The Local AI Purity Test

VRAM 0.0 / 24.0 GB24 GB card

Your GPU: 38°C

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

Caution: This is not a bucket list. Your electricity bill can't take it.

The Local AI Purity Test

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

Caution: This is not a bucket list. Your electricity bill can't take it.

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Device

Memory

How many

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 rigA quick power upgrade to the house.
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. Stopped using the existing recipes and started cooking your own on a DGX SparkThe full arc.
79. Stopped trusting benchmarks after a model you loved scored badly
80. Plugged your idle Mac into Darkbloom while you sleptYour Mac has a side hustle now.
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. Bought a second DGX SparkThen a third. It's fine.
92. Have a model running right nowWhich one? Be specific.
93. Opened a terminal at a party to show someone your local model
94. Own 4 or more DGX SparksSome people stopped at 3. Some people are building The All Spark.
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. 👋

0 checked

Made with ❤️ by Pratik

Open source on GitHub

0 cloud calls

$ No cloud was used to calculate your score. It's a count of boxes.

Inspired by the work of Tyler and Sarah on sfpurity.com.

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