Every AI tool your team adopts makes demands on their hardware. Some need RAM. Some need CPU. Some need both. Most companies find out the hard way — we help you find out before it matters.
Book a Free AuditYour engineers want AI pair-programming. These tools run language servers, index entire codebases, and stream completions in real time. Running 4–8 Claude Code agents in parallel? That's 8 Node.js processes plus whatever they're building and testing.
Want to run AI without sending data to the cloud? Local inference is the most hardware-hungry AI workload. A 7B parameter model needs 4–8 GB of RAM just for the model. A 70B model? You need a Pro or Max chip.
Designers are already using AI for image generation, background removal, auto-layout, and style transfer. These features run locally when possible for speed, falling back to cloud. Your design team's M1 with 8 GB is going to choke.
Real-time transcription, AI summaries, smart search across docs. These seem lightweight but they stack — Zoom AI + Notion AI + Slack AI + a browser with 40 tabs. Each one eating RAM in the background, all day long.
Writing tools, smart replies, notification summaries, image generation — these are coming to every Mac. But they require macOS 15+ and Apple Silicon. Any Intel Macs in your fleet? They're locked out entirely.
If your team trains models, processes datasets, or runs notebooks with large dataframes, they need serious unified memory. Apple's M-series is actually great for this — but only with enough RAM. A 16 GB machine tops out fast.
| Tier | Chip | RAM | Storage | AI Capability |
|---|---|---|---|---|
|
AI Ready
Full AI toolkit, no compromises
|
M3 Pro / M4 Pro+ | 24–96 GB | 1 TB+ | Local LLMs, multi-agent coding, ML training, everything |
|
AI Capable
Cloud AI tools work great, local is limited
|
M1 / M2 / M3 | 16 GB | 512 GB | Copilot, Claude, Zoom AI, Notion AI — the cloud-based stack |
|
AI Limited
Falling behind, upgrade needed
|
Intel / M1 8 GB | 8 GB | 256 GB | Browser-based AI only. No Apple Intelligence. Sluggish with AI + normal workload |
When company laptops can't run AI tools well, employees use personal devices and accounts. Your data ends up in tools you don't control, with no audit trail.
An engineer waiting 30 seconds for every AI completion — or switching to Chrome because their IDE is too slow — loses hours per week. Multiply by your headcount.
Paying $20/seat/month for Copilot on machines that can't run it properly? That's money on fire. We see this constantly — AI licenses on hardware that bottlenecks them.
Most startups have no approved AI tool list, no data handling guidelines, and no idea what's being pasted into ChatGPT. That's a compliance problem waiting to happen.
Without a readiness plan, you end up panic-buying laptops one at a time at full price. A fleet strategy saves 15–25% vs. ad-hoc replacement.
Engineers expect to use modern AI tools. "We'll upgrade your laptop eventually" is not a retention strategy. Top talent goes where the tools are.
15 min call. We learn your team size, roles, and what AI tools you're using or want to use.
Share your device list (or we'll help you build one). We map every machine to our readiness tiers.
You get a one-page report: what's ready, what's at risk, what to upgrade first, and estimated costs.
Optional: a phased upgrade plan aligned to your hiring and budget. No pressure, just a plan.
Book a free AI Readiness Audit. We'll assess your fleet, map it to your AI plans, and give you a clear scorecard — no commitment, no pitch.
Book Your Free Audit