- Who it helps
- Teams evaluating large coding models on dedicated GPU infrastructure
- Availability
- Repository describes MIT-licensed weights and inference code. FP8 deployment requires about 315 GB for weights plus inference memory; current API access was not verified.
Architecture and practical size
The model has 309 billion total parameters and activates 15.5 billion per token. Sparse attention selects part of the history for computation, while the full key/value cache is retained. This is a server-scale deployment.
Speed needs a matched comparison
The README advertises up to 2,000 tokens per second in Ultrafast mode. A November 10 independent article uses a different headline figure. Neither number was reproduced here; hardware, mode, output length and total answer time matter.
Naive-N0.5-Flash model repository ↗NaiveAI coding comparison ↗
What you can do
- Check the GPU memory requirement and compare end-to-end answer time under matched serving conditions. Open reference ↗
Where the evidence stops
Repository documentation and the independent article introduction checked November 11, 2026. No weights downloaded, API called or benchmark run.
Sources read. Public source text checked November 11, 2026 UTC. No third-party project or complete workflow executed.
Sources & further reading
GitHub · implementation
Naive-N0.5-Flash model repository ↗
README architecture, availability and memory requirements inspected. Speed is an author claim and differs from the comparison headline.
NaiveAI Labs · page checked 2026-11-11
Web · analysis
NaiveAI coding comparison ↗
November 10 article introduction and benchmark setup inspected. Full experiments were outside the sample.
Gabriele Beltramo · page checked 2026-11-11 · Published 2026-11-10
Coverage note: Repository documentation and the independent article introduction checked November 11, 2026. No weights downloaded, API called or benchmark run.
The record over time (1)
Dated source checks and published explanations. An observation date can differ from the original release date.
- · Added to Current
Naive-N0.5-Flash is an open-weight coding model that combines local and sparse attention to reduce long-context inference work. Its repository describes a one-million-token context window.