On May 18, 2026, Publickey reported that Dell Announced "Dell Deskside Agentic AI," Desktop PCs for Running AI Agents Locally Equipped with NVIDIA GB10 and GB300. Dell unveiled AI workstations designed to sit deskside as a new product lineup, formally commercializing configurations capable of running 70B to 200B parameter class LLMs locally powered by NVIDIA GB10 / GB300. This is a symbolic milestone marking how AI inference infrastructure has descended "from the server room to the deskside."
For teams managing AI operational foundations for mid-sized enterprises through custom development, this means projects deploying "one unit per conference room or department" are quickly becoming viable. Discussions regarding "leveraging AI without sending data outside the company" previously covered in Ubuntu Local AI Custom Development and Local-First AI Inference Custom Engagements can now be applied to mid-sized enterprises that lack dedicated server rooms. In this article, we outline how to design deskside AI hardware selection and agent operations.
Why "Deskside Agentic AI" triggers explosive custom development demand among mid-sized enterprises
| Structure | Cloud LLM Usage | Server-room on-premise LLM | Dell Deskside Agentic AI |
|---|---|---|---|
| Installation location | Not required | Server room / data center | Deskside within office |
| Initial investment | 0 | 15 million yen+ | 2 million to 6 million yen |
| Facility requirements | Not required | Air conditioning / electrical power works | Standard wall outlet |
| Data exfiltration | Available | None | None |
| Model size | Optional | Optional | Up to 200B (quantized) |
| Target user base | Company-wide | Company-wide | Department / conference room basis |
| Assets remaining upon contract termination | 0 | Server hardware assets | PC assets |
In other words, the biggest constraint for mid-sized enterprises—"not having a server room yet being unable to let AI data leave the company"—can potentially be resolved on a scale of several million yen through Deskside AI + outsourced operations.
Three structural shifts driven by Deskside Agentic AI
Structure 1: From "on-premise AI requiring a server room" to "in-office AI"
Until now, on-premise LLMs required server rooms, air conditioning, and electrical facility work. Deskside AI runs on in-office installation and standard power supplies, drastically lowering the installation hurdles.
Structure 2: From "one shared unit company-wide" to "by department or conference room"
While server-room large-scale LLM infrastructure was predominantly run as "one shared unit for the entire company," Deskside AI enables distributed deployment by department, project, or conference room. This delivers dedicated AI tailored to specific business tasks.
Structure 3: From "IT department assets" to "business unit assets"
Cases where business units purchase Deskside AI directly will increase. An operational approach where the IT team only finds out after the fact leads to a breakdown in governance. In custom development and operations, an architecture is needed to operate AI hardware purchased by business units according to IT team standards.
Five phases for designing Deskside AI hardware selection and agent operations
Phase 1: Operational use-case inventory and hardware architecture (2–3 weeks)
We take inventory of "which departments," "for which workflows," and "with what model sizes" the client wants to utilize Deskside AI. We break these down into departmental use cases, such as "meeting room minutes summarization," "sales proposal drafting," and "legal contract reviews."
Phase 2: Hardware selection and procurement (2–4 weeks)
| Application | Recommended configuration | Estimated price |
|---|---|---|
| Small department (assuming 10 people / 7B–13B) | Dell Deskside GB10 configuration | From ¥2,000,000 |
| Medium department (assuming 30 people / 30B–70B) | Dell Deskside GB300 configuration | From ¥4,000,000 |
| Large site (assuming 100 people / 70B–200B) | Multiple Dell Deskside GB300 units | From ¥6,000,000 |
To prevent over-provisioning, we incorporate a roadmap of "minimal initial configuration → evaluation for expansion after 6 months" into the contract.
Phase 3: Hardware setup and IT governance integration (3–4 weeks)
- Ubuntu / RHEL setup
- Internal IdP (Entra ID / Okta) integration
- File sharing (SMB / NFS) integration
- Audit logging + prompt retention
- Asset management (registration in the IT team CMDB)
Phase 4: Departmental agent design and deployment (4–6 weeks)
- Meeting minutes summarization agent (for conference rooms)
- Proposal drafting agent (for sales)
- Contract review agent (for legal)
- Internal knowledge search agent (for all departments)
Each agent is designed to be implemented as an MCP server and called from tools like Claude Desktop and Cursor.
Phase 5: Monthly operational reviews (ongoing)
Every month, we report hardware utilization, model usage rates, agent adoption status by department, business operational impact (time saved and quality improvements), and model update proposals to executive leadership.
Standard technology stack set for custom development
| Layer | Recommended technology | Alternative |
|---|---|---|
| Hardware | Dell Deskside Agentic AI（GB10 / GB300） | NVIDIA DGX Spark / Lambda |
| OS | Ubuntu 26.04 LTS | RHEL |
| LLM runtime | vLLM / ollama / llama.cpp | Triton |
| Model | Llama 3.x / Qwen 3 / DeepSeek V4-Flash | Gemma / Mistral |
| Agent foundation | MCP server + Claude Desktop / Cursor | LangChain |
| Asset management | Internal CMDB / Intune / Jamf | Manual |
| Auditing | OpenTelemetry + Loki | Splunk |
This is positioned as the "hardware-level implementation" of client-side AI inference discussed in Local-First AI Inference for Clients, combined with the OS-layer Local AI standardization covered in Ubuntu Local AI for Clients.
Which projects need this and which do not
| Projects requiring this | Projects not requiring this |
|---|---|
| Data export prohibited / no server room | Server room available / existing on-premise AI infrastructure |
| AI usage needs at the departmental level | Company-wide shared AI only |
| High volume of confidential documents (minutes, proposals, contracts, etc.) | Publicly available information only |
| Already spending over ¥200,000 monthly on cloud LLMs | Infrequent LLM usage |
| Business units actively considering purchasing AI hardware | Centralized under the IT team |
Six clauses to include in client contracts
| Clause | Details | What the client should verify |
|---|---|---|
| Target hardware | Dell Deskside GB10 / GB300 | Configuration rationale |
| Installation location | Inside office / conference room / department | Physical security |
| Asset ownership | Client asset / custom leased | Contract continuity upon termination |
| Model update frequency | Monthly / quarterly | Operational impact |
| Audit log retention | 12 / 36 months | Compliance |
| Handover upon contract termination | Hardware + models + IaC | In-house operational feasibility |
Client-side ROI estimate (assuming 5 departments / ¥600,000 monthly cloud LLM spend / high volume of confidential documents)
| Item | Continuing cloud LLMs | Adopting Dell Deskside AI | Difference |
|---|---|---|---|
| Monthly LLM usage fees | ¥600,000 | ¥80,000 (electricity + maintenance) | -¥520,000 |
| Data breach risk (annual estimate) | 15 million yen | 1,000,000 yen | -¥14,000,000 |
| Operational impact from department-dedicated AI | Limited | +25% via departmental optimization | +25% |
| Audit and compliance workload | 200h/year | 50 hours/year | -150h |
| Annual benefit (excluding hardware investment) | — | — | Approx. 22 million yen |
| Dell Deskside hardware investment (5-year depreciation) | 0 | ¥2,500,000/year | -¥2,500,000 |
| Net annual benefit | — | — | Approximately ¥19,500,000 |
Even when factoring in the hardware investment, the net annual benefit reaches the scale of ¥19,000,000, leaving ample room for return on investment even after adding the cost of building the operational structure. However, because this relies heavily on baseline cloud LLM spend and confidential document volume, please validate these numbers using your company's actual metrics.
Five common pitfalls
Pitfall 1: Business units purchase independently without IT awareness
Deskside AI falls into a price range purchasable within business unit budgets. To prevent independent purchases by business units getting blocked by IT audits, establish company-wide procurement guidelines first.
Pitfall 2: Buying the maximum model size only to leave it idle
Buying hardware scaled for 200B models "for the future" leads to a 70% idle rate. Build an approach into the contract where you start with 13B–30B and expand as adoption grows.
Pitfall 3: Postponing physical security
Because the machine sits at the deskside, physical access is easy. Make physical locks, Kensington locks, and security cameras explicit requirements.
Pitfall 4: Storing audit logs only locally on the hardware
Hardware failures bring the risk of losing audit logs. Mandate forwarding logs to the internal centralized logging infrastructure.
Pitfall 5: Frontline disappointment that it doesn't work like ChatGPT
On-premise LLMs can be slower or less capable than the latest cloud models. From the beginning, design for helping frontline teams achieve quick wins through expectation management and use-case specialization.
90-day action plan
| Week | Action |
|---|---|
| Week 1〜3 | Use-case inventory and hardware architecture |
| Week 4〜7 | Hardware selection and procurement |
| Week 8〜11 | Setup and IT governance integration |
| Week 12〜13 | Departmental agent design and start of rollout |
Conclusion — The era when mid-sized enterprises without server rooms can own their AI
Dell Deskside Agentic AI is an offering that replaces "on-premises AI conditioned on server rooms" with "in-office Deskside AI." For those overseeing AI infrastructure for mid-market enterprises, moving beyond the binary choice of "cloud or server-room on-premise" makes "Deskside AI plus managed operations for clients" a third realistic solution.
Whether your challenges are "being unable to send data offsite while lacking a server room," "wanting departmental AI units," or "needing to operate AI hardware purchased by business units according to IT team standards," the implementation approach depends on hardware count, target departments, and required service levels. We will provide a tailored estimate after reviewing your requirements. Please feel free to reach out via our inquiry form.
