Local AI labs that scale to the cloud
We put NVIDIA DGX Spark (desktop AI supercomputer) in your operation — a working laboratory, not a cluster RFP (Request for Proposal). Your teams run local data analytics and model training on site. Agentic Partner then scales the same pipeline to NVIDIA-accelerated cloud production.
01
What you get
On-prem (on-premises) assistants + labs for analytics, live vision, and training
02
Where we start
Mining and geology, then industry safety, biomedicine, SMEs (small and medium-sized enterprises)
03
How we run it
NVIDIA DGX Spark on site, then NVIDIA cloud when it is proven
04
How we work
One KPI (key performance indicator), weeks not budget cycles, then a production handover
Company
Who we are and what we offer
A local NVIDIA laboratory you can use — then a clear path to production in the cloud.
Mission
- Accelerate local AI adoption for people and companies.
- Put practical NVIDIA compute in reach of customers who cannot buy a cluster.
- Prove a use case on local data, then take it to production in the cloud.
Product
- NVIDIA-powered personal AI assistants for homes and companies.
- Labs: local data analytics, live vision, and model training on DGX Spark.
- Scale-up with Agentic Partner onto NVIDIA-accelerated cloud.
Who it's for
- Households and businesses that need private, on-prem AI.
- Mining and geology operators, juniors, and service firms.
- Industrial safety, biomedicine, and other data-sensitive industries.
Differentiators
- A working DGX Spark lab you can use — not a slide.
- Senior domain team versus generic AI startups.
- Hardware most customers cannot afford, offered as a lab they can use.
Labs on local data — then scale to the cloud
The offer is not a cluster RFP. Customers bring cameras, files, and site data. We run live vision, agents, analytics, and training on DGX Spark. Agentic Partner then takes the winning pipeline to NVIDIA cloud production.
01 Lab
Stand up DGX Spark with the NVIDIA AI stack. Connect cameras and load local data under NDA (non-disclosure agreement): geology, plant, video, documents. Define one KPI.
02 Analytics
Local data analytics on-prem: RAG (retrieval-augmented generation) over private files, GIS (geographic information system) and sensor fusion, dashboards and agent workflows that never leave the site in the learning phase.
03 Live systems
Real-time vision and agents on the box. PPE (personal protective equipment) and fire/smoke on many cameras at once, tracking, evidence clips, local VLM (vision-language model) verification, a natural-language agent, and phone alerts — all on-device. Example: 20 concurrent 1080p streams on a single Spark.
04 Training
Fine-tune and evaluate models on DGX Spark (vision, language, tabular). Geologists, safety leads, or operators sit with the model. Go / no-go in weeks.
05 Cloud scale
Package the winner. Agentic Partner deploys to NVIDIA-accelerated cloud or on-prem DGX. Same stack, production SLOs (service level objectives), handover and ops.
Market
Why mining and geology come first
Data-heavy, private, often on-site. Energy transition and AI data centers are copper-intensive. The gap is who can run a serious lab on real data without cluster CAPEX (capital expenditure).
On-prem
Private geology, plant, and safety data stays on site in the lab phase
Privacy
Data-heavy
Geology, geochemistry, geophysics, core photos, and sensors
Operations
Copper
Energy transition and AI data centers are both copper-intensive
Demand
No cluster
A data-center GPU (graphics processing unit) is the wrong first buy for an experiment
CAPEX
Mid-tier
Juniors and service firms that cannot buy a GPU cluster
Access
Same stack
Prove on Spark, then promote the winner to NVIDIA cloud
Path
A copper squeeze — and a lab gap
Operators already spend capital on pits, plants, and license to operate. Energy transition and AI data centers both pull on copper. The gap is who can run a serious lab on real data without cluster CAPEX.
Durable demand
Mine productivity is an operations issue, not a niche IT (information technology) project.
Capital already committed
Pits, plants, and social license come first. A GPU cluster is the wrong first experiment.
Cloud is not always first
Metered GPUs are fast, but cost and data-residency risk stall after the pilot invoice.
Exploration and mid-tier
Juniors, mid-tier operators, and service firms that cannot buy a GPU cluster still need a lab.
The category is proven
Prospectivity mapping, core intelligence, and mine autonomy are already live AI workloads.
Why this is an AI market
- World-class operations already run data-heavy workflows: prospectivity, grade control, plant, and safety.
- Geological surveys and operators have shown the category: prospectivity, core intelligence, mine autonomy.
- Most of the market still cannot stand up a data-center GPU. That is the offer: a DGX Spark lab you can use now, then a cloud path you can justify with evidence.
Problem we solve
CAPEX is why most AI projects never start
Operators already spend capital on pits, plants, and social license. A data-center GPU cluster is the wrong first buy. Our product is the missing first step.
GPU cluster first
US$100k–millions
Board CAPEX, cooling, staff. Right for production — wrong as the first experiment.
Cloud GPU only
Low entry, high burn
Fast, but metered cost and data-residency risk. Easy to stall after the pilot invoice.
Laptops / nothing
False economy
Geology and vision models do not fit. Teams stay in PowerPoint. The project never starts.
Our offer: Spark lab + cloud
~US$4–5k unit to start
Local analytics and training on DGX Spark. Agentic Partner scales the proven pipeline to NVIDIA cloud.
NVIDIA technology
DGX Spark on site. NVIDIA cloud when you are ready.
We operate DGX Spark as the customer laboratory. You prove the use case on your data. We take the validated workload to production scale.
GB10
GB10 (Grace Blackwell 10) Superchip
1 PFLOP
Peak AI compute (one petaflop; FP4, 4-bit floating point)
128 GB
128 GB (gigabytes) of coherent unified memory
~70B / ~200B
Fine-tune / local inference class
240 W · 1.2 kg
Desk lab, not a data center
DGX OS + stack
DGX OS (operating system); same path as DGX Cloud
Today on Spark
Customer labs: local data analytics, fine-tuning, agents, and vision. Sensitive mine, geology, and clinical data stay on site.
Then on NVIDIA cloud
Agentic Partner promotes the winning container and model to DGX Cloud or other NVIDIA-accelerated infrastructure for production.
What you walk away with
A go/no-go on one KPI, a trained or validated model, and a production plan. Agentic Partner stays for handover and cloud scale-up.
Next step
Start a lab. Prove one use case. Scale what works to the cloud.
You do not need to buy a GPU cluster to start. Agentic Partner puts NVIDIA DGX Spark in front of your miners, geologists, operators, or clinicians; runs labs on your local data; trains models; and walks with you into NVIDIA cloud production.
How to start with us
- 01Pick one KPI: a geology, safety, plant, or documents use case.
- 02Bring data under NDA. We stand up the DGX Spark lab on site or in our facility.
- 03Run analytics and training for two to six weeks. Go / no-go on evidence.
- 04If it works, Agentic Partner scales the same pipeline to NVIDIA cloud production.
Why clients choose us
- A physical DGX Spark lab you can sit in, not a brochure.
- Senior domain team that can work with geology, mine planning, safety, and IT.
- Your data stays local during the risky learning phase.
- Clear commercial path: lab → evidence → cloud production, with us on the handover.
DGX Spark laboratories and cloud scale-up
Start a lab with us
Tell us the KPI, the data, and the site. We will stand up DGX Spark, run the lab, and walk the path to NVIDIA cloud production.
- Emailangel@agenticpartner.io
- Mobile+51963750749