All three deliverables of AI browser for complete starter kit

 

All three deliverables of AI browser so you have a complete starter kit:

1. Ready-to-run script (bash) for downloading & quantizing Hugging Face models
2. Hardware shopping list (GPU, storage, CPU, PSU) with approximate pricing
3. JSON catalog template to keep track of models

1. Bash Script — Download & Quantize Models

This script assumes:

• You have huggingface-cli installed and logged in (huggingface-cli login)
• You have llama.cpp tools installed (quantize, convert-llama-gguf.py, etc.)
• You’re storing models in ~/models/

#!/bin/bash
# Script: get_models.sh
# Purpose: Download + quantize multiple 

Hugging Face models for LocalAI/Ollama

# Where to store models
MODEL_DIR=~/models
mkdir -p $MODEL_DIR

# Example list of 

Hugging Face repos (add more as needed)
MODELS=(
  "meta-llama/Llama-2-7b-chat-hf"
  "mistralai/Mistral-7B-Instruct-v0.2"
  "google/gemma-7b"
  "TheBloke/vicuna-7B-v1.5-GGUF"
  "TheBloke/mixtral-8x7b-instruct-GGUF"
)

# Loop: download, convert, quantize
for repo in "${MODELS[@]}"; do
  echo ">>> Processing $repo"
  NAME=$(basename $repo)

  # Download from HF
  huggingface-cli repo download 

$repo --local-dir $MODEL_DIR/$NAME

  # Convert to GGUF (example 

for llama-based models)
  if [[ -f "$MODEL_DIR/$NAME/

pytorch_model.bin" ]]; then
    echo ">>> Converting $NAME to GGUF..."
    python3 convert-llama-gguf.py 

$MODEL_DIR/$NAME --outfile 

$MODEL_DIR/$NAME/model.gguf
  fi

  # Quantize (4-bit for storage efficiency)
  if [[ -f "$MODEL_DIR/$NAME/model.gguf" ]];

 then
    echo ">>> Quantizing $NAME..."
    ./quantize $MODEL_DIR/$NAME/model.gguf 

$MODEL_DIR/$NAME/model-q4.gguf Q4_0
  fi
done

echo ">>> All models processed. 

Stored in $MODEL_DIR"

👉 This script will give you ~5 models. Expand MODELS=( … ) with more Hugging Face repos until you hit 200+ total. Use quantized versions (-q4.gguf) for storage efficiency.

2. Hardware Shopping List

This setup balances cost, performance, and storage for hosting 200+ quantized models.

Component	Recommendation	Reason	Approx. Price (USD)
GPU	NVIDIA RTX 4090 (24GB VRAM)	Runs 13B models comfortably, some 70B with offload	$1,600–$2,000
Alt GPU (budget)	RTX 4080 (16GB)	Good for 7B models, limited for 13B+	$1,000–$1,200
CPU	AMD Ryzen 9 7950X / Intel i9-13900K	Multi-core, helps with CPU inference when GPU idle	$550–$650
RAM	64GB DDR5	Smooth multitasking + local inference	$250–$300
Storage	2TB NVMe SSD (PCIe Gen4)	Stores ~400 quantized models (avg 4–5GB each)	$120–$180
Alt storage	4TB HDD + 1TB NVMe	HDD for bulk storage, SSD for active models	$200–$250
PSU	1000W Gold-rated	Supports GPU + CPU safely	$150–$200
Cooling	360mm AIO liquid cooler	Keeps CPU stable under long inference	$150–$200
Case	Mid/full tower ATX	Good airflow for GPU + cooling	$120–$180

👉 If you don’t want to buy hardware: Cloud option — rent an NVIDIA A100 (80GB) VM (~$3–$5/hour). For batch evaluation of hundreds of models, it’s cheaper to spin up a VM for a day and shut it down.

3. JSON Catalog Template (Track 200+ Models)

This catalog helps you track local + hosted models, their paths, and notes.

{
  "models": [
    {
      "name": "Llama-2-7B-Chat",
      "provider": "Local",
      "path": "~/models/Llama-2-7b-chat-hf/

model-q4.gguf",
      "size_gb": 3.8,
      "type": "Chat/General",
      "strengths": "Conversational,

 general Q&A",
      "weaknesses": "Limited reasoning depth"
    },
    {
      "name": "Mistral-7B-Instruct-v0.2",
      "provider": "Local",
      "path": "~/models/

Mistral-7B-Instruct-v0.2/

model-q4.gguf",
      "size_gb": 4.1,
      "type": "Instruction-following",
      "strengths": "Fast, reliable 

instructions",
      "weaknesses": "Less creative generation"
    },
    {
      "name": "GPT-4o",
      "provider": "OpenAI API",
      "path": "https://api.openai.com/v1",
      "size_gb": null,
      "type": "Hosted",
      "strengths": "Advanced reasoning, 

multimodal",
      "weaknesses": "Token cost, API dependency"
    },
    {
      "name": "Claude 3.5",
      "provider": "Anthropic API",
      "path": "https://api.anthropic.com/v1",
      "size_gb": null,
      "type": "Hosted",
      "strengths": "Strong long-context 

reasoning",
      "weaknesses": "Subscription required"
    }
  ]
}

👉 Add entries as you download/quantize models or add hosted endpoints. This makes it easy to see at a glance how many total models you have (local + hosted), their size, and their strengths.

✅ With these 3 components, you now have:

• A script to build your own 200+ model library
• A hardware plan to run them effectively
• A catalog system to stay organized