The machine is an unusually ambitious deskside AI system, with a configuration advertised around 748 GB of coherent memory. Yet its headline numbers, apparent retail availability, expansion hardware, and Windows prospects all need more careful interpretation than a typical workstation announcement. For Windows professionals in particular, the central point is simple: the currently listed WS300T60L is an Ubuntu system, while Windows-oriented DGX Station systems from MSI remained an expectation for later in 2026.
Shipping is the real August development
MSI’s March announcement established the original launch date for the XpertStation WS300 and said orders could begin immediately. On August 27, MSI instead said the system was now shipping and available through ASI, D&H, and Newegg. For prospective buyers, that is still important news: a product moving into distribution is materially different from one that exists only as an announced configuration.
It does not, however, prove that every sales channel had immediately deliverable inventory. A Newegg Business listing showed a September 2 release date and a pre-order status when the information was collected. That could reflect the normal lag between an OEM’s shipment announcement, distributor availability, and a particular retailer’s inventory system. It means customers should not equate “shipping” with guaranteed same-day delivery from every storefront.
This is especially relevant at this price and class of equipment. A company considering a six-figure AI workstation will likely need to verify lead time, support coverage, installation expectations, and the exact configuration with its intended supplier. A public retail listing can be useful evidence that a product is entering the market, but it is not a substitute for a confirmed order and delivery commitment.
The $99,999 price is real—but not necessarily MSRP
A U.S. Newegg listing displayed the WS300 at $99,999.00, though it also marked the system out of stock. That provides a firmer market reference than treating the price as wholly unknown or merely estimating that it may approach $100,000.
There is an important qualification. The available material does not establish that $99,999 is MSI’s universal official MSRP. MSI’s shipping announcement names distribution channels but does not provide a manufacturer list price. The retailer figure could be an intended market price, a retailer-specific figure, or a temporary listing price. Buyers should therefore treat it as an observed U.S. retail listing, not as a confirmed global price or a guarantee of what every channel will charge.
Even with that caveat, the price helps frame the WS300 correctly. This is not positioned as a conventional high-end desktop for a developer who needs faster GPU rendering or occasional local model testing. It is an enterprise-oriented local AI system whose value proposition depends on workloads that are expensive, sensitive, difficult to move to cloud services, or sufficiently persistent to justify dedicated infrastructure.
The economics will not be defined by purchase price alone. Organizations should account for electrical capacity, cooling and acoustic requirements, physical placement, networking, deployment expertise, vendor support, storage planning, security controls, and the cost of operating a specialized Linux-based AI environment. A $99,999 purchase can be easier to approve than recurring cloud bills in some cases, but only after a team has established genuine utilization and a clear operational owner.
What “748 GB of coherent memory” means
The headline configuration combines 496 GB of LPDDR5X CPU memory with 252 GB of HBM3e GPU memory, for 748 GB of coherent memory. It is a striking specification because it points to a system designed around very large AI workloads rather than the familiar division between a CPU with ordinary system RAM and a discrete GPU with comparatively limited video memory.
Coherency is meaningful. In broad terms, it can make it easier for software to work across CPU and GPU memory resources without treating them as completely isolated pools. That can be valuable when a workload needs large models, large context windows, data preparation, or complex pipelines that would otherwise run into a GPU-memory boundary.
But coherent memory should not be read as a promise of uniform memory behavior. The published figures associated with this configuration distinguish the 496 GB LPDDR5X portion, rated at 396 GB/s, from the 252 GB HBM3e portion, rated at 7.1 TB/s. Those are vastly different bandwidth classes. A workload’s performance will still depend on where data resides, how software schedules work, the behavior of the model and framework, and whether its bottleneck is compute, memory capacity, memory bandwidth, storage, or network transfer.
That is the practical correction to the eye-catching 748 GB number. Capacity is potentially transformative for some local AI tasks; it does not mean all 748 GB performs like HBM3e. Buyers should ask for benchmarks that resemble their own deployment—model size, quantization method, batch size, context length, simultaneous users, and inference-versus-training mix—rather than selecting a system solely on its aggregate memory total.
The materials describe the platform as using an NVIDIA Grace CPU with 72 Arm Neoverse V2 cores alongside an NVIDIA Blackwell Ultra GPU. That architecture further reinforces that the WS300 is a specialized AI platform, not simply a conventional x86 workstation with a powerful graphics card inserted into it. Software certification, operating-system support, and application compatibility deserve the same scrutiny as raw specifications.
Storage and expansion are broader than the simple headline
Storage is not limited to two M.2 slots. The WS300 is described as having four 2280 M.2 NVMe ports: two CPU-attached PCIe 5.0 ports populated with 2 TB RAID 1 storage, plus two open ConnectX-8-attached PCIe 6.0 ports. MSI also lists three PCIe 5.0 expansion slots.
That arrangement has useful implications for an AI deployment. The supplied RAID 1 boot storage provides redundancy for the operating system and core software environment, but it should not be mistaken for a complete data-management plan. Local model repositories, vector databases, training corpora, evaluation data, checkpoints, logs, and container images can consume space quickly. Teams should determine whether the open M.2 capacity and PCIe expansion meet their anticipated storage needs and whether their backup and recovery design is appropriate for data sensitivity.
The networking specification is similarly aimed beyond a solitary desktop workflow. MSI and NVIDIA describe dual 400 GbE networking through NVIDIA ConnectX-8, for up to 800 Gb/s of aggregate bandwidth, and describe linking up to two DGX Station or WS300 systems for larger workloads.
That does not mean every buyer will have an environment capable of using such connectivity. Many offices lack the switching, cabling, storage infrastructure, and network design needed to make 400 GbE useful. But in a datacenter-adjacent lab, research group, media pipeline, or enterprise AI team with high-speed shared storage, the networking may be as strategically important as the local accelerator. It enables the WS300 to be considered as part of a broader compute and data environment rather than an isolated tower.
An additional GPU requires explicit configuration confirmation
NVIDIA says a DGX Station can be configured with up to one additional RTX PRO Blackwell-generation GPU. That describes a possible DGX Station configuration, not a confirmed feature of every MSI WS300T60L sold through the channel.
For the WS300T60L specifically, buyers should obtain confirmation from MSI or their reseller that an RTX option is offered and supported. They should also establish the exact GPU model, the software support available for the intended workload, and the warranty terms before ordering. An expansion slot alone is not sufficient evidence that a particular accelerator configuration is a supported product option.
There is also a performance trade-off to investigate. An independent hands-on review reported that an optional RTX PRO GPU shares the WS300’s 1,600 W system power budget with the GB300 module. When both are under load, power assigned to the RTX card can reduce the B300 power cap and, in turn, B300 performance.
This is a useful reminder that a second GPU may expand capability without delivering a simple “more is always better” result. It could make sense where separate visualization, graphics, simulation, or parallel workloads benefit from another accelerator. It may be less attractive if the primary requirement is extracting the maximum possible performance from the principal AI GPU under sustained load.
Before selecting that option, an organization should ask its supplier how the intended software will use the accelerators and what performance is expected when they are used together. The relevant question is not merely how many GPUs fit in the chassis; it is whether the offered configuration advances the organization’s specific workload without an unacceptable trade-off.
Windows users should focus on the operating-system gap
For WindowsForum readers, the most consequential limitation is current software positioning. The listed WS300T60L configuration specifies Ubuntu 24.04 LTS with NVIDIA AI Developer Tools pre-installed. That is the concrete operating-system baseline documented for the listed product.
MSI’s June 3, 2026 COMPUTEX announcement said the WS300 “supports Windows-based AI development.” That wording is compatible with Windows developers using the machine as a networked Linux AI resource, but it does not establish a shipping Windows WS300T60L SKU. It also does not establish that the Ubuntu model can be converted to Windows, or that such a conversion would be supported under MSI’s warranty and certification terms.
NVIDIA separately said on May 31 that Windows DGX Station systems, including systems from MSI, were expected in the fourth quarter of 2026. As of August 30, that remained a future expectation rather than confirmation of a Windows WS300T60L product. The June MSI statement should therefore not be treated as resolving the gap between Windows-based development and a Windows system delivered, validated, and supported by the OEM.
That distinction should shape purchasing decisions. A Windows-centric organization should not buy the currently listed Ubuntu configuration on the assumption that Windows support will necessarily arrive later for the same hardware under the same warranty and certification terms. A specific Windows SKU, supported conversion route, and upgrade policy remain unconfirmed in the available product information.
This does not make the WS300 irrelevant to Windows environments. It may still serve Windows-based teams over the network, as a dedicated Linux AI resource accessed by developers and services elsewhere in the organization. But that is operationally different from giving an engineer a locally managed Windows workstation. Identity integration, remote administration, file access, development tooling, security policies, driver support, and help-desk workflows all need to be planned around the actual operating system.
A sensible Windows buyer’s checklist is therefore direct:
- Confirm whether the intended deployment can operate as an Ubuntu-based AI node.
- Obtain written confirmation of the exact operating-system support, certification, warranty implications, and any Windows SKU or upgrade path before ordering.
- Verify with MSI or the reseller whether an RTX PRO Blackwell-generation GPU option is offered and supported for the WS300T60L.
- Validate that critical frameworks, containers, model-serving tools, and management tooling support the Grace/Blackwell environment.
- Test representative workloads, not just synthetic accelerator benchmarks.
- Verify facility power, cooling, high-speed network, and data-governance requirements.
A specialized system entering a practical buying phase
MSI’s August announcement matters because the WS300 is moving from launch claims toward actual channel distribution. Its observed $99,999 retail listing, immense coherent-memory configuration, high-speed networking, and expansion potential make it one of the more unusual deskside AI systems now being marketed.
Still, the details are more important than the superlatives. The 748 GB figure spans memory tiers with very different bandwidth. Channel shipping does not settle immediate retailer delivery. An additional RTX GPU requires a confirmed supported WS300T60L configuration and can involve a shared power-envelope trade-off. Most importantly for Windows buyers, language about Windows-based AI development does not turn the listed Ubuntu 24.04 LTS WS300T60L into a shipping, warranty-supported Windows workstation.
For enterprises with sustained local AI demands, suitable facilities, and Linux-ready operations, those trade-offs may be entirely reasonable. For a Windows-first buyer looking for a familiar plug-in workstation experience, the prudent course is to wait for explicit Windows product, support, and availability details—or to treat the WS300 as a Linux infrastructure node rather than a traditional Windows PC.