Written by Jakub Rusinowski · Last updated 2026-07-08 · Prices verified 2026-03-01
A team of 5–25 people generates enough aggregate volume that a single shared GPU server becomes economically interesting fast: at ~4M tokens/day, one RTX 4090 workstation (~$2,590) running a 30B-class model breaks even against GPT-4o pricing in about 5 months. The countervailing force is admin: someone has to own that box. Teams with any infrastructure habit usually win with a shared local server for internal workloads plus per-seat cloud access for frontier tasks; teams with zero ops capacity should stay cloud and negotiate volume terms.
Shared-server local vs per-seat cloud at team scale.
| Dimension | Local | Cloud |
|---|---|---|
| Up-front cost | ~$2,590 (RTX 4090 workstation) — one box serves the team | $0 |
| Monthly cost at 4M tok/day | ~$45 electricity (+$108/mo amortization) | ~$570 (GPT-4o); ~$34 (GPT-4o Mini-class) |
| Per-seat scaling | Marginal user ≈ free until the GPU saturates | Linear: every seat/token billed |
| Admin overhead | One owner: updates, uptime, access control | Key management and spend monitoring only |
| Data governance | Prompts stay on your network | Provider DPA + usage policies per seat |
| Quality ceiling | 30–70B open models | Frontier models |
| Concurrency | vLLM batches dozens of simultaneous users on one card | Effectively unlimited |
Team of ~10 with an internal assistant + RAG: ~4M tokens/day aggregate — 4M tokens/day, RTX 4090 24GB Enthusiast vs GPT-4o (OpenAI), electricity $0.15/kWh. Adjust every input in the interactive calculator on this page.
| Cloud cost / month | $570 (GPT-4o, $2.5/M input + $10/M output) |
| Local cost / month (24-mo TCO) | $153 — $45.00 electricity + hardware amortization |
| Hardware up-front | $2,590 (RTX 4090 24GB Enthusiast) |
| Break-even | Month 5 — cumulative cloud spend passes local |
Estimates: 70/30 input/output mix, 24-month amortization, no resale value, load-time electricity only. Cloud prices last verified: 2026-03-01. Hardware street price checked: 2026-07-06.
The economics that are marginal for one person become compelling in aggregate. Ten people making moderate use of an internal assistant, a RAG system over company docs, and some automation easily sum to 4M tokens/day. At GPT-4o prices that's roughly $570/month, forever, growing with headcount. One RTX 4090 workstation running Qwen 3 32B behind vLLM serves that same aggregate load — vLLM's continuous batching turns one 24 GB card into a genuine multi-user server — for about $153/month all-in during amortization and ~$45/month after. Break-even in about 5 months, and each additional user until saturation costs approximately nothing.
The catch is the sentence "behind vLLM." Someone on the team now owns a server: OS updates, the inference runtime, access control, a restart script, capacity judgment. None of it is hard — it's a Docker container and an SSO proxy, an afternoon for anyone who has deployed anything — but it must be *owned*. Teams where nobody wants that pager should read the cloud column without shame; an unowned server is worse than an invoice.
The pattern we see in workshops: three workloads dominate small-team AI value, and all three fit comfortably in the open-model class.
1. Internal assistant (chat over an OpenAI-compatible endpoint) — Qwen 3 32B-class quality is fully sufficient for drafting, summarizing, and internal Q&A. 2. RAG over company knowledge — the retrieval layer matters more than the model tier; a well-tuned local pipeline over your wiki and docs (guide) beats a frontier model with no context. Keeping embeddings + documents on your own network is also the quiet governance win: nothing about your knowledge base leaves the building. 3. Automation (ticket triage, report drafts, code review comments) — high-volume, latency-tolerant, quality-tolerant: the textbook local workload.
What does *not* fit: the occasional hard task — a thorny contract analysis, a nasty production bug. Keep a metered frontier key for those. A sensible policy is "local endpoint is the default; the cloud key requires a reason" — which also gives your data-governance story teeth, since the sanctioned default keeps prompts on-network (the full argument lives in our GDPR comparison).
For a 5–25 person team: a single RTX 4090 (24 GB) covers 30B-class models with batching headroom — the $2,590 workstation build is the reference config. Heavier or 70B-ambitious teams should look at the dual RTX 3090 build ($2,870, 48 GB) which runs Llama 3.3 70B at Q4, or a 128 GB unified-memory box (GMKtec EVO-X2 class, $2,349) if silence and MoE models matter more than raw throughput. Concurrency rule of thumb: one modern 24 GB card with vLLM sustains a 10–25 person team's interactive load; batch jobs should run off-hours. If two boxes are entering the conversation, read the enterprise comparison — the calculus shifts again at rack scale.
Before buying hardware, price the alternative properly: at team volume you can drop to GPT-4o Mini/Gemini Flash-class models for the easy 80% of traffic and the cloud bill collapses (~$34/month at our preset volume — cheaper than the server's electricity). If your team's workload is genuinely Mini-class, cloud wins the pure cost fight, and the local case rests on governance, latency, and unlimited usage instead. Run both numbers in the calculator; let the spreadsheet, not the vibe, pick your side.
Small teams are the sweet spot for shared local inference: aggregate volume delivers a 5-month break-even against frontier pricing, one card serves everyone via batching, and internal documents never leave the network. But the deal requires an owner for the box and honesty about model class — a Mini-class workload keeps cloud cheaper indefinitely. Local server for the default workload, cloud key by exception, decided by measured volume.
Jakub Rusinowski, the founder of LLM Configurator, runs corporate workshops and lectures on deploying local LLMs — hardware sizing, model selection, compliance-friendly architectures, and hands-on setup for your team. Ask about a workshop, or join the biweekly local AI digest.
Methodology & assumptions. All cost figures are estimates from one shared model (lib/costCompare.ts): cloud costs = tokens/day × published per-1M-token prices at a 70/30 input/output split × 30 days; local costs = hardware amortized over 24 months with no resale value + electricity for load time only at your rate, with machine count scaled when volume exceeds one machine’s throughput. Cloud prices carry per-entry source URLs and verification dates; hardware prices come from the curated /build catalog (street prices with check dates). Real bills vary with usage mix, discounts, and idle power — treat break-even months as directional, not contractual.