How Fast Does a GPU Pay for Itself With GIDE? Faster Than You Think
Per-token AI coding bills grow with every developer. We ran the numbers on pointing GIDE at your own GPUs, for teams that already own them and teams buying new ones.
In our last post we showed that the Mac on every developer’s desk can carry most of an AI coding agent’s work. This post is about the other machine many companies have, or are thinking about buying: a GPU server.
The question is simple. If a team points GIDE at its own GPUs for the routine work and keeps a frontier model for the hard turns, how fast does the hardware pay for itself, and what does it save after that?
Short version: if you already own the GPUs, you save from the first day. If you buy new ones and run the right model, the box typically pays for itself in 6 to 10 months, and every month after that is mostly savings.
Why GIDE, in short
- Connect GIDE to a model on your own GPU server, and every developer on the network can use it
- Frontier models on your own key for the hard turns, one
@away, in the same conversation - The same gates, audit log and memory for every model, local or online
- Your code stays on hardware you control
The bill you’re comparing against
AI coding is now billed per token. Anthropic’s own figure for enterprise teams is about $13 per developer per active day, or $150 to $250 a month, with 90% of developers under $30 a day (Anthropic’s cost docs, as reported by Business Insider). Over a working year of about 220 active days, that’s roughly $2,860 per developer.
For a team of 50, that’s about $143,000 a year, on a line that grows every time someone works harder.
Most of those tokens aren’t hard problems. As we covered last time, about 96% of an agent’s tokens are re-reads of context it has already seen, and most of the work is small edits, test loops and “run it again and fix the import.” That’s volume, and volume is what a model on your own hardware is good at.
Case 1: you already own GPUs
Plenty of companies have GPUs that aren’t busy all day: a server from an earlier AI pilot, an ML team’s inference box, render or simulation machines, high-end workstations. If you have one with a 96 GB card, or a few smaller ones, there’s no purchase to pay back. The only new costs are power and someone’s time to look after it.
Here’s what that looks like when GIDE sends 70% of the work to a model on that server and the rest stays on a frontier model:
| Team size | GPUs used (96 GB class) | Power + upkeep / year | Net saving / year |
|---|---|---|---|
| 25 developers | 1 | ~$16,000 | ~$34,000 |
| 50 developers | 2 | ~$22,000 | ~$78,000 |
| 100 developers | 4 | ~$34,000 | ~$166,000 |
Over five years, that’s roughly $170,000 for a 25-person team and over $800,000 for a team of 100, from hardware that was already on the books.
The upkeep figure is deliberately generous: 3 to 6 hours a week of an engineer’s time at $100 an hour, plus electricity at the US commercial average of about 14 cents per kWh (EIA data via ElectricChoice). Power is the small part, under $800 a year per card. Most of the cost is the person.
Case 2: you buy new GPUs
GPU prices went up this year. NVIDIA’s RTX PRO 6000 Blackwell, the 96 GB card that has become the standard single-card choice for serving coding models, launched at $8,565 and now lists at $16,000 on NVIDIA’s own store, because of the memory shortage. We used that price, plus about $5,000 per card for the server around it.
Even at that price, the numbers work:
| Team size | GPUs bought | Upfront cost | Pays for itself in | Saved after 3 years | Saved after 5 years |
|---|---|---|---|---|---|
| 25 developers | 1 | ~$21,000 | ~7 months | ~$80,000 | ~$147,000 |
| 50 developers | 2 | ~$42,000 | ~6 months | ~$191,000 | ~$347,000 |
| 100 developers | 4 | ~$84,000 | ~6 months | ~$414,000 | ~$746,000 |
These are net figures: the hardware, power and upkeep are already subtracted.
After payback, the margin only grows. Once the hardware is paid off, the only ongoing costs are power and upkeep, so almost every dollar the GPUs keep off the token bill is savings. In the table above, a 50-person team saves about $78,000 in each year after the first.
The one choice that makes or breaks it: the model
These numbers assume a mixture-of-experts (MoE) coding model, and that choice matters more than the GPU.
A dense model runs all of its parameters for every token it writes. An MoE model only runs a small slice of them. In Jarvislabs’ tests on a single RTX PRO 6000, Qwen3.6-35B-A3B, an MoE model that uses about 3 billion parameters per token, served roughly three times as much as the dense Qwen3.6-27B under the same coding load. It still scores 73.4% on SWE-bench Verified.
In practice that means one card can serve a much bigger team. We planned on 25 developers per card for an MoE model and 10 per card for a dense 27B model, both with room for everyone to hit it at once.
If your team wants the stronger dense model, the hardware still pays off, it just takes longer:
| Team size, dense 27B | GPUs bought | Pays for itself in | Saved after 5 years |
|---|---|---|---|
| 50 developers | 5 | ~23 months | ~$168,000 |
| 100 developers | 10 | ~14 months | ~$674,000 |
A good middle path: run the MoE model as the team default and keep the frontier model for the turns that really need it. That’s exactly the workflow GIDE is built around.
How GIDE uses the server
The GPUs are the easy part. The hard part is getting a whole team to use them without losing the context, conventions and controls they have with a frontier model.
One server, every developer. GIDE’s VPS & Private Servers support connects the editor to a model running on a machine you control. One box serves the whole team over your network, so a developer on a 16 GB laptop gets the same model as someone on a workstation. If you want help setting it up, we’ll do it with you.
Rent the brains, own the volume. GIDE runs local models, models on your server and frontier models on your own API key (Anthropic, OpenAI, Google) in the same editor. Local inference is unlimited on every plan. You can @ mention any model for a single message, and it sees the whole conversation. A typical day looks like this:
- Ask a frontier model on your key to plan the hard turn.
- Approve the plan at the Plan Gate.
- Let the model on your GPU server make the edits, run the tests and fix what breaks.
@the frontier model again to review the result.
You pay per token for steps 1 and 4. The volume in step 3 runs on your hardware. The developer picks the model for each turn, so every frontier call is a deliberate one and the spend is easy to explain.
Context that moves with the work. Memory, history and project conventions in GIDE are local files that whichever model answers can read. When work passes from a frontier model to the one on your server, it picks up from the same context, not from zero.
The same controls on every model. Whether the answer comes from a frontier API or your own server, it goes through the same gates: the Plan Gate before any change, the Write Gate on every file write, the Command Gate on every shell command and MCP tool call. Every plan, write and rejection lands in a local JSONL audit log. For a security team, that’s one review of one tool, not one per model.
Code that stays inside. With a model on your own server, prompts and code never leave your network. On Enterprise, GIDE can run fully air-gapped.
How we got these numbers
So you can check the math or plug in your own:
- API cost: $13 per developer per active day, 220 active days a year. At today’s lower list prices a typical day can come in closer to $9.50, which stretches payback for the MoE setup to about 10 months for a 50-person team.
- Share moved to your server: 70% of the spend. Teams with more routine work will move more.
- Hardware: $16,000 per RTX PRO 6000 plus $5,000 per card for the server. No resale value counted, even though used cards have held their price this year.
- Power: 450 W average per card including its share of the server, 1.4x for cooling, 14 cents per kWh.
- Upkeep: 3 to 8 hours a week of an engineer’s time at $100 an hour, depending on the number of cards.
- Capacity: 25 developers per card for an MoE model, 10 for a dense 27B, both conservative against published throughput measurements.
These are estimates from public prices and published benchmarks, not GIDE benchmarks. Your numbers will depend on your team, your models and how your developers work.
FAQ
Do we need to buy GPUs to use GIDE?
No. GIDE runs local models on Apple Silicon Macs (M2 or newer, 16 GB) and on Windows machines with an RTX 3060 12 GB or better, and works with frontier models on your own key. A shared GPU server is an option for teams that want a bigger model for everyone.
How many developers can one GPU serve?
With an MoE coding model on a 96 GB card like the RTX PRO 6000, we planned on about 25 developers per card with headroom for busy moments. A dense 27B model serves fewer, about 10 per card.
What happens after the GPUs are paid off?
The saving grows. After payback, the only ongoing costs are power and upkeep, so nearly everything the server keeps off your token bill is savings. A 50-person team in our model saves about $78,000 a year after the first year.
Does the model on our server replace the frontier model?
No. Frontier models are still better at the hardest problems. The server carries the volume: edits, test loops, re-runs. In GIDE you keep a frontier model one @ away for the turns that need it.
Does our code leave the network?
Not when the model runs on your server. Prompts and code go only to the machine you choose. On Enterprise, GIDE can run fully air-gapped.
Try it
If your company already has GPUs, they can start cutting your AI coding bill this week. If it doesn’t, a single card can pay for itself in well under a year.
Every GIDE plan starts with a 30-day free trial, no credit card needed. Download GIDE, read why the Mac on every desk is the first place to start, or talk to us about setting up your own server.