AI Model VRAM & Hardware Estimator
Calculate exact GPU VRAM and Unified Memory requirements to run modern LLMs locally (Llama 3.3, DeepSeek-R1, Qwen 2.5, FLUX) across quantized weights, KV cache, and context window lengths.
Model & Runtime Configuration
REACTIVE ENGINEMinimum dedicated VRAM / Unified RAM needed
Calculating feasibility...
Adjust parameters to view execution verdict and hardware matching.
Compatible Hardware Tiers
ollama run llama3.1:8bHow VRAM Calculation Works (Technical Formula)
1. Weight Memory
Calculated as Params (Billion) × Bits_Per_Weight ÷ 8. A 70B parameter model in FP16 precision requires roughly 140 GB just for weights, whereas in 4-bit (Q4_K_M) it compresses down to ~38.5 GB without significant loss of reasoning capability.
2. KV Cache Scaling
Attention mechanisms store Key-Value pairs for every token in the prompt. Modern architectures like Llama 3 and DeepSeek use Grouped-Query Attention (GQA) to reduce KV cache size by 4x to 8x compared to classical Multi-Head Attention (MHA).
3. CUDA Scratchpad
Operating systems and runtime engines (llama.cpp, vLLM, TensorRT-LLM) require a constant memory footprint (1.0 GB to 1.5 GB) for context buffers, memory allocation maps, and activation layers during inference passes.