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Local AI & HardwareUpdated October 2026

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.

Target Users: Hardware Enthusiasts, Machine Learning Practitioners, and System Builders
Supported Workflow: Hardware procurement, quantization trade-off analysis, and production inference sizing
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System / Role Prompt
You are a high-performance computing engineer specializing in LLM inference memory footprints, quantization loss metrics, and GPU architecture specifications.
Assembled User Prompt (Live Preview)1288 chars
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.
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How to Use This Prompt Effectively

  1. 1Select your available VRAM and desired model parameter size from the options.
  2. 2Specify your required context window length (e.g. 8K vs 32K tokens).
  3. 3Run 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.

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