Self-hosted coding assistant vs GitHub Copilot
Copilot sends your code to Microsoft's servers to generate a suggestion, then discards or retains it according to the plan you're on. A self-hosted assistant sends your code nowhere. That single difference decides the comparison for a lot of teams before cost even enters the conversation.
What actually leaves your network
Every autocomplete request Copilot serves includes the surrounding file, and often other open files in the editor, sent to GitHub's inference endpoints to generate the completion. For an individual developer working on open-source code, that's a non-issue. For a team under an NDA, a defense contractor, a bank, or anyone working on code they can't show a third party, it's the whole issue. GitHub's own Copilot Trust Center documents what is and isn't retained by plan tier, and the details differ enough between Individual, Business and Enterprise that "Copilot doesn't train on my code" is not a statement you can make without checking which plan a given developer is actually on.
Running a coding model on your own DGX Spark removes that question entirely. The model, the weights, and the inference process all sit on hardware your organization controls. Prompts don't cross a network boundary to a vendor, so there's nothing to configure, no data-processing addendum to read, and no plan tier to get wrong. That's the whole pitch of a self-hosted setup: the privacy guarantee is architectural, not contractual.
What the model itself can do
GPUwerk measured Qwen3-Coder 30B-A3B (AWQ, 4-bit) on a single 128 GB DGX Spark at 80.9 tokens per second decode for one user under vLLM, on 2 September 2026, full figures on the model page. That's comfortably above the point where a chat-style or inline-completion interface feels responsive. Because it's a mixture-of-experts model, it also holds up under load rather than collapsing: aggregate throughput reaches 3,235 tokens per second at 256 concurrent requests, up only 7.5% from the 128-concurrent figure, so a small team sharing one box doesn't fight each other for latency the way a dense model would.
We won't put a number on how Qwen3-Coder's suggestion quality compares to Copilot's underlying model on a specific benchmark; that's a moving target on both sides and GPUwerk hasn't run a head-to-head eval. What we can say concretely: 80.9 tok/s single-user and a 262,144-token context window (measured prefill detail on the model page) is enough headroom to feed an agent large diffs and multi-file context without the model becoming the bottleneck. For wiring it into an actual editor or CLI agent loop, see coding agents against your own endpoint.
Cost at team scale
Copilot's pricing has multiple tiers with different seat and usage limits, and it's the kind of number that changes between when this is written and when you read it, so rather than quote a figure that might already be stale, go straight to GitHub's own pricing page for the current per-seat cost. What we can compare on solid ground is the shape of the two cost curves. Copilot bills per seat per month, so cost scales linearly with headcount regardless of how much any individual developer actually uses it. A dedicated Spark bills at a flat $0.79 per hour, metered per minute, and serves the whole team from one box concurrently; the more developers you put behind it, the lower the effective per-seat cost, down to the point where the Spark's throughput becomes the limit rather than your budget.
Where that crossover sits depends on team size and how continuously the box runs. A single developer running a Spark 24/7 for a month is spending more than the equivalent Copilot seat would cost; a team of eight to ten sharing one box, especially if it's only running during working hours, tends to land below the per-seat total. The honest way to check is to run the arithmetic against your own team size and GitHub's current published rate, not against a number we'd be guessing at here.
Where Copilot still wins
Copilot is zero-setup: install the extension, sign in, done. A self-hosted assistant needs a box, a model, and an inference server, which is real infrastructure work even if it's a few commands on a Spark. Copilot also benefits from continuous updates to a frontier model with no action on your part. If code privacy isn't a constraint for your team and nobody minds the per-seat cost, that convenience is worth something and there's no reason to build around it.
The decision comes down to one question: does any code your developers write need to stay off a third party's servers? If yes, the setup cost of a self-hosted assistant is the price of an actual guarantee rather than a policy read from a trust center page. If no, Copilot's convenience is hard to beat.