What is Dell Pro Max with NVIDIA GB10 and how is it different from a regular workstation?

Dell Pro Max with GB10 is a deskside AI supercomputer powered by the NVIDIA Grace Blackwell GB10 Superchip, featuring a 20-core Arm CPU, Blackwell GPU, and 128 GB unified memory. Unlike traditional workstations with separate CPU and GPU memory, GB10's unified architecture eliminates bottlenecks for large models and multimodal workloads. It ships with DGX OS and NVIDIA AI Enterprise, making it a true AI development platform, not just a powerful PC.

How does Copilots.in extend NVIDIA DGX Spark on GB10?

DGX Spark provides the foundation (quickstarts for NIM, NeMo, RAG, CUDA-X), while Copilots Lab adds hardware-aware multi-agent orchestration, no-code agent builder, model registry, monitoring dashboards, and DPDP compliance tools. Think of DGX Spark as the engine and Copilots Lab as the complete vehicle with navigation, safety features, and support.

Which AI workloads can I run on day one?

Out of the box, you can deploy: Local LLM chat (Open WebUI + Ollama), RAG applications using NIM and vector databases, Fine-tuning with NeMo and LLaMA Factory, Video analytics with VSS agents, Data science pipelines with cuDF, cuML, cuGraph, and Multi-agent chatbots and copilots. All blueprints are pre-tested on GB10 and ready to run within your first hour.

How many parameters can a single GB10 support, and when do I need two nodes?

A single Dell Pro Max with GB10 can handle models up to approximately 200B parameters (like Llama 3 405B with quantization or optimization). By connecting two GB10 units via NVLink-C2C, you can work with models up to ~405B parameters at full precision. You'll want dual nodes when working with frontier models, large multimodal systems, or when serving multiple concurrent users on large models.

How does GB10 reduce my cloud GPU spending?

For continuous AI workloads (8+ hours/day), owning a GB10 typically costs 60–90% less over 3 years than renting equivalent cloud GPUs. You avoid hourly GPU compute charges (₹200–500/hour on AWS/Azure/GCP), data egress fees (₹10–13K per TB), storage costs for datasets and models, and platform service fees (SageMaker, Vertex AI, etc.). Most teams see payback within 12–24 months, after which the GB10 is pure profit.

Is GB10 suitable for regulated industries in India (BFSI, healthcare, government)?

Yes. GB10 is ideal for regulated sectors because all data processing happens on-prem, meeting data residency requirements. DPDP Act compliance tools are built into Copilots Lab (audit trails, encryption, RBAC). No vendor lock-in or dependency on foreign cloud providers. Proven in similar environments: Nasdaq (finance), NYU (clinical research), government labs. You maintain full control and can demonstrate compliance during audits.

How do I scale from GB10 to larger Dell AI Factory or DGX Cloud environments?

Workloads you build on GB10 using DGX Spark and NVIDIA AI Enterprise are container-based and portable. When you need more capacity: Scale to 2–4 GB10 nodes in your office, move containers to Dell PowerEdge AI servers or DGX systems in your data center, or burst to DGX Cloud for peak workloads. All use the same NVIDIA stack, so there's minimal refactoring. GB10 becomes your development sandbox while production runs at scale.

What support do I receive with Copilots GB10 Lab?

Your package includes Dell hardware warranty (1 year standard, extendable), NVIDIA DGX Spark documentation and software updates, Copilots.in success program with 24/7 technical support, architecture consulting, quarterly optimization reviews, and training (50–150 hours depending on tier), plus access to community (2,500+ users), marketplace, and templates. You're not buying hardware alone—you're joining a complete AI ecosystem.

Can we keep using cloud for some workloads while using GB10 for others?

Absolutely. Many teams use GB10 as their development and low-latency inference node while keeping large-scale batch training or public-facing APIs in the cloud. DGX Spark and Copilots Lab support hybrid architectures, so you can develop and validate models on GB10, push containers to cloud for scale, and serve latency-sensitive or private workloads from GB10. This gives you flexibility without cloud lock-in.

What skills does my team need to operate GB10 + Copilots Lab?

Basic requirements include familiarity with Python, Docker, and common ML frameworks (PyTorch, TensorFlow, Hugging Face), plus understanding of API integration and data pipelines. DGX Spark quickstarts and Copilots Lab's no-code builder reduce the need for deep MLOps or infrastructure expertise. Most data-savvy teams become productive within 2–4 weeks, and we provide training to accelerate onboarding.

How does GB10 compare to building a custom GPU workstation?

GB10 offers unified memory architecture (128 GB shared) vs separate CPU/GPU memory pools, DGX OS and NVIDIA AI Enterprise pre-installed and supported, optimized cooling and power efficiency in a compact form factor, official Dell + NVIDIA warranty and support, and portable workloads compatible with Dell AI Factory and DGX Cloud. A DIY workstation may cost less upfront but lacks the integrated software stack, support, and upgrade path GB10 provides.

What happens when I need more compute power than 200B parameters?

You have three options: Bond two GB10 units to support ~405B parameters, add Dell PowerEdge AI servers or DGX systems to your on-prem setup, or burst to DGX Cloud for peak capacity. All use the same NVIDIA AI Enterprise stack, so your code and containers migrate seamlessly. Copilots.in helps you size and architect the right configuration.

Can GB10 run non-LLM workloads like data science, optimization, and simulations?

Yes. CUDA-X libraries pre-installed on GB10 support data science with cuDF and cuML (accelerated pandas, scikit-learn), graph analytics with cuGraph (network analysis, fraud detection, recommendations), optimization with cuOpt (routing, scheduling, supply chain), genomics with Parabricks (variant calling, sequence alignment), physics with Modulus (physics-informed neural networks), and quantum simulation with cuQuantum. GB10 is a true AI + HPC platform, not just for LLMs.

What's the typical ROI timeline, and when do we break even?

Most organizations see 12–24 months as the break-even point when comparing to equivalent cloud GPU usage, 18–36 months when they start generating profit as cloud costs avoided exceed GB10 investment, and immediate ROI through faster iteration (no queue times), better data control, and compliance benefits. Teams with heavy daily GPU usage or data-sensitivity requirements often justify the investment in under 12 months.

Why not just use AWS/Azure/GCP? Isn't cloud more flexible?

Cloud is excellent for bursting to massive scale for short periods, teams with unpredictable or seasonal workloads, and organizations without on-prem infrastructure. GB10 is better when you run AI daily (8+ hours/day) for continuous development and inference, need sub-50ms latency for real-time applications, must keep data on-prem for compliance or IP protection, and want predictable costs and no vendor lock-in. Many teams use both: GB10 for development and private workloads, cloud for scale and public APIs. Copilots.in supports hybrid architectures.

AI Sales Agent on GB10: 64% Open Rate

The Problem: Cloud LLM Costs Are Killing Sales Teams

Every sales team faces the same challenge: how do you scale personalized outreach without hiring an army of SDRs (Sales Development Representatives)? Traditional cold email campaigns deliver abysmal results—15% open rates if you're lucky, single-digit click-through rates, and constant spam folder battles. Meanwhile, hiring SDRs costs $50,000-$80,000 per year per person, and they can only handle 50-100 personalized emails daily.

Enter AI sales agents powered by large language models (LLMs). The promise is incredible: hyper-personalized emails at scale, LinkedIn profile research, industry context analysis, and natural-sounding copy that doesn't trigger spam filters. But there's a catch—cloud-hosted LLMs like GPT-4 cost $10-$30 per million tokens, and a single personalized email campaign can burn through thousands of dollars in API calls. For a sales team sending 10,000 emails per month, cloud LLM costs can easily exceed $5,000-$15,000 monthly.

What if you could run the same AI sales agent locally on your own hardware, with zero ongoing costs, 100% data privacy, and performance that matches or exceeds cloud models? That's exactly what we built using the NVIDIA GB10 workstation powered by Grace Blackwell architecture.

The Solution: Agent Mira on NVIDIA GB10

Agent Mira is an AI-powered sales automation platform that replaces your SDR team by handling the entire outreach workflow: understanding your product offering, building target personas, scraping LinkedIn profiles, finding verified email addresses, generating hyper-personalized emails, tracking engagement metrics, and updating your CRM—all autonomously.

The breakthrough? Running Agent Mira on an NVIDIA GB10 workstation with local LLMs (specifically GPT-4o-S 120 billion parameter model via Ollama) delivers three game-changing advantages:

Zero Ongoing Costs

No per-token API charges. Run unlimited email campaigns without worrying about cloud bills. The GB10 hardware investment pays for itself in 3-6 months compared to cloud LLM costs.

100% Data Privacy

All customer data, LinkedIn profiles, email content, and proprietary sales strategies stay on your device. No data leaves your infrastructure. Critical for regulated industries (healthcare, finance, government) and enterprises with strict data governance policies.

Production-Grade Performance

The 120B parameter GPT-4o-S model running on GB10 delivers latency and quality comparable to cloud GPT-4, with the added benefit of fine-tuning on your specific sales data without exposing it to third parties.

How It Works: The Technical Architecture

The Agent Mira workflow on NVIDIA GB10 consists of seven automated stages, all orchestrated through a no-code agent builder platform that runs entirely on-premise:

1Product & Persona Understanding

The agent ingests your product documentation, value propositions, and target customer profiles. For example, when promoting "AI Lab in a Box" (GB10 workstation), the agent learns that ideal customers are CTOs, AI researchers, and enterprise IT leaders in pharma, finance, and education sectors who need on-premise AI infrastructure.

2LinkedIn Profile Scraping & Research

The workflow loops through your target list, scraping LinkedIn profiles to extract job titles, company information, recent posts, industry focus, and professional interests. This data feeds into the personalization engine. For instance, when targeting Mahesh at a telecom regulatory firm, the agent identifies his focus on policy frameworks and cross-sector AI adoption.

3Data Storage & Context Building

All scraped data is stored in a local SQL database, creating a rich context repository. The agent maps each prospect's industry challenges, recent activities, and potential pain points that your product solves.

4Hyper-Personalized Email Generation

This is where the local GPT-4o-S 120B model shines. Using structured prompts, the agent generates multi-paragraph emails with:

Industry-specific context: References to the prospect's sector challenges (e.g., telecom regulatory reforms)
Personalized value propositions: How your product solves their specific problems
Natural conversational tone: Avoids robotic AI language that triggers spam filters
Actionable hooks: Specific chapters, frameworks, or features relevant to their role

5Spam Score Optimization

Before sending, each email is analyzed for spam triggers. The agent achieved 0% spam scores by avoiding promotional language, maintaining natural sentence structure, and including personalized references that prove the email isn't mass-blasted.

6Automated Email Delivery

Emails are packaged in HTML format and sent through your email infrastructure with proper authentication (SPF, DKIM, DMARC). The system handles scheduling, throttling, and deliverability best practices automatically.

7Engagement Tracking & CRM Updates

The agent tracks opens, clicks, and replies, updating the database in real-time. This feedback loop informs follow-up sequences and helps refine future campaigns.

Real-World Results: The "Invisible Enterprises" Book Launch

To validate the system, we used Agent Mira to promote "Invisible Enterprises," a book about 90-day AI adoption frameworks for organizations. The target audience was C-suite executives, AI leaders, and enterprise decision-makers across telecom, pharma, finance, and government sectors.

Here's a real example of the personalization in action. When targeting Mahesh, a telecom regulatory professional, the agent generated an email that:

Email Personalization Breakdown:

Industry Context: Referenced telecom regulatory reforms and the role of private players, ministries, and policymakers
Strategic Positioning: Positioned AI adoption as a strategic imperative for navigating unseen industry dynamics
Specific Value Props: Highlighted book chapters on "mapping trust workflows" and "bridging silos using AI tools cross-sector"—directly relevant to his regulatory role
Actionable Hook: Mentioned the "90-day playbook for leaders" and specific week-by-week frameworks
Natural Tone: Conversational language that reads like a colleague recommendation, not a sales pitch

The result? The book became an Amazon bestseller in 30 days, driven entirely by this AI-powered outreach campaign running on local GB10 hardware.

Cloud vs. Local: The Cost Comparison

Let's break down the economics. For a sales team sending 10,000 personalized emails per month:

Cost Factor	Cloud LLM (GPT-4)	GB10 Local LLM
Monthly API Costs	$5,000 - $15,000	$0
Hardware Investment	$0 upfront	$25,000 - $40,000 (one-time)
Data Privacy	Data sent to third party	100% on-premise
Scalability Costs	Linear increase with volume	Fixed (no marginal cost)
12-Month Total Cost	$60,000 - $180,000	$25,000 - $40,000
ROI Breakeven	N/A	3-6 months

The GB10 investment pays for itself in 3-6 months, and every month after that is pure savings. Over three years, the cost difference is staggering: $720K - $2.16M for cloud vs. $25K-$40K for on-premise.

Why NVIDIA GB10 (Grace Blackwell) Changes Everything

The NVIDIA GB10 workstation, powered by Grace Blackwell architecture, represents a fundamental shift in AI infrastructure. Here's why it's the ideal platform for running Agent Mira and similar agentic AI workflows:

120B Parameter Model Inference

GB10 handles GPT-4o-S 120 billion parameter models with production-grade latency. The demo video shows real-time email generation—no waiting, no throttling, just instant results comparable to cloud GPT-4.

Unified Memory Architecture

Grace CPU and Blackwell GPU share a unified memory pool, eliminating data transfer bottlenecks. This is critical for agentic workflows that constantly move data between scraping, database storage, LLM inference, and email packaging stages.

Energy Efficiency

GB10 delivers 25x better energy efficiency than previous-generation GPU workstations, making 24/7 agent operations economically viable. Your AI sales agent can run continuously without astronomical power bills.

Workbench Application Ecosystem

GB10 ships with a no-code agent builder platform where you can visually design workflows, connect data sources, configure LLM prompts, and deploy production agents—no PhD in machine learning required.

Building Your Own AI Sales Agent: Getting Started

Ready to replicate these results? Here's the roadmap to deploy your own AI sales agent on NVIDIA GB10:

Acquire GB10 Hardware

Order a Dell Pro Max with GB10 workstation through Copilots.in. We deliver fully configured systems with NVIDIA DGX Spark software stack, Ollama pre-installed, and the agent builder platform ready to use. Deployment time: 90 days from order to production-ready pilots.

Download GPT-4o-S 120B Model

Use Ollama to pull the GPT-4o-S 120 billion parameter model. This open-source model delivers quality comparable to GPT-4 for sales and marketing use cases. The GB10 workbench application handles model management automatically.

Design Your Agent Workflow

Using the no-code agent builder, create a workflow similar to Agent Mira: data scraping → SQL storage → LLM inference → email packaging → delivery → engagement tracking. The platform provides pre-built templates for common sales automation scenarios.

Configure Prompts & Personas

Define your product value propositions, target personas, and email structure prompts. The key to high open rates is hyper-personalization—instruct the LLM to reference LinkedIn activity, industry challenges, and specific pain points your product solves.

Run Test Campaigns

Start with a small test batch (100-500 prospects) to validate email quality, deliverability, and engagement metrics. Refine prompts based on results. The demo video shows this iterative process—switching between cloud and local models to compare performance.

Scale to Production

Once validated, scale to thousands of prospects per month. The GB10 hardware handles this effortlessly—no throttling, no rate limits, no surprise cloud bills. Set up automated follow-up sequences based on engagement signals.

Beyond Sales: Other Agentic AI Use Cases on GB10

While this article focuses on sales automation, the GB10 + local LLM architecture unlocks countless agentic AI applications:

Customer Support Automation

AI agents that handle tier-1 support tickets, route complex issues to humans, and learn from resolution patterns—all without sending customer data to cloud providers.

Market Research Agents

Scrape competitor websites, analyze pricing strategies, track product launches, and generate weekly intelligence reports—automatically.

Content Marketing Automation

Generate blog posts, social media content, and email newsletters tailored to different audience segments, with brand voice consistency enforced by local fine-tuning.

Compliance & Risk Monitoring

Agents that continuously scan internal documents, contracts, and communications for compliance violations, flagging issues before they become legal problems.

The Future: 2026 and Beyond

As mentioned in the demo video, 2026 will bring even more powerful AI-ready hardware. NVIDIA's next-generation architectures will enable 500B+ parameter models running locally on desktop workstations, further closing the gap between cloud and on-premise AI capabilities.

For sales teams, this means the AI SDR replacement trend will accelerate. Companies that adopt local LLM infrastructure now will have a 2-3 year competitive advantage in cost structure, data privacy, and AI customization capabilities.

The question isn't whether AI will replace traditional SDR workflows—it's whether you'll run that AI on expensive cloud infrastructure or own it outright on hardware like the NVIDIA GB10.

Key Takeaways

AI sales agents can replace SDR teams by automating LinkedIn research, email personalization, and engagement tracking—delivering 64% open rates vs. industry average of 15-25%.
Cloud LLM costs are unsustainable for high-volume sales outreach, with monthly bills reaching $5K-$15K for 10,000 emails.
NVIDIA GB10 workstations enable zero-cost local LLM inference with 120B parameter models, achieving cloud-comparable quality with 100% data privacy.
ROI breakeven occurs in 3-6 months, with 3-year savings of $700K-$2M+ compared to cloud infrastructure.
No-code agent builders make it possible to deploy production AI workflows without machine learning expertise.
Hyper-personalization is the key to high engagement—LinkedIn research, industry context, and natural tone eliminate spam flags and drive clicks.

Ready to Build Your AI Sales Agent?

If you're tired of cloud LLM bills eating into your sales budget, or if data privacy regulations prevent you from using third-party AI services, the NVIDIA GB10 + Agent Mira approach offers a proven alternative.

Copilots.in delivers fully configured GB10 labs with the agent builder platform pre-installed, plus assisted use case development to help you replicate the 64% open rate results. From unboxing to production-ready pilots in 90 days.

AI Sales Agent on NVIDIA GB10 Delivers 64% Open Rate—Zero Cloud Costs

Results That Speak for Themselves

Watch the Live Demo: AI Sales Agent in Action