llm ram calculator
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🧠 PRO LLM RAM & VRAM CALCULATOR
ESTIMATE MEMORY REQUIREMENTS FOR LOCAL AI MODELS
1. LLM CONFIGURATION
2. MEMORY REQUIREMENTS
TOTAL RECOMMENDED VRAM / RAM
0.00 GB
SAFE ZONE MARGIN INCLUDED
MODEL WEIGHTS (0%)
KV CACHE (0%)
CUDA OVERHEAD (0%)
MODEL WEIGHTS ONLY
0.00 GB
KV CACHE (CONTEXT)
0.00 GB
💻 MINIMUM HARDWARE RECOMMENDATION
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💾 WHAT IS QUANTIZATION?
Standard models use 16-bit (fp16) precision, taking ~2 bytes per parameter. Quantization (e.g., 4-bit GGUF/AWQ) compresses the model to use 0.5 bytes per parameter. This allows you to run huge models like 70B on consumer hardware with minimal quality loss.
🧠 KV CACHE EXPLAINED
The Context Window (how much text the AI remembers at once) is stored in the KV (Key-Value) Cache. If you run a model at 128K context, the KV cache alone can consume more RAM than the entire model itself!
POPULAR CONSUMER GPUs FOR AI
| GRAPHICS CARD (GPU) | VRAM CAPACITY | BEST FOR MODELS |
|---|---|---|
| NVIDIA RTX 4090 / 3090 | 24 GB | 8B (fp16), 14B (8-bit), 32B/70B (4-bit) |
| NVIDIA RTX 4080 / 4070 Ti SUPER | 16 GB | 8B (8-bit), 14B (4-bit) |
| NVIDIA RTX 4070 / 3060 (12G) | 12 GB | 8B (4-bit/8-bit) |
| NVIDIA RTX 4060 / 3060 Ti | 8 GB | 7B/8B (4-bit restricted context) |
| APPLE MAC STUDIO M2 ULTRA | 64GB - 192GB (Unified) | Giant Models: 70B (8-bit), 104B (4-bit) |