RRepoGEO

REPOGEO REPORT · LITE

b4rtaz/distributed-llama

Default branch main · commit e0c59737 · scanned 6/28/2026, 10:21:54 AM

GitHub: 2,967 stars · 237 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 b4rtaz/distributed-llama, 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
    Reposition the README's opening to emphasize consumer-grade hardware

    Why:

    CURRENT
    Connect home devices into a powerful cluster to accelerate LLM inference. More devices mean faster performance, leveraging tensor parallelism and high-speed synchronization over Ethernet.
    COPY-PASTE FIX
    Distributed Llama transforms your home devices into a powerful cluster for LLM inference. It's uniquely designed for consumer-grade hardware, leveraging tensor parallelism and high-speed synchronization over standard Ethernet to deliver faster performance than single-device setups.
  • highhomepage#2
    Add the project's homepage URL to the repository's 'About' section

    Why:

    COPY-PASTE FIX
    https://n4no.com/projects/distributedLlama/
  • mediumtopics#3
    Expand repository topics to include terms related to consumer hardware and edge AI

    Why:

    CURRENT
    distributed-computing, distributed-llm, llama2, llama3, llm, llm-inference, llms, neural-network, open-llm
    COPY-PASTE FIX
    distributed-computing, distributed-llm, llama2, llama3, llm, llm-inference, llms, neural-network, open-llm, consumer-hardware, edge-ai, home-lab, local-llm

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 b4rtaz/distributed-llama
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
ray-project/ray
Recommended in 3 of 2 queries
COMPETITOR LEADERBOARD
  1. ray-project/ray · recommended 3×
  2. vllm-project/vllm · recommended 2×
  3. microsoft/DeepSpeed · recommended 2×
  4. TimDettmers/bitsandbytes · recommended 1×
  5. PanQiWei/AutoGPTQ · recommended 1×
  • CATEGORY QUERY
    How to speed up large language model inference by connecting multiple home devices?
    you: not recommended
    AI recommended (in order):
    1. bitsandbytes (TimDettmers/bitsandbytes)
    2. AutoGPTQ (PanQiWei/AutoGPTQ)
    3. ExLlamaV2 (turboderp/exllamav2)
    4. Hugging Face Transformers (huggingface/transformers)
    5. vLLM (vllm-project/vllm)
    6. llama.cpp (ggerganov/llama.cpp)
    7. FlashAttention (Dao-AILab/flash-attention)
    8. DeepSpeed (microsoft/DeepSpeed)
    9. Hugging Face Accelerate (huggingface/accelerate)
    10. Ray (ray-project/ray)
    11. Ray Serve (ray-project/ray)
    12. RunPod
    13. Vast.ai

    AI recommended 13 alternatives but never named b4rtaz/distributed-llama. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What tools allow distributing LLM inference across a cluster of consumer-grade hardware?
    you: not recommended
    AI recommended (in order):
    1. vLLM (vllm-project/vllm)
    2. Ray (ray-project/ray)
    3. DeepSpeed-MII (microsoft/DeepSpeed)
    4. TGI (huggingface/text-generation-inference)
    5. OpenLLM (bentoml/OpenLLM)
    6. BentoML (bentoml/BentoML)
    7. Kubernetes (kubernetes/kubernetes)
    8. KServe (kserve/kserve)
    9. Seldon Core (SeldonIO/seldon-core)

    AI recommended 9 alternatives but never named b4rtaz/distributed-llama. 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 b4rtaz/distributed-llama?
    pass
    AI did not name b4rtaz/distributed-llama — 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 b4rtaz/distributed-llama in production, what risks or prerequisites should they evaluate first?
    pass
    AI named b4rtaz/distributed-llama 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 b4rtaz/distributed-llama solve, and who is the primary audience?
    pass
    AI named b4rtaz/distributed-llama explicitly

    AI answers can be confidently wrong. Read for accuracy: does it match your actual tech stack, audience, and differentiator?

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b4rtaz/distributed-llama — 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