REPOGEO REPORT · LITE

ovg-project/kvcached

Default branch main · commit d70d674c · scanned 6/22/2026, 8:22:11 AM

GitHub: 1,075 stars · 120 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)

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

AI VISIBILITY SCORE

35 /100

Critical

Category recall

0 / 2

Not recommended in any query

Rule findings

1 pass · 1 warn · 0 fail

Objective metadata checks

AI knows your name

3 / 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 ovg-project/kvcached, 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

Add a concise, direct project definition as the very first line of the README

Why:

CURRENT

The README currently starts with a <div> block containing links, followed by an H2.

COPY-PASTE FIX

kvcached is an elastic KV cache library that brings OS-style virtual memory to LLM serving on shared GPUs, enabling demand-driven allocation and improved GPU utilization.

highhomepage#2
Add the project homepage URL to the repository's About section
Why:
COPY-PASTE FIX
```
https://kvcached.org/
```

mediumreadme#3

Strengthen the README's initial problem statement and unique solution

Why:

CURRENT

kvcached (KV cache daemon) is a KV cache library for LLM serving/training on **shared GPUs**. By bringing OS-style **virtual memory** abstraction to LLM systems, it enables **elastic and demand-driven** KV cache allocation, improving GPU utilization under dynamic workloads.

COPY-PASTE FIX

Unlike traditional LLM serving engines that pre-allocate fixed KV cache, kvcached introduces a virtual memory abstraction for KV caches, allowing serving engines to reserve virtual memory and back it with physical GPU memory only when actively used. This unique decoupling enables on-demand allocation and flexible sharing, significantly boosting GPU memory utilization under dynamic and mixed workloads.

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 ovg-project/kvcached

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

vLLM

Recommended in 2 of 2 queries

COMPETITOR LEADERBOARD

vLLM · recommended 2×
Hugging Face Optimum · recommended 2×
NVIDIA Triton Inference Server · recommended 1×
DeepSpeed-MII · recommended 1×
NVIDIA TensorRT · recommended 1×

CATEGORY QUERY
How to improve GPU utilization for LLM inference with dynamic workloads?
you: not recommended
AI recommended (in order):
1. vLLM
2. NVIDIA Triton Inference Server
3. DeepSpeed-MII
4. NVIDIA TensorRT
5. AutoGPTQ
6. bitsandbytes
7. Hugging Face Optimum
8. NVIDIA TensorRT-LLM
9. Hugging Face Optimum
10. OpenVINO
11. Hugging Face Transformers
12. DeepSpeed-FastGen
AI recommended 12 alternatives but never named ovg-project/kvcached. This is the gap to close.
Show full AI answer
CATEGORY QUERY
Seeking a library for elastic KV cache management in LLM serving systems.
you: not recommended
AI recommended (in order):
1. vLLM
2. TGI (Text Generation Inference) by Hugging Face
3. DeepSpeed-MII (Microsoft Inference Interface)
4. FasterTransformer (NVIDIA)
5. TensorRT-LLM (NVIDIA)
AI recommended 5 alternatives but never named ovg-project/kvcached. This is the gap to close.
Show full AI answer

Objective checks

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

Metadata completeness
warn
Suggestion:
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 ovg-project/kvcached?
pass
AI named ovg-project/kvcached explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?
If a team adopts ovg-project/kvcached in production, what risks or prerequisites should they evaluate first?
pass
AI named ovg-project/kvcached explicitly
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 ovg-project/kvcached solve, and who is the primary audience?
pass
AI named ovg-project/kvcached explicitly
AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

Embed your GEO score

Drop this badge into the README of ovg-project/kvcached. It auto-updates whenever the report is rescanned and links back to the latest report — easy public proof that you care about AI discoverability.

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MARKDOWN (README)

[![RepoGEO](https://repogeo.com/badge/ovg-project/kvcached.svg)](https://repogeo.com/en/r/ovg-project/kvcached)

HTML

<a href="https://repogeo.com/en/r/ovg-project/kvcached"><img src="https://repogeo.com/badge/ovg-project/kvcached.svg" alt="RepoGEO" /></a>

Pro

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ovg-project/kvcached — 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