LLM VRAM & GPU Memory Calculator
Estimate exact GPU VRAM requirements for Llama 3, DeepSeek, Mistral, and custom Transformer models based on parameters, quantization precision, KV cache, and batch sizes.
⚙️ Model Parameters & Config
Model Parameters (Billions)
8B
Model Weight Precision
Context Length (Tokens)
8,192
Concurrent Batch Size
1
KV Cache Precision
📊 Memory Allocation Breakdown
Model Weights Memory
Static network parameter footprint
4.00 GB
KV Cache Memory
Dynamic key-value context buffer
0.26 GB
CUDA & Activation Overhead
PyTorch / CUDA runtime buffer (~20%)
0.85 GB
Total Estimated VRAM Required
5.11 GB
💡 Suitable GPU: 1x RTX 3060 (12GB) / RTX 4060 (16GB)
Frequently Asked Questions (FAQ)
Q1: How is LLM VRAM calculated for inference?
Total VRAM is the sum of three components: $\text{Total VRAM} = \text{Model Weights} + \text{KV Cache} + \text{CUDA Activation Overhead}$. For a 4-bit quantized 70B model, model weights require $\sim 35 \text{ GB}$, while KV cache scales dynamically with batch size and context window length.
Q2: How much VRAM does INT4 vs FP16 save?
INT4 (4-bit AWQ / GPTQ) quantization consumes $0.5 \text{ bytes per parameter}$, reducing model weight RAM footprint by 75% compared to FP16 ($2.0 \text{ bytes per parameter}$). This allows a 70B model to fit onto a single 48GB GPU (like an NVIDIA A6000 or 2x RTX 3090s).
Q3: What is the impact of KV Cache on multi-user throughput?
As batch sizes and context lengths grow (e.g. 128k context), KV cache memory can exceed the model weight size itself. Systems like PagedAttention in vLLM allow non-contiguous memory allocation to prevent fragmentation.
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