What this is
We noticed an interesting test: a developer ran a 27B-parameter (the larger the parameter count, the stronger the model) Qwen model on a single RTX 3090 (24GB VRAM) for local coding tests. Three of four Python tasks passed, one failed completely. The failure wasn't due to insufficient GPU power—it was that the AI burned through all of its "thinking time."
Specific setup: the model was quantized using Q4_K_M (a compression format that trades precision for smaller size and faster speed), weighing 16.8GB and loaded entirely into the 24GB GPU. The inference framework is llama.cpp, and the coding agent is the open-source OpenCode. The context window is 131K tokens (the smallest unit of text AI processes, roughly equivalent to one Chinese character or half an English word), with an 8K output cap per turn.
Across the four tasks: the LRU cache implementation went from 0/10 to 10/10 in about 3 minutes; the CSV accounting system went from 1/10 to 10/10 in about 5 minutes 44 seconds; the incremental build planner stayed at 0/10 → 0/10 and produced no code at all; and the SQLite atomic transfer went from 1/10 to 10/10 but timed out.
Industry view
The encouraging side: a mid-size 27B model used to require the cloud or a multi-GPU server. Now a single used 3090 can locally drive an AI coding agent. "Local AI coding" has moved from the geek circle to mainstream hardware accessibility.
But what deserves more of our attention is the completely failed task. Its input was only 4,985 tokens, yet the AI spent the entire 8,192 output allowance on internal "reasoning" (letting the model "think" before answering, similar to outlining before writing). In the end, not a single character of code was produced. This is "reasoning budget overspend," unrelated to context length. In other words, the model wasn't "running out of room"—it was "thinking too much."
There's another counterintuitive finding: passing tests ≠ correct code. The CSV task's preset tests all passed at 10/10, but the developer's manual review afterward found a rounding bug with decimals. In other words, even when AI-written code goes fully green on tests, it doesn't mean it's production-ready.
Impact on regular people
For enterprise IT: local AI coding is now feasible at the hardware level, but the vision of "AI auto-writes code, no human review, straight to production" isn't mature at this stage—at minimum, a human review loop must remain in place.
For working professionals: if you're using AI to code more efficiently, we recommend treating "I'll review what AI wrote" as a standard practice—especially in scenarios involving finance, precision, and edge cases.
For the consumer market: over the next year, the bottleneck in mainstream AI coding tools will shift from "not enough compute" to "AI thinks too long" or "tests pass but hidden bugs remain." This is a new trend worth product managers and CTOs preparing for in advance.