REPOGEO REPORT · LITE

qualcomm/nexa-sdk

Default branch main · commit edd01833 · scanned 6/26/2026, 4:11:26 AM

GitHub: 8,110 stars · 1,010 forks

Scan history for this repo

Score trend below includes all ready runs (older left, newer right; scroll horizontally if needed). The table is collapsed by default—expand for newest-first rows, 10 per page.

Score trend (left → right: older → newer)

3 ready scans. Expand the table below for newest-first rows (10 per page, paginated).

AI VISIBILITY SCORE

27 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

2 pass · 0 warn · 0 fail

Objective metadata checks

AI knows your name

1 / 3

Direct prompts that named your repo

HOW TO READ THIS REPORT

Action plan is what to do next — copy-pasteable changes prioritized by impact. Category visibility is the real GEO test: when a user asks an AI a brand-free question that should surface qualcomm/nexa-sdk, does the AI actually recommend you — or your competitors? Objective checks verify the metadata signals AI engines weight first. Self-mention check detects whether AI even knows you exist by name.

Action plan — copy-paste fixes

3 prioritized changes generated by gemini-2.5-flash. Mark items done after you ship the fix.

OVERALL DIRECTION

highreadme#1

Emphasize broad platform and hardware support in README's opening

Why:

CURRENT

NexaSDK lets you build the smartest and fastest on-device AI with minimum energy. It is a highly performant local inference framework that runs the latest multimodal AI models locally on NPU, GPU, and CPU - across Android, Windows, and Linux devices with a few lines of code.

COPY-PASTE FIX

NexaSDK is a highly performant local inference framework that lets you run frontier LLMs and VLMs with day-0 model support across GPU, NPU, and CPU. It provides comprehensive runtime coverage for PC (Python/C++), mobile (Android & iOS), and Linux/IoT (Arm64 & x86 Docker), enabling you to build the smartest and fastest on-device AI with minimum energy.

hightopics#2

Remove 'go' from topics

Why:

CURRENT

gemma3, go, gpt-oss, granite4, llama, llama3, llm, on-device-ai, phi3, qwen3, qwen3vl, sdk, stable-diffusion, vlm

COPY-PASTE FIX

gemma3, gpt-oss, granite4, llama, llama3, llm, on-device-ai, phi3, qwen3, qwen3vl, sdk, stable-diffusion, vlm

mediumreadme#3
Refine README H1 to include core function
Why:
CURRENT
```
# NexaSDK
```
COPY-PASTE FIX
```
# NexaSDK: On-Device LLM & VLM Inference Framework
```

Category GEO backends resolved for this scan: google/gemini-2.5-flash, deepseek/deepseek-v4-flash

Category visibility — the real GEO test

Brand-free queries asked to google/gemini-2.5-flash. Did AI recommend you, or someone else?

Same questions for every model — switch tabs to compare answers and rankings.

Recall

0 / 2

0% of queries surface qualcomm/nexa-sdk

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ONNX Runtime

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

ONNX Runtime · recommended 2×
MediaPipe · recommended 1×
TensorFlow Lite · recommended 1×
PyTorch Mobile · recommended 1×
Core ML · recommended 1×

CATEGORY QUERY
How to run cutting-edge multimodal AI models locally on mobile and embedded systems?
you: not recommended
AI recommended (in order):
1. MediaPipe
2. TensorFlow Lite
3. ONNX Runtime
4. PyTorch Mobile
5. Core ML
6. OpenVINO Toolkit
7. Edge Impulse
AI recommended 7 alternatives but never named qualcomm/nexa-sdk. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Looking for a performant inference framework for LLMs and VLMs across NPU, GPU, and CPU.
you: not recommended
AI recommended (in order):
1. ONNX Runtime
2. TensorRT
3. OpenVINO
4. DeepSpeed-MII
5. vLLM
6. PyTorch
7. Triton Inference Server
AI recommended 7 alternatives but never named qualcomm/nexa-sdk. This is the gap to close.
Show full AI answer

Objective checks

Rule-based audits of metadata signals AI engines weight most.

Metadata completeness
pass
README presence
pass

Self-mention check

Does AI even know your repo exists when asked about it directly?

Compared to common alternatives in this category, what is the core differentiator of qualcomm/nexa-sdk?
pass
AI named qualcomm/nexa-sdk explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts qualcomm/nexa-sdk in production, what risks or prerequisites should they evaluate first?
pass
AI did not name qualcomm/nexa-sdk — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
In one sentence, what problem does the repo qualcomm/nexa-sdk solve, and who is the primary audience?
pass
AI did not name qualcomm/nexa-sdk — likely talking about a different project
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/qualcomm/nexa-sdk.svg)](https://repogeo.com/en/r/qualcomm/nexa-sdk)

HTML

<a href="https://repogeo.com/en/r/qualcomm/nexa-sdk"><img src="https://repogeo.com/badge/qualcomm/nexa-sdk.svg" alt="RepoGEO" /></a>

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qualcomm/nexa-sdk — Lite scans stay free; this card itemizes Pro deep limits vs Lite.

Deep reports10 / month
Brand-free category queries5 vs 2 in Lite
Prioritized action items8 vs 3 in Lite