Repository sample
Start with a bounded private code sample, docs, issue patterns, and expected answers.
- No secrets
- No production credentials
- Fixed tasks
Private code assistant offer note
Private code assistant benchmark for local developer teams
Use this benchmark before rolling a local AI coding assistant into private repositories. It turns Qwen3-Coder momentum into a measured scope: safe repository samples, secrets boundaries, context budget, editor fit, and RTX 4000 Ada model-fit limits.
- Representative code, docs, and issue tasks come before production promises
- Secrets, credentials, customer data, and write permissions stay out of the first corpus
- Qwen3-Coder 30B and Qwen3-Coder-Next are benchmark candidates, not live-throughput claims
Updated 2026-05-31; live local-inference claims remain gated until the actual server passes driver, Ollama, and model smoke tests.
Model-fit trial
Test Qwen3-Coder 30B and lighter fallbacks against real repository work before team rollout.
- Latency notes
- VRAM notes
- Failure cases
Business Secure
For production teams, code assistant work belongs behind access control, audit scope, and update windows.
- RBAC scope
- OIDC/SSO planning
- Change windows
Benchmark gates before rollout
These gates turn developer interest into a paid, evidence-based setup instead of an uncontrolled AI experiment.
Select safe repository slices
Choose representative source files, READMEs, architecture notes, and issue examples. Exclude secrets, production data, private keys, tokens, and customer records before the first model run.
Use fixed developer prompts
Define tasks such as explain a module, locate a bug, draft a patch, update tests, or summarize a pull request. Every task needs an expected answer or review rubric.
Decide who may act
Separate read-only answer generation from patch creation. Scope users, API keys, editor access, logs, and review requirements before anyone depends on the assistant.
Commercial rule: sell the benchmark, report, and managed operating scope first. Upgrade public copy to live local code assistant claims only after runtime health and target-model smoke tests pass on the actual host.
Model-fit checks for Qwen3-Coder class tools
Current model pages make private code assistants attractive, but 20 GB VRAM still needs measured limits.
Qwen3-Coder 30B at 19 GB
Ollama lists a 30B Qwen3-Coder variant at 19 GB with a large context window. On an RTX 4000 Ada 20 GB system, that makes it a benchmark candidate, not a capacity guarantee.
Qwen3-Coder-Next watchlist
Qwen3-Coder-Next targets agentic coding and local development with long-context behavior. It stays a watchlist benchmark until quantization, memory, latency, and tool calling work on the target host.
Smaller models first when needed
If the 30B path fails memory, latency, or concurrency checks, the useful product is still the benchmark result and a smaller fallback plan for developer workflows.
What the benchmark report should prove
The deliverable must be useful to a buyer even if the largest local model is not a production fit.
Can it load?
Record driver state, Ollama health, model availability, context target, VRAM behavior, and startup failures.
Can it answer?
Run fixed repository questions and compare answers against expected files, symbols, and rationale.
Can it patch?
Test small patches only with review gates, test commands, and rollback expectations.
Can a team use it?
Define access, logs, backups, update windows, user count, and support boundaries.
Primary sources tracked
These sources guide scope language; final claims still depend on the server runtime and the buyer repository.
Qwen3-Coder model path
Use for local model availability, size, and endpoint planning after Ollama health is proven.
Ollama Qwen3-Coder →Qwen3-Coder and Next
Use for agentic coding, long-context, and tool-use positioning, then verify locally before promises.
Qwen3-Coder GitHub →Qwen3-Coder blog →Qwen3-Coder 30B
Use the official model card and license as planning input before any production repository rollout.
Hugging Face model card →RTX 4000 Ada 20 GB
Use NVIDIA specifications as the public hardware boundary, not as an automatic throughput promise.
NVIDIA RTX 4000 Ada →