The Local AI Purity Test

The official purity test for people who run open-weight models at home. Tick everything you've done.

Caution: This is not a bucket list. Completing every item makes you a GPU-poor loser.

• 1. Checked how much VRAM a laptop had before checking anything else
• 2. Bought a GPU "for gaming" and never launched a game
• 3. Bought a used RTX 3090 for the VRAMIt was mined on. It's fine. It's fine.
• 4. Owned two or more 3090s at the same time
• 5. Ran one model split across GPUs with different VRAM sizes
• 6. Bought a Mac with 64GB+ unified memory "for local inference"
• 7. Bought a Mac with 512GB of unified memoryFor productivity. Obviously.
• 8. Clustered multiple Macs or PCs to run one model
• 9. Bought a DGX Spark or a Strix Halo (Ryzen AI Max) mini PC
• 10. Priced out an RTX Pro 6000 and closed the tabThe cart is still open in another tab.
• 11. Used PCIe risers or an open-air mining frame
• 12. Tripped a breaker or needed a new circuit for your rigYour landlord has questions.
• 13. Power-limited or undervolted your GPUs
• 14. Know your GPU's memory bandwidth by heartNobody asked. You told them anyway.
• 15. Made a tokens-per-second-per-dollar spreadsheet
• 16. Called yourself "GPU poor"Usually said by someone with two 4090s.
• 17. Rented a cloud GPU to run a "local" modelLocal to the datacenter, technically.
• 18. Ran an LLM on a Raspberry Pi just to prove you could
• 19. Ran an LLM on your phone
• 20. Ran an LLM entirely in the browser with WebGPU
• 21. Installed Ollama
• 22. Left Ollama for llama.cppAnd you tell people about it.
• 23. Compiled llama.cpp from source
• 24. Pulled a new llama.cpp build more than once in one dayThree new builds dropped while you read this.
• 25. Used LM Studio
• 26. Used MLX on a Mac
• 27. Fought CUDA versions to get vLLM or SGLang running
• 28. Used text-generation-webui (oobabooga)
• 29. Set up Open WebUI
• 30. Used KoboldCpp or SillyTavern
• 31. Wrote an Ollama Modelfile
• 32. Fixed a broken chat template by handJinja. In this economy.
• 33. Pointed a coding agent (Claude Code, Cline, Aider) at a local endpoint
• 34. Used a local model as your coding agent for a week, then went backIt was fine. It was not fine.
• 35. Hit "CUDA out of memory" and cut the context instead of the model size
• 36. Quantized your KV cache
• 37. Tried speculative decoding
• 38. Built a local RAG over your own notes
• 39. Ran a home AI server on Proxmox or Unraid
• 40. Wired a local LLM into Home Assistant
• 41. Ran Whisper locally
• 42. Have a ComfyUI workflow that looks like spaghetti
• 43. Offloaded MoE experts to CPU to fit a model that shouldn't fitIt shouldn't fit. It fits. Barely.
• 44. Know what Q4_K_M means without googling
• 45. Argued about imatrix quants
• 46. Downloaded a GGUF from TheBloke🫡
• 47. Downloaded quants from bartowski or Unsloth
• 48. Downloaded a model on release day and the GGUF was broken
• 49. Re-downloaded the "fixed" GGUF the next day
• 50. Ran a 2-bit or lower quant of a giant model and called it usable
• 51. Ran DeepSeek R1 671B locally, at any speed0.8 tokens per second. Worth it.
• 52. Ran a model at under 1 token/sec and called it fineThis is fine.
• 53. Have 1TB+ of model weights on diskAnd you've used 3 of them.
• 54. Ran out of disk space mid-download
• 55. Asked a model how many r's are in "strawberry"There are 2 r's in strawberry. —every model, 2024
• 56. Asked a model to draw an SVG of a pelican riding a bicycle
• 57. Used an abliterated or uncensored model
• 58. Downloaded a model with "-v2-final-fixed" in the name-v2-final-fixed-REAL-use-this-one.gguf
• 59. Fine-tuned a LoRA
• 60. Fine-tuned with Unsloth on a free Colab
• 61. Merged models with mergekit
• 62. Uploaded a model to Hugging Face
• 63. Uploaded a GGUF to Hugging Face
• 64. Your HF models have more downloads than you have followersBig on Hugging Face. Unknown at dinner.
• 65. Said "Qwen is cooking"Qwen is always cooking.
• 66. Ran gpt-oss locally and had opinions
• 67. Refreshed a Hugging Face org page waiting for a release
• 68. Lurked r/LocalLLaMA every day
• 69. Posted your rig on r/LocalLLaMA
• 70. Commented "GGUF when?"Soon™.
• 71. Asked "will it run on my 8GB card?"It will not.
• 72. Said "not your weights, not your model"
• 73. Starred llama.cpp on GitHub
• 74. Opened an issue on llama.cpp, Ollama, or vLLM
• 75. Got a PR merged into llama.cpp, Ollama, or vLLMRespect. Genuinely.
• 76. Followed Georgi Gerganov on X
• 77. Watched a "run DeepSeek locally in 5 minutes" YouTube videoIt took 5 hours.
• 78. Built your own vibe eval
• 79. Stopped trusting benchmarks after a model you loved scored badly
• 80. Tested a model on OpenRouter before downloading it
• 81. Explained quantization to a non-tech friend at dinnerThey did not ask for a second dinner.
• 82. Said "privacy" was the reason when it was really because it's cool
• 83. Still pay for ChatGPT, Claude, or GeminiFor comparison. Obviously.
• 84. Spent more on hardware than you'd spend on APIs in 5 yearsBut you own it.
• 85. Your electricity bill went up enough to notice
• 86. Heated a room with your GPUs in winterNot a bug. A feature.
• 87. Someone you live with complained about the fan noiseIt's white noise. It helps them sleep.
• 88. Named your rig
• 89. Said "I'll just run it locally" about what became a 3-weekend project
• 90. Ran a local model on a plane with no Wi-Fi and felt superiorThe person in 14C did not care.
• 91. Chose a laptop based on memory bandwidth
• 92. Have a model running right nowWhich one? Be specific.
• 93. Opened a terminal at a party to show someone your local model
• 94. Made a quant tier list
• 95. Tweaked temperature and top_p to "fix" a model
• 96. Have a folder named "models" and another named "models2"
• 97. Tried running a model bigger than your RAM with mmap and swap
• 98. Worried about your SSD's wear level
• 99. Bought hardware because of a single r/LocalLLaMA post
• 100. Took this test while a local model ran in the backgroundHi. 👋