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.
- Listings
- —
- In stock now
- 00
- Ships
- India
In stock right now
See everything in stockFeatured 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.
No featured hardware right now
The catalogue has no products flagged as featured at the moment. You can still browse everything we stock.
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.
- Shop AI Accelerators
AI Accelerators
00Dedicated inference and training accelerators.
- Shop GPUs
GPUs
00High-VRAM graphics cards for local model inference.
- Shop Workstations
Workstations
00GPU-equipped machines for local AI.
- Shop Edge AI Devices
Edge AI Devices
00Compact boards and devices for on-device inference.
- Shop AI Mini PCs
AI Mini PCs
00Small-form-factor machines with unified memory.
- Shop Memory & Storage
Memory & Storage
00High-capacity memory and fast NVMe storage.
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.
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.
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.
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.
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.
Lowest entry point
Live pricing is unavailable right now.
Every listing states who sells it — swaymAI or a partner merchant.
Shipping, returns and support
- ShippingDelivery timelines and what ships where.
- ReturnsHow returns work for in-house orders.
- SupportTalk to us before you commit to a build.
Stay in the loop
New hardware, when it lands
We will only send you something when there is a genuinely new listing or a guide worth reading.
Email delivery may be disabled on this deployment — we will always tell you what the API actually did.