RRepoGEO

REPOGEO REPORT · LITE

jjang-ai/vmlx

Default branch main · commit 52fcd4af · scanned 6/15/2026, 8:16:52 PM

GitHub: 669 stars · 70 forks

AI VISIBILITY SCORE
40 /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
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 jjang-ai/vmlx, 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
  • highabout#1
    Clarify the repository's primary function in the 'About' description

    Why:

    CURRENT
    vMLX - JANGTQ Uber Compressed MLX Models - L2 Disk Cache (survives restart) + L1 Paged (super fast ttft) + Hybrid SSM Scheduler + Cont Batching + etc!
    COPY-PASTE FIX
    vMLX: A high-performance, self-hosted MLX inference server for LLMs, VLMs, and image generation on Apple Silicon, featuring Uber Compressed MLX Models, L2 Disk Cache, L1 Paged KV Cache, Hybrid SSM Scheduler, and Continuous Batching.
  • highreadme#2
    Add a prominent, direct statement to the README clarifying its role and correcting miscategorization

    Why:

    COPY-PASTE FIX
    **vMLX is a high-performance, self-hosted MLX inference server for Apple Silicon, *not* a vector database.** It enables running LLMs, VLMs, and image generation models locally with advanced KV cache optimizations and an OpenAI/Anthropic/Ollama compatible HTTP API.
  • mediumtopics#3
    Add more explicit category-level topics for LLM inference servers

    Why:

    CURRENT
    anthropic-api, kvcache-compression, kvcache-optimization, kvcache-reuse, llm, lmstudio, macbook, mcp-server, mlx, mlxllm, mlxstudio, omlx, omlx-alternative, openai-api, openclaw, openclaw-agent, persistent-memory, prefix-cache, vmlx
    COPY-PASTE FIX
    anthropic-api, kvcache-compression, kvcache-optimization, kvcache-reuse, llm, llm-inference-server, local-llm, apple-silicon-llm, inference-engine, lmstudio, macbook, mcp-server, mlx, mlxllm, mlxstudio, omlx, omlx-alternative, openai-api, openclaw, openclaw-agent, persistent-memory, prefix-cache, vmlx

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 jjang-ai/vmlx
Avg rank
—
Lower is better. #1 = top recommendation.
Share of voice
0%
Of all named tools, what % are you?
Top rival
LM Studio
Recommended in 2 of 2 queries
COMPETITOR LEADERBOARD
  1. LM Studio · recommended 2×
  2. ollama/ollama · recommended 1×
  3. janhq/jan · recommended 1×
  4. ggerganov/llama.cpp · recommended 1×
  5. abetlen/llama-cpp-python · recommended 1×
  • CATEGORY QUERY
    How can I run self-hosted large language model inference locally on my Apple Silicon Mac?
    you: not recommended
    AI recommended (in order):
    1. Ollama (ollama/ollama)
    2. LM Studio
    3. Jan (janhq/jan)
    4. llama.cpp (ggerganov/llama.cpp)
    5. llama-cpp-python (abetlen/llama-cpp-python)
    6. MLC LLM (mlc-ai/mlc-llm)

    AI recommended 6 alternatives but never named jjang-ai/vmlx. This is the gap to close.

    Show full AI answer
  • CATEGORY QUERY
    What are the best local LLM inference servers with optimized KV cache for Apple Silicon devices?
    you: not recommended
    AI recommended (in order):
    1. llama.cpp
    2. MLX
    3. Ollama
    4. LM Studio
    5. Text Generation WebUI (oobabooga/text-generation-webui)

    AI recommended 5 alternatives but never named jjang-ai/vmlx. 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 jjang-ai/vmlx?
    pass
    AI named jjang-ai/vmlx explicitly

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

  • If a team adopts jjang-ai/vmlx in production, what risks or prerequisites should they evaluate first?
    pass
    AI named jjang-ai/vmlx 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 jjang-ai/vmlx solve, and who is the primary audience?
    pass
    AI named jjang-ai/vmlx explicitly

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

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jjang-ai/vmlx — 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