RRepoGEO

REPOGEO REPORT · LITE

eugr/spark-vllm-docker

Default branch main · commit ba9dde96 · scanned 5/12/2026, 6:32:17 PM

GitHub: 1,337 stars · 239 forks

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 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

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

OVERALL DIRECTION
  • hightopics#1
    Add specific topics for better categorization

    Why:

    CURRENT
    (none)
    COPY-PASTE FIX
    vllm, spark, llm-inference, docker, gpu, distributed-inference, ray, dgx, high-performance-computing
  • highreadme#2
    Refine README's opening paragraph to emphasize distributed LLM inference on Spark/DGX

    Why:

    CURRENT
    This repository contains the Docker configuration and startup scripts to run a multi-node vLLM inference cluster using Ray. It supports InfiniBand/RDMA (NCCL) and custom environment configuration for high-performance setups. Cluster setup supports direct connect between dual Sparks, connecting via QSFP/RoCE switch and 3-node mesh configuration.
    COPY-PASTE FIX
    This repository provides the Docker configuration and startup scripts to deploy **scalable, distributed large language model (LLM) inference** using vLLM on Apache Spark clusters, leveraging Ray for multi-node orchestration. Optimized for high-performance setups like NVIDIA DGX systems, it supports InfiniBand/RDMA (NCCL) and custom environment configurations for efficient LLM serving across multiple GPU servers.
  • mediumabout#3
    Update the 'About' description for conciseness and clarity

    Why:

    CURRENT
    Docker configuration for running VLLM on dual DGX Sparks
    COPY-PASTE FIX
    Docker configuration and scripts for high-performance, distributed vLLM inference on multi-node Spark clusters, optimized for DGX systems.

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
0%
Of all named tools, what % are you?
Top rival
kubernetes/kubernetes
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. kubernetes/kubernetes · recommended 2×
  2. NVIDIA/gpu-operator · recommended 2×
  3. triton-inference-server/server · recommended 2×
  4. moby/moby · recommended 2×
  5. ray-project/ray · recommended 2×
  • CATEGORY QUERY
    What's the best way to deploy LLM inference across multiple GPU servers using containers?
    you: not recommended
    AI recommended (in order):
    1. Kubernetes (kubernetes/kubernetes)
    2. NVIDIA GPU Operator (NVIDIA/gpu-operator)
    3. KServe (kserve/kserve)
    4. NVIDIA Triton Inference Server (triton-inference-server/server)
    5. Docker Swarm (docker/swarm)
    6. Hugging Face TGI (Text Generation Inference) (huggingface/text-generation-inference)
    7. Docker (moby/moby)
    8. Ray Serve (ray-project/ray)
    9. Ray Clusters (ray-project/ray)
    10. FastAPI (tiangolo/fastapi)
    11. Uvicorn (encode/uvicorn)
    12. containerd (containerd/containerd)
    13. Prometheus (prometheus/prometheus)
    14. Grafana (grafana/grafana)
    15. ELK stack (elastic/elasticsearch)
    16. Loki (grafana/loki)
    17. NGINX (nginx/nginx)
    18. HAProxy (haproxy/haproxy)

    AI recommended 18 alternatives but never named eugr/spark-vllm-docker. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    Seeking a container setup for high-throughput language model inference on powerful GPU systems.
    you: not recommended
    AI recommended (in order):
    1. NVIDIA Triton Inference Server (triton-inference-server/server)
    2. Kubernetes (kubernetes/kubernetes)
    3. NVIDIA GPU Operator (NVIDIA/gpu-operator)
    4. Docker (moby/moby)
    5. NVIDIA Container Toolkit (NVIDIA/nvidia-container-toolkit)
    6. Singularity (apptainer/apptainer)
    7. Apptainer (apptainer/apptainer)
    8. AWS SageMaker Endpoints

    AI recommended 8 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 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?

  • 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?

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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