Choose AI hardware based on real workloads, not marketing claims.
JTech helps businesses, founders, builders, and technical teams understand GPUs, NPUs, TPUs, RAM, VRAM, AI PCs, local AI, edge AI, and cloud options through the lens of actual AI product requirements.
Who this is for
- Small businesses exploring which AI tools require dedicated hardware
- Founders sizing infrastructure requirements for a new AI SaaS launch
- AI learners and developers choosing their next workstation or laptop config
- Technical teams comparing cloud API hosting costs vs local server ownership
- System builders seeking verification of CUDA/VRAM needs for local models
- Teams confused by Copilot+ laptop marketing metrics and NPU TOPS specifications
- Robotics and physical AI builders planning edge computer setups
Problems JTech solves
GPU vs NPU vs TPU Confusions
Misunderstanding the structural difference between training accelerators, local inference chips, and general CPUs.
Overbuying & Underpowering
Wasting thousands on high-end consumer GPUs with insufficient VRAM context sizes, or purchasing underpowered hardware that halts operations.
Cloud Cost Bill Shocks
Deploying heavy API inference models in the cloud when a local workstation or dedicated edge node could save substantial monthly fees.
Model Size & VRAM Mismatch
Not knowing how many gigabytes of memory are required to fit a quantized 8B, 34B, or 70B parameter LLM.
What is included
Example Consultation Focus Areas
Workstation & Laptop Advice
Personalized hardware component guidance (VRAM, RAM bandwidth, CPU lanes) for developers and individual AI learners.
Local AI Setup Planning
System layout mapping to run models locally inside your office for absolute privacy, low latency, and zero per-token cloud costs.
AI Product Infrastructure
High-level cloud sizing (TPUs vs GPU clusters), network bandwidth plans, and redundancy layouts for SaaS systems.
Edge AI / Robotics Sizing
Scoping computer modules (e.g. Jetson kits), sensor interfaces, power budgets, and SLAM computing guidelines.
How JTech delivers from start to launch
Discover
Identify target AI models, token throughputs, and user scale.
Plan
Sift local data compliance requirements and latency constraints.
Design
Map hardware architectures (GPU VRAM counts, RAM speeds).
Build
Draft clean hardware profile recommendations and specs.
Secure
Audit physical access controls and offline fail-safes.
Launch
Deliver recommendation report and assist with procurement lists.
Improve
Re-audit specs as newer, more efficient model architectures drop.
Frequently Asked Questions
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Need help choosing AI hardware?
Share what you want to run, your budget, your privacy needs, and your current setup. JTech will help you understand the best hardware path.