Tier-1 Engineering Sizing Tool

LLM VRAM & Hardware Calculator

Compute GPU VRAM and Unified Memory required to serve any open-weights model. Mathematically accounts for model weights, KV cache context expansion, and CUDA overhead.

1. Select Model Architecture & Parameter Scale

2. Weight Quantization Precision (Bits Per Weight)

FP16 (Unquantized - 16-bit)

Frontier precision, maximum quality.

Q8_0 (8-bit Quantized)

Near-zero perplexity loss, 50% VRAM saving.

Q4_K_M (4.5-bit Medium - Recommended)

Gold standard for local inference speed & accuracy.

Q3_K_M (3.5-bit Small)

Maximum compression for budget GPUs.

3. Target Context Window (Tokens)

KV Cache Quantization:

Exact Mathematical Formula & Engineering Assumptions

1. Model Weights Memory:Weights (GB) = (Params × 10⁹ × (BPW / 8)) / 1024³

2. KV Cache Context Memory:KV Cache (GB) = (2 × Layers × KV_Heads × Head_Dim × Context × Bytes) / 1024³

3. Total Allocation Buffer:Total VRAM (GB) = Weights + KV_Cache + 1.25 GB (CUDA Runtime + PyTorch Context)

Disclaimer: Results are technical engineering estimates based on standard FlashAttention-2 / PagedAttention vLLM and Ollama GGUF runtimes. Actual VRAM may vary depending on driver version, continuous batching concurrency, and GPU architecture.

Computed Sizing Verdict

Total VRAM Required

6.44 GB

Minimum GPU / Unified Memory buffer

Model Weights:4.19 GB

KV Cache (8,192 tok):1 GB

CUDA & Context Overhead:1.25 GB

Recommended Local Hardware:

NVIDIA RTX 3060 12GB or Apple Mac M-Series (16GB Unified)

Recommended Cloud Instance:

AWS g5.xlarge (1x A10G 24GB) or RunPod RTX 4090 ($0.34/hr)

Instant Terminal Launch Snippet:

ollama run llama3.1:8b