The short version
Ask someone shopping for a local AI model what hardware they need, and you usually get a
vague answer: "a decent GPU." That answer is expensive, and it is the reason a lot of
self-hosted AI projects stall between "I want to run this privately" and "it actually runs."
I sell a fully private, self-contained local AI model called Mentary AI. Before anyone
spends money on hardware — or on the license — the useful conversation is not "which model
is smartest." It is "what is the smallest machine that gives you the latency and context
window you actually need." That question is answerable, and this is how I answer it.
First: the constraint is not FLOPs, it is memory bandwidth
People benchmark local models on throughput and then discover the real bottleneck on
first use. Two numbers dominate the experience:
- Weight residency. The model weights have to sit in RAM or VRAM for the whole session. If they do not, the host starts paging and latency stops being a function of the GPU.
- KV cache growth. Every token of conversation you keep in context costs memory that grows with context length and batch size. A context window you never use is memory you paid for and never touch.
The practical consequence: sizing to the weight set plus the context you actually use is
usually smaller than sizing to the vendor's headline spec. Most buyers over-provision,
and the over-provisioned machine is then sitting idle, which is its own cost.
The rough numbers, with the caveat attached
These are order-of-magnitude planning figures for a quantized small-to-mid model, not a
promise about any specific build. Treat them as a starting point you verify on your own
hardware:
| Target | Practical starting point |
|---|---|
| Weight residency | quantized weights in RAM or VRAM, fully resident, no swap |
| Context window you actually need | 8k tokens is often enough; 32k is a luxury until proven otherwise |
| Latency floor | driven by memory bandwidth, so a slower GPU with enough VRAM often beats a faster one that spills |
| Headroom | leave roughly 20-30% free for the KV cache at your real context length |
The last line is the one people skip. They size the box to the weights, run inference, and
watch latency collapse the moment the conversation gets long — because the cache had
nowhere to go.
Why the hardware question is actually a privacy question
Here is the part that is easy to miss. If your model cannot fit on the hardware you own,
the natural next step is to rent capacity — which means sending the prompt somewhere else.
That is how a "local only" deployment quietly becomes a cloud dependency: not because
anyone decided to, but because the box was too small.
So sizing is not just an ops question. If you have a data-residency requirement —
healthcare, legal, government, finance — the minimum viable machine is a compliance
number. Below it, you are not private, you are just private until you run out of memory.
How Mentary AI fits into that
Mentary AI is sold as one package: complete model weights, an optimized inference engine,
deployment scripts for bare-metal and VMs, a production Docker image with Compose
orchestration, documentation and hardening guides, and 30-day priority deployment
support. The premise is that the package arrives complete — no registry pull at install
time, no runtime entitlement check, no external API calls — so that the only thing you
need in order to be private is hardware you control.
That is also why the product page leads with a demo instead of a spec sheet. There is a
working interactive tool on the page: you type a query, it answers, and the page reports
0 bytes transmitted. You can see the behaviour before you commit to a machine for it.
The honest caveats
- The license is a one-time $299.99 payment. You own the package and the lifetime license; there are no recurring fees. There is also no free tier, so the demo is the only way to evaluate before paying.
- You supply and operate the hardware. That is the point, but it means you are also responsible for backups, updates, and physical security.
- Sizing advice is environment-specific. The deployment support included with the license is where you take your own numbers and get them checked against your own workload.
Buying
One-time $299.99 — full package, lifetime license, no recurring fees.
- PayPal: send to redman1309@proton.me — include a note with what you are buying and I confirm it in the inbox.
- Crypto: 0x078fE079A9440631Bc6AD722D7143dC382b4ea4f on Base (ETH / USDC / USDT).
- Western Union / MoneyGram also accepted.
Product page with the live demo: https://reggiebro.github.io/WorkflowAlpha.ai/mentary-ai/
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