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swaymAI

Local-first AI hardware

Run frontier AI modelson hardware you own.

Accelerators, workstations and memory picked for local inference — with the specifications that actually matter listed in the open, so you can compare a 48 GB accelerator with a 128 GB workstation and know what you are buying.

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ZOTAC Gaming GeForce RTX 5060 Twin Edge 8GB Graphics Card - Black | 8 GB | DLSS4 | GDDR7 | PCIE 5.0 | SFF Ready | 3840 CUDA Cores | ZT-B50600E-10M product image

ZOTAC · GPUs

ZOTAC Gaming GeForce RTX 5060 Twin Edge 8GB Graphics Card - Black | 8 GB | DLSS4 | GDDR7 | PCIE 5.0 | SFF Ready | 3840 CUDA Cores | ZT-B50600E-10M

Inference-tuned
Live stock
Specs in the open

In stock right now

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Featured build-outs

Hardware worth building around

A rotating selection from the catalogue. Availability and pricing come straight from live inventory — nothing here is a placeholder number.

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Categories

Shop by what you are building

Every category links to a filtered view — filters and page numbers live in the URL, so a result set is easy to share with your team.

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Memory first

The only number that decides what runs

Model weights have to fit in memory before anything else matters. Buy for the model and the context window you actually need, then check bandwidth, power and cooling around it.

These bands are rounded estimates. Quantisation, context length and batch size all move them — always leave headroom.

Read the memory guideAccelerator classes

Usable memory → model size

4-bit quantised

  • 8 GB7B–8B quantisedSmall assistants, embeddings
  • 16 GB13B–14B quantisedComfortable single-GPU work
  • 24 GB30B–34B quantisedPractical floor for that class
  • 48 GB70B quantisedOne accelerator, one large model
  • 80 GB+70B at 8-bitMulti-model serving

Bandwidth drives tokens per second; capacity decides whether a model fits at all.

Learn

Read the arithmetic before you buy

Every listing here advertises a number. The guide explains which numbers decide whether a model fits, and the posts run that arithmetic on specific models — including the ones where the API is the cheaper answer.

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Buying guide

Updated 01 Nov 2026

4 chapters · memory, class, power, shortlist

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Latest writing

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Prices, tokens per second, break-even

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Why local AI

Owning the machine changes the economics

Hosted inference is convenient until your workload becomes steady. At that point the trade-offs flip toward hardware — in privacy, in cost and in control.

About swaymAI

  • Your prompts stay on your machine

    Inference runs locally, so sensitive data never leaves your network. No third-party retention to reason about.

  • Predictable cost per token

    You buy the hardware once. After that, long-running jobs and experimentation have no per-token bill attached.

  • No rate limits or cold starts

    Your throughput is bounded by your own silicon, not by a provider’s quota — useful for batch jobs and agent loops.

  • Run 7B–70B parameter models without a monthly API contract.
  • Keep embeddings, documents and customer data inside your own network.
  • Reuse one machine for inference, fine-tuning experiments and evaluation runs.
  • Upgrade incrementally — add memory or a second accelerator instead of replacing the box.
  • Works offline: ideal for field deployments and air-gapped environments.

Available now

In stock with us

Stock levels shown are the live quantities the API reports for these listings.

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Start here

Not sure which class of hardware you need? Start from the model, not the spec sheet.

The buying guide walks through memory, accelerator class and system balance in the order those decisions actually matter.

Open the buying guideCompare products side by side

Lowest entry point

3060 Battle AX Triple Fan 12GB GDDR6

At merchant

Memory & Storage

Open listing

Every listing states who sells it — swaymAI or a partner merchant.

Shipping, returns and support

Session-based authentication, HttpOnly cookiesLive stock levels on every listingTotals calculated server-side, never in the browserThird-party listings clearly disclosed

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