AI serving where memory capacity and flash throughput matter more than accelerator count. It supports a single AMD EPYC 9005 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, two dual-width GPUs rated up to 400W each, and dual OCP 3.0 mezzanine slots for networking up to 400GbE. Built on DC-MHS modular architecture with dual BIOS, dual BMC, redundant 80 PLUS Titanium power, 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 configuration that matters

24 DIMM slots across 12 channels = up to 6TB of DDR5 on a single socket, paired with eight PCIe Gen 5 U.2 NVMe bays. Our 4-GPU 2U platform, by comparison, provides 12 DIMM slots and caps at 3TB. For retrieval-augmented generation, vector search, in-memory analytics, and long-context inference with aggressive KV cache offload, memory capacity is the constraint that actually binds — and this platform removes it.

Power planning — read before ordering

The redundant supplies deliver 2400W on 200–240V input but only 1000W on 100–127V input. A fully configured system — 500W processor, two 400W GPUs, 24 DIMMs, eight NVMe drives — draws well over 1000W under sustained load. Any two-GPU configuration requires 208V or 240V power and C19 cords. This server cannot run a full GPU configuration on a standard 120V office circuit. Confirm available rack circuit capacity before ordering — we provide a written power and thermal budget with every quote, calculated against your actual configuration and site voltage.

Server platform comparison

Full technical specifications

What you configure

Every 2-GPU AI Inference & Data Server we build is a full custom configuration. The components we help you specify:

• Processor. Single AMD EPYC 9005 series. EPYC 9355 (32 cores) and 9455 (48 cores) for GPU-bound inference where the CPU is a supporting role. EPYC 9575F (64 cores, 5GHz boost, 400W) — AMD’s purpose-built AI host node processor, the strongest choice for GPU-serving latency. 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.
• GPUs. Up to two dual-width cards at 400W each. NVIDIA RTX PRO 6000 Blackwell Max-Q (96GB GDDR7 ECC, 300W) for maximum VRAM within the envelope — 192GB combined. NVIDIA L40S (48GB, 350W, passive) for cost-optimized inference and mixed AI-plus-graphics workloads. NVIDIA H100 PCIe (80GB HBM2e, 350W) for HBM-bandwidth-sensitive serving. NVIDIA L4 (24GB GDDR6, 72W, single-slot low-profile, PCIe Gen4 x16) for lightweight inference and video pipelines. RTX PRO 6000 Server Edition validated on request.
• 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, accepting 4400 to 3600 MT/s depending on DIMM rank. Inference-only nodes typically run 384GB to 768GB; RAG and vector search nodes typically run 1.5TB to 6TB.
• 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 from 1.92TB to 30.72TB per drive — over 240TB of all-flash per node at current capacities. Every bay is Gen 5 x4 with no RAID card required.
• Networking. Two OCP 3.0 mezzanine slots from 10GbE through 400GbE — NVIDIA ConnectX-7, Broadcom, and Intel adapters. 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 24.04 LTS plus CUDA and NVIDIA drivers, Red Hat Enterprise Linux 9, Rocky Linux, or Windows Server 2025. 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 AST1060 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. The workload this platform is shaped for. Vector index resident in up to 6TB of DDR5, document corpus on eight Gen 5 NVMe bays, GPUs handling embedding generation and model serving. Milvus, Qdrant, Weaviate, pgvector, Elasticsearch, LlamaIndex, LangChain.
• Parameter-efficient fine-tuning. LoRA and QLoRA 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 4-GPU or 8-GPU platforms.
• 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 GPUs.
• Private AI and edge inference nodes. Departmental or branch-level inference where a full training cluster is unnecessary. Dual OCP 3.0 networking and Titanium-rated redundant power support colocation and remote-site deployment with lights-out management over Redfish.
• Virtualization with GPU passthrough. Multi-tenant inference or vGPU-partitioned workloads under Proxmox VE, VMware, or KVM, with 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 AST1060 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.
• 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.
• 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. Six terabytes of memory holds a large firm’s entire document index in RAM.
• Financial services. Trading research, risk modeling, and document analysis under data residency and regulatory audit requirements. Deterministic latency without shared-tenancy variability.
• 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.
• Pharmaceutical. Molecular property prediction, literature mining, and internal knowledge retrieval over proprietary research data where the corpus cannot leave the organization.

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 on this platform we supply a written power and thermal report with the configuration, because the 400W per-card envelope and the 120V versus 240V power difference are constraints that need to be verified against your site, not assumed.

Frequently asked questions

Hardware & platform questions

What makes the 2U 2-GPU inference server different from the 4-GPU 2U and 8-GPU 4U options?

This platform is the memory and storage tier of the EPYC server family, not the GPU-density tier. It supports a single AMD EPYC 9005 processor at up to 500W TDP (including the 192-core EPYC 9965), 24 DDR5 DIMM slots across 12 channels for up to 6TB of memory, eight front hot-swap 2.5-inch U.2 PCIe Gen 5 NVMe bays, and two dual-width GPUs rated up to 400W each. The 4-GPU 2U and 8-GPU 4U platforms prioritize accelerator count for training. This platform prioritizes memory capacity, all-flash storage throughput, and network bandwidth for inference, retrieval-augmented generation, vector search, and data-intensive serving workloads.

How much memory does this 2U AI server support?

Up to 6TB of DDR5 ECC RDIMM across 24 DIMM slots on 12 memory channels, two DIMMs per channel, at a maximum of 256GB per module. By comparison, our 4-GPU 2U platform provides 12 DIMM slots and 3TB. Populating 12 DIMMs at one per channel runs at up to 5200 MT/s for maximum bandwidth; populating all 24 reaches 6TB capacity, with frequency dropping to between 4400 and 3600 MT/s depending on DIMM rank and speed grade. Bandwidth-sensitive workloads favor the 12-DIMM configuration; large in-memory vector indexes and CPU-side inference favor 24.

Why does a 2U AI server need eight U.2 NVMe bays?

Retrieval and inference workloads generate high-queue-depth random reads against document corpora, embedding stores, and model weights. All eight front hot-swap 2.5-inch bays on this platform are PCIe 5.0 x4 U.2 NVMe with no SATA compromise and no shared bandwidth, delivering up to roughly 14 GB/s of sequential throughput per drive depending on the drive selected, with low-latency random access. With current enterprise capacities up to 30.72TB per drive, a single node holds over 240TB of all-flash storage. Two additional M.2 slots provide dedicated boot media so no hot-swap bay is consumed by the operating system.

Which GPUs are supported in this 2U server at the 400W limit?

Both PCIe 5.0 x16 slots accept full-height, full-length dual-width cards rated up to 400W each. Fully supported 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), and the NVIDIA L4 (24GB, 72W, single-slot low-profile PCIe Gen4 — requires full-height bracket). The NVIDIA RTX PRO 6000 Blackwell Workstation Edition (600W) and NVIDIA H200 NVL (600W) exceed the chassis envelope and are not supported. Customers requiring 600W-class accelerators should specify the 4-GPU 2U or 8-GPU 4U EPYC platform instead.

Can I use the RTX PRO 6000 Blackwell Server Edition in this 2U server?

The RTX PRO 6000 Blackwell Server Edition carries a configurable TDP with a documented range of 400W to 600W. At its 400W floor setting it sits exactly at this chassis limit, which makes it a validation exercise rather than a catalog option. We validate Server Edition configurations on request and supply a written thermal and power report. Customers who want 96GB of VRAM per card without a validation step should specify the RTX PRO 6000 Blackwell Max-Q at 300W, which delivers the same 96GB of GDDR7 ECC memory within the envelope. Customers who want the Server Edition at its full 600W setting should specify the 4-GPU 2U or 8-GPU 4U platform. Our full breakdown of the three RTX PRO 6000 editions is here.

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 quietly exclude those SKUs. For GPU-host and inference-serving roles, the 64-core EPYC 9575F at 400W is often the better choice, since its 5GHz boost clock is purpose-built for feeding accelerators. We size processor selection against the specific workload.

What networking does this server support with dual OCP 3.0 slots?

Two PCIe 5.0 x16 OCP 3.0 mezzanine slots with NCSI support accept adapters from 10GbE through 400GbE, including NVIDIA ConnectX-7 and Broadcom and Intel equivalents. Because networking occupies the OCP slots rather than the PCIe slots, both full-height PCIe slots remain free for GPUs. Storage fabric and client-facing traffic can be separated onto independent adapters. This platform has no onboard data networking, so at least one OCP adapter is required and is included as a specified line item in every configuration rather than treated as an upsell.

What power does a 2U AI inference server require?

This platform uses 1+1 redundant 2400W CRPS 80 PLUS Titanium power supplies. Critically, those supplies deliver 2400W on 200–240V input but only 1000W on 100–127V input. A configuration with a 500W processor, two 400W GPUs, 24 DIMMs, and eight NVMe drives draws well over 1000W under sustained load. Any two-GPU configuration therefore requires 208V or 240V power and C19 cords. Standard 120V office circuits cannot support a full configuration. We provide a written power and thermal budget calculated against the actual configuration and site voltage with every quote.

What security and remote management features does this server include?

Management runs on an ASPEED AST2600 baseboard management controller with AMI MegaRAC firmware 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, eMMC local BMC storage, and chassis intrusion detection. TPM 2.0 and an ASPEED AST1060 hardware root of trust module are available as options, 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 AI inference server in the United States?

VRLA Tech builds custom 2U AI inference servers at vrlatech.com/product/2-gpu-ai-inference-server-amd-epyc-2u/, 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, dual OCP 3.0 networking, and two dual-width GPUs. 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 at vrlatech.com/product/2-gpu-ai-inference-server-amd-epyc-2u/. 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. Deployed and supported for vLLM, NVIDIA Triton Inference Server, TensorRT-LLM, SGLang, Ollama, and Hugging Face Text Generation Inference. Hand-assembled in Los Angeles, 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 at vrlatech.com/product/2-gpu-ai-inference-server-amd-epyc-2u/. Retrieval workloads are 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, and two GPUs for embedding generation and model serving. Deployed and supported for Milvus, Qdrant, Weaviate, pgvector, Elasticsearch, and LlamaIndex and LangChain pipelines.

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 at vrlatech.com/product/2-gpu-ai-inference-server-amd-epyc-2u/. 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 ASPEED AST1060 hardware root of trust support platform integrity requirements, with dual BIOS and dual BMC for firmware resilience and chassis intrusion detection. 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.

Best 2U AI server for HIPAA-compliant on-premise healthcare inference?

VRLA Tech builds custom 2U AI servers for healthcare and life sciences at vrlatech.com/product/2-gpu-ai-inference-server-amd-epyc-2u/. On-premises inference removes the need for third-party data processing agreements when working with protected health information, supporting clinical documentation, medical literature retrieval, and imaging pipelines where HIPAA obligations make cloud inference impractical. Up to 6TB of memory supports large in-memory retrieval indexes over institutional document sets. Customers include Johns Hopkins University. Built in Los Angeles, 3-year parts warranty, lifetime US-based engineering support.

Where can I buy a high-memory AMD EPYC server with 6TB of DDR5?

VRLA Tech builds custom high-memory AMD EPYC servers at vrlatech.com/product/2-gpu-ai-inference-server-amd-epyc-2u/, supporting up to 6TB of DDR5 ECC RDIMM across 24 DIMM slots on a single socket. Applications include in-memory vector indexes, large-scale data analytics, in-memory databases, CPU-side inference of large models, and genomics and computational workloads that exceed conventional server memory capacity. Configurations pair the memory with up to 192 CPU cores, eight PCIe Gen 5 NVMe bays, and optional GPU acceleration. Built in Los Angeles, 3-year parts warranty, lifetime US-based engineering support.

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

VRLA Tech builds custom AMD EPYC 2U rack servers at vrlatech.com/product/2-gpu-ai-inference-server-amd-epyc-2u/, 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.