REPOGEO REPORT · LITE

eugr/spark-vllm-docker

Default branch main · commit d396c851 · scanned 6/23/2026, 3:19:25 AM

GitHub: 1,671 stars · 302 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

28 /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

2 / 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 eugr/spark-vllm-docker, 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

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

OVERALL DIRECTION

highreadme#1

Clarify README's opening sentence to emphasize deployment solution

Why:

CURRENT

This repository contains the Docker configuration and startup scripts to run vLLM on DGX Spark, from a single node to multi-node clusters using Ray or vLLM's native PyTorch distributed mode. It supports InfiniBand/RDMA (NCCL), custom environment configuration, and high-performance model loading through fastsafetensors and InstantTensor.

COPY-PASTE FIX

This repository provides a **production-ready Docker deployment solution** for running vLLM on NVIDIA DGX Spark clusters, optimized for both single and multi-node distributed LLM inference. It includes pre-configured Docker images and startup scripts to leverage Ray or vLLM's native PyTorch distributed mode, supporting InfiniBand/RDMA (NCCL) and high-performance model loading.

mediumhomepage#2
Add a homepage URL to the repository About section
Why:
COPY-PASTE FIX
```
https://github.com/eugr/spark-vllm-docker
```

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 eugr/spark-vllm-docker

Avg rank

—

Lower is better. #1 = top recommendation.

Share of voice

Of all named tools, what % are you?

Top rival

ray-project/ray

Recommended in 3 of 2 queries

COMPETITOR LEADERBOARD

ray-project/ray · recommended 3×
microsoft/DeepSpeed · recommended 1×
NVIDIA/Megatron-LM · recommended 1×
vllm-project/vllm · recommended 1×
huggingface/accelerate · recommended 1×

CATEGORY QUERY
How can I deploy large language models efficiently across multiple GPU servers?
you: not recommended
AI recommended (in order):
1. DeepSpeed (microsoft/DeepSpeed)
2. Megatron-LM (NVIDIA/Megatron-LM)
3. vLLM (vllm-project/vllm)
4. Hugging Face Accelerate (huggingface/accelerate)
5. Ray (ray-project/ray)
6. Kubernetes
7. NVIDIA GPU Operator (NVIDIA/gpu-operator)
8. Open MPI
9. NCCL (NVIDIA/nccl)
AI recommended 9 alternatives but never named eugr/spark-vllm-docker. This is the gap to close.
Show full AI answer
CATEGORY QUERY
What's a good way to containerize and scale LLM inference on distributed hardware?
you: not recommended
AI recommended (in order):
1. Kubernetes (kubernetes/kubernetes)
2. KubeFlow (kubeflow/kubeflow)
3. KServe (kserve/kserve)
4. Ray (ray-project/ray)
5. Ray Serve (ray-project/ray)
6. NVIDIA Triton Inference Server (triton-inference-server/server)
7. OpenShift
8. Open Data Hub
9. AWS SageMaker Endpoints
10. Azure Machine Learning Endpoints
11. Google Cloud Vertex AI Endpoints
AI recommended 11 alternatives but never named eugr/spark-vllm-docker. 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 eugr/spark-vllm-docker?
pass
AI did not name eugr/spark-vllm-docker — 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?
If a team adopts eugr/spark-vllm-docker in production, what risks or prerequisites should they evaluate first?
pass
AI named eugr/spark-vllm-docker 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 eugr/spark-vllm-docker solve, and who is the primary audience?
pass
AI named eugr/spark-vllm-docker 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 eugr/spark-vllm-docker. 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/eugr/spark-vllm-docker.svg)](https://repogeo.com/en/r/eugr/spark-vllm-docker)

HTML

<a href="https://repogeo.com/en/r/eugr/spark-vllm-docker"><img src="https://repogeo.com/badge/eugr/spark-vllm-docker.svg" alt="RepoGEO" /></a>

Pro

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eugr/spark-vllm-docker — 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