By LLM Hardware team · Methodology ·
Checked 2026-10-13. Author: LLM Hardware team. This site does not store or show Amazon prices. Check current price on Amazon.
Key fact: devin.ai, read 2 October 2026, says Devin runs in the cloud or on your machine, can use its own VMs, and that you can iterate in Desktop or the CLI. Parallel cloud agents are part of that description.
How we got it: Primary homepage fetch. A cognition.ai/blog/devin-desktop URL returned 404 the same day.
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Key fact: That homepage did not use the words Windsurf or Cascade. Older search names are not confirmed as current product labels here.
How we got it: Absence on the fetched page. If your installed app still says Cascade, verify it in the client rather than from this guide.
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Key fact: One local coding loop at 27–32B Q4 fits a 24 GB card (18.2–21.2 GB). A fleet of resident models wants 48–128 GB or more than one GPU.
How we got it: VRAM formula. N full copies cost N times the weights unless they share one server.
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Methodology — parameter math, quantization bytes, and the source list.
Devin’s site, fetched 2 October 2026, describes an autonomous software engineer that runs in the cloud or on your machine, tests in its own browser, and can split work across parallel cloud agents. The page says you delegate to Devin Cloud, iterate in Desktop or the CLI, or mention @Devin in Slack, Teams, and issue trackers. Sessions run in their own VMs. This guide does not quote a price. The page sends you to “Get started” and “Book a demo,” and the numbers that do appear there (a cost-efficiency claim, model names in a changelog) are marketing lines, not a basis for a hardware recommendation.
Devin’s parallelism is several cloud VMs. Your parallelism is however many models fit in memory, or however many clients share one model. The second design is the one that works on hardware a person can buy.
A single Cascade-like loop — one agent, one repo, one model — is a 24 GB problem. A fleet of specialists that each load their own 32B is a fantasy on that card. See also Cursor Cloud Agents, which is the other commercial “many VMs” product.
| Shape | Weights at Q4 | Hardware |
|---|---|---|
| One coding loop | 27B = 18.2 GB, or 32B = 21.2 GB | 24 GB GPU or 36 GB unified |
| Many clients, one server | Same 21.2 GB, plus KV cache per client | 48 GB if the chats are long |
| Two full models resident | Roughly double. Two 32B Q4 loads are about 42 GB before cache. | 64–128 GB, or two GPUs. The calculator is per model; add them yourself. |
K2-Horizon-MoVA-36B-A4B is 36B total and about 4B active. Q4 of the full expert set is on the order of 36 × 0.6 + 2 = 23.6 GB. It can feel fast and still fill a 24 GB card. Active parameters are not a discount on memory.
If you truly want more than one heavy model hot, the verified 128 GB machine is the GMKtec EVO-X2, ASIN B0F53MLYQ6,649.99 on 1 October 2026. No CUDA. Bandwidth is the Strix Halo class, not a 5090. A single loop does not need it. A 24 GB card, or the 36 GB Studio, is the honest buy for one Devin-like local agent. This guide links no Devin SKU. There isn’t an Amazon listing for the software in this catalog.
GMKtec EVO-X2 128 GB on Amazon
ASIN B0F53MLYQ6. Amazon Associates link. The live price is on that page, not locked in here.
Cloud: Devin is the better pick if you want a managed agent fleet without running servers. Many agents in parallel are cheap in the cloud and expensive in local VRAM.
Local: A local fleet is better when source code must stay on your hardware and a single shared model server is enough for your parallelism.
Product claims on this page carry their source and an “as of” date in the Citeable facts box. Prices are Amazon.com listings on the date given next to each one, not live prices. Reviewed .
A correction or a hardware question goes to the contact page.