GPU VRAM & Local LLM Quantization Planner
Plan your local AI hardware footprint with precision. Calculate weights, KV cache consumption across context sizes, and determine the exact quantization level required for full GPU offload.
Customize & Generate Your Prompt
Adjust the variables below. Your customized prompt updates live in real-time ready to copy with zero setup.
Please calculate the precise memory requirements and recommend the optimal quantization strategy for running a local LLM. ### Target Setup - **Total Dedicated GPU VRAM**: 12 GB (e.g. RTX 3060 12GB / RTX 4070 12GB) - **Target Model Architecture & Parameter Count**: 14B - 16B Parameters (e.g. Qwen2.5-14B, DeepSeek-14B) - **Desired Context Window (Tokens)**: 16K Tokens (16,384 tokens) - **Inference Engine & Preferred Format**: llama.cpp / GGUF with full GPU offload ### Calculation Directives 1. **Weight Memory Footprint**: - Calculate raw parameter weight size across standard quantization schemes (Q8_0, Q5_K_M, Q4_K_M, Q3_K_M, IQ2_XS). 2. **KV Cache Memory Footprint**: - Calculate KV cache memory required for **16K Tokens (16,384 tokens)** tokens using 16-bit float (FP16) vs 8-bit / 4-bit quantized KV cache. 3. **CUDA Runtime & Operating System Overhead**: - Deduct operating system display overhead (Windows DWM vs headless Linux) and CUDA context allocation (~0.8GB - 1.5GB). 4. **Feasibility Verdict & Recommended Model**: - Provide an unequivocal verdict: Can this model run with **100% layer offloading to GPU** at the requested context length? - If not, recommend the highest-accuracy alternative model that will run at maximum speed without PCIe swapping.
How to Use & Customize This Prompt
Fill in your target framework, environment rules, or source metrics in the interactive builder above. The assembled template updates instantly.
Click "Copy Prompt to Clipboard". You can copy the full assembled text or copy system instructions separately.
Refer to the Model Guidance below to choose the highest-performing frontier model or local runtime for this specific task.
Paste into your AI coding tool, chat interface, or local Ollama runtime. Verify syntax, types, and schema compliance against expected output.
Workflow Guidance
- Select your available VRAM and desired model parameter size from the options.
- Specify your required context window length (e.g. 8K vs 32K tokens).
- Run the prompt to get a complete memory budget calculation and optimal quantization tier.
Representative AI Output
Sample output produced when using frontier AI models with the default parameters:
### VRAM Budget Breakdown (12GB Total) - **Available Usable VRAM**: 10.8 GB (after OS & CUDA runtime reserve) - **Model Weights (Q4_K_M for 14B)**: ~8.9 GB - **16K Context KV Cache (FP16)**: ~2.1 GB - **Total Required VRAM**: 11.0 GB ### Verdict: TIGHT FIT / SLIGHT SPILLOVER At FP16 KV cache, a 16K context window will exceed 10.8GB by approximately 200MB, triggering slow system RAM offload. ### Recommended Strategy Enable **8-bit KV Cache Quantization** (`--cache-type-k q8_0 --cache-type-v q8_0`). This shrinks your KV cache from 2.1 GB to ~1.1 GB, allowing the entire model and 16K context to reside 100% on the GPU with full 45+ tokens/second throughput.
Pro Optimization & Edge-Case Tips
- KV cache memory grows linearly with context size and batch size. Doubling context from 8K to 16K doubles the memory consumed by the cache.
- Q4_K_M represents the sweet spot for perplexity retention on models between 7B and 70B.
- Consider enabling Flash Attention (`-fa` in llama.cpp) to significantly reduce intermediate activation VRAM.
Recommended Runtimes & Models for Local AI Hardware
Self-hosted inference engines and quantized open-weight models optimized for consumer GPUs.
Ollama Runtime
Local terminal setup, one-click model pulling, and background API serving on macOS, Linux, and Windows.
- Seamless Modelfile configuration for custom prompt templates
- Automatic GPU layer offloading based on available VRAM
- Native OpenAI-compatible HTTP endpoint for local web UIs
vLLM / llama.cpp
Production high-throughput serving, PagedAttention, and custom GGUF/EXL2 quantization.
- PagedAttention eliminates KV cache memory fragmentation
- Continuous batching supports concurrent API requests smoothly
- Unlocks custom flash-attention kernels for fast token generation
LM Studio
Visual model discovery from Hugging Face, side-by-side token benchmarks, and local GPU monitoring.
- Real-time VRAM allocation bar and token generation counter
- Zero-setup Hugging Face model search and direct GGUF downloading
- Preset temperature and top-p profiles for code vs chat
Copy your assembled prompt and test immediately with approved frontier models or your own API key.
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