Written by Jakub Rusinowski · Last updated 2026-07-08 · Prices verified 2026-03-01
Split the workload and both sides win: local models (Qwen 2.5 Coder 32B-class on a 24 GB GPU) now handle autocomplete, boilerplate, tests, and everyday code questions at near-parity — with lower latency and zero per-token cost — while frontier cloud models remain clearly better at multi-file debugging, architecture reasoning, and unfamiliar frameworks. Heavy assistant users burning ~3M tokens/day at Claude Sonnet prices (~$594/month) recoup an RTX 4090 workstation in about 5 months by routing the bulk tier locally.
The coding workload, split into what it actually consists of.
| Dimension | Local | Cloud |
|---|---|---|
| Autocomplete / boilerplate | Excellent — 7–14B coder models, sub-50ms first token | Excellent, plus network round-trip |
| Multi-file refactoring & debugging | Decent at 32B; visibly below frontier | Best available — where frontier earns its price |
| Latency in the editor loop | No network; feels instant | 100–500ms+ round trips, felt at completion frequency |
| Cost at 3M tok/day | ~$34/mo electricity (+$108/mo amortization) | ~$594/mo (Claude Sonnet) |
| Proprietary/NDA code exposure | Never leaves the machine | Provider terms; often contractually restricted by clients |
| Context window practicality | Large contexts eat VRAM — plan for it | 128k–1M tokens, no local resource cost |
| Setup | Ollama + editor plugin (Continue/Cline), ~1 hour | API key |
Heavy coding-assistant use: ~3M tokens/day (context-rich completions all day) — 3M tokens/day, RTX 4090 24GB Enthusiast vs Claude 3.7 Sonnet (Anthropic), electricity $0.15/kWh. Adjust every input in the interactive calculator on this page.
| Cloud cost / month | $594 (Claude 3.7 Sonnet, $3/M input + $15/M output) |
| Local cost / month (24-mo TCO) | $142 — $33.75 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 "AI coding" bill conflates two very different things. Volume work — completions, docstrings, test scaffolds, "what does this error mean" — is enormous in token count (an editor assistant re-sends context on every keystroke pause; 3M tokens/day is normal for a heavy user) and modest in difficulty. Depth work — cross-file debugging, architecture trade-offs, unfamiliar-framework surgery — is maybe 5% of tokens and 50% of the value.
That split is the whole answer. Volume work is exactly what local models are now good at: Qwen 2.5 Coder 32B on a 24 GB card is a genuinely strong completion and everyday-questions model, and 7–14B coder models are more than adequate for autocomplete at speeds no cloud API can match, because there is no network in the loop. Depth work is where frontier models still clearly win, and where paying $3/$15 per million tokens (Anthropic pricing) is rational — you're buying hours of your own debugging time back.
At 3M tokens/day against Claude Sonnet, the assistant bill is roughly $594/month. An RTX 4090 workstation (~$2,590) running the volume tier locally consumes about $34/month of electricity at 14 hours of daily load. Even keeping a frontier key for the depth tier, moving the bulk offline breaks even in about 5 months — see the calculator below with your own volume. If your usage is lighter (say 500k tokens/day), break-even stretches to ~2.5 years and the case weakens to latency and privacy; measure your dashboard before buying.
One honest complication: coding contexts are large, and context is a local resource. A 32B model at Q4 with a 32k context fits a 24 GB card; push toward 128k and you need smarter context management (most editor integrations do retrieval-style trimming anyway) or more VRAM. Cloud models make long context somebody else's memory problem — a real advantage for repo-scale questions.
Completion UX lives or dies at the 100ms scale. A local 7B coder model starts streaming in tens of milliseconds; a cloud round trip adds 100–500ms before the first token on a good day. Over hundreds of completions daily, that's the difference between the assistant feeling like part of the editor and feeling like a suggestion popup you wait for. Developers who try local completion rarely go back for that reason alone — quality parity at the completion tier arrived quietly in 2025.
If you do contract work, your MSA very likely restricts sending client code to third parties, and "but the API terms say no training" does not amend a contract. A local model is the clean answer: NDA code never transits anyone's infrastructure. This single constraint pushes more professional devs to local coding setups than the cost math does — the privacy comparison covers the general case.
Editor: Continue or Cline pointed at Ollama's OpenAI-compatible endpoint. Models: a 7B coder model for tab-completion (fast lane) + Qwen 2.5 Coder 32B for chat/refactor (quality lane) — Ollama hot-swaps them. Hardware: 16 GB VRAM minimum for the two-model setup, 24 GB comfortable (what fits your card). Keep the frontier API key wired into the same tools for the depth tier; the point is routing, not abstinence.
Route by tier and stop arguing about the average: local for the completion/boilerplate volume that dominates your token count (better latency, zero meter, contractually clean), frontier API for the debugging depth where it demonstrably outperforms. Heavy users recoup the workstation in about 5 months; light users should keep the API key and skip the purchase. The developers who are happiest with this decision measured their own usage first.
Not sure which tier fits? The build recommender maps budgets to complete part lists — or check what your existing GPU already runs for free.
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.