Tyan B7119F77V14HR-2T55-N 10-GPU Machine Learning Server Platform

Overview

The Tyan B7119F77V14HR-2T55-N is a barebone server platform from the Thunder HX FA77-B7119 line, built around a 10-GPU chassis layout for machine learning and AI training workloads. At 95 lb and sized at 6.93 x 30.31 x 17.24 in (L x W x H), this is a dense rackmount chassis engineered to carry a heavy GPU load in a data center footprint rather than a general-purpose compute box. If you're scoping infrastructure for model training, inference clusters, or GPU-dense HPC nodes, the B7119F77V14HR-2T55-N (often searched as B7119F77V14HR 2T55 N) is built specifically for that class of deployment.

Key Features

• 10-GPU platform architecture: the chassis is purpose-built to house ten GPUs, which matters if you're consolidating training capacity into fewer rack units instead of spreading GPUs across multiple lower-density servers.
• 95 lb chassis weight: a loaded 10-GPU server carries real mass — plan rack rail load ratings and lift-assist accordingly before installation day, especially once GPUs, drives, and PSUs are populated.
• 6.93 x 30.31 x 17.24 in footprint: the 30.31 in depth is the number to check against your rack's usable depth before ordering — a GPU-dense chassis this size needs adequate airflow clearance front and rear, not just a fit on paper.
• Thunder HX FA77-B7119 platform family: part of Tyan's high-density compute line built around GPU-forward workloads rather than general storage or web-tier serving.

Integration & Compatibility

As a barebone platform, the B7119F77V14HR-2T55-N is designed to be configured out with CPUs, memory, GPUs, storage, and networking to match a specific training or inference workload. Confirm rack depth, power circuit capacity, and cooling budget at the room level before deployment — a 10-GPU chassis draws substantially more power and generates more heat per rack unit than a standard 1U/2U server, and your data center's power and cooling plan needs to account for that up front.

What's in the Box

Ships as a barebone chassis platform. Confirm exact included components against your specific configuration at time of order, since barebone platforms vary by build.

Frequently Asked Questions

Q: How many GPUs does the Tyan B7119F77V14HR-2T55-N support?

A: The platform is built as a 10-GPU chassis, designed for GPU-dense machine learning and AI training deployments.

Q: What are the dimensions of the B7119F77V14HR-2T55-N?

A: The chassis measures 6.93 x 30.31 x 17.24 in (L x W x H) and weighs approximately 95 lb.

Q: Is the B7119F77V14HR-2T55-N a complete server or a barebone platform?

A: It ships as a barebone server platform from Tyan's Thunder HX FA77-B7119 line, intended to be configured with CPUs, memory, GPUs, and storage for a specific workload.

Q: What workloads is the B7119F77V14HR-2T55-N designed for?

A: It's built for machine learning and AI training workloads that require high GPU density in a single chassis.

Marty Allison

The B7119F77V14HR-2T55-N is a 10-GPU barebone platform from Tyan's Thunder HX FA77-B7119 line, and the spec that jumps out first is the chassis itself — at 95 lb empty and 30.31 in deep, this is built to carry serious GPU density, not to be a generalist rack server.

Technical Highlights:

• 10-GPU chassis layout: consolidates training capacity that would otherwise need multiple lower-density servers into a single rack footprint.
• 95 lb bare chassis weight: confirms this is a heavy-duty build before you even add GPUs, drives, and dual PSUs — factor that into rail and rack weight budgets.
• 30.31 in chassis depth: the dimension most likely to trip up a rack fit check — measure your cabinet's usable depth before this ships.

Deployment Considerations:

• As a barebone platform, budget separately for CPUs, GPUs, memory, and storage — this SKU is the chassis and platform, not a turnkey configured server.
• A fully populated 10-GPU chassis this size will pull considerably more power and generate more heat per rack unit than a standard 1U/2U server — verify your PDU circuit capacity and CRAC/cooling budget before committing rack space.

This is the right starting platform for a dedicated GPU training node in an ML/AI cluster build-out where rack space is at a premium and you need to concentrate GPU count per rack unit — not a fit for general virtualization or file-serving duty.