LLM GPU VRAM, KV-Cache & Quantization Sizing Calculator
Estimate exact GPU memory requirements (GiB) across model weights, Group-Query Attention (GQA) KV-cache growth at long contexts, and CUDA activation buffers for self-hosted vLLM, llama.cpp, and Ollama deployments.
VRAM Breakdown & Hardware Fit Matrix
GPU & Unified Memory Cluster Compatibility
| Hardware Target | Available VRAM | Min Cards Needed | Headroom / Status |
|---|
Ready-to-Run vLLM Serving Command
How LLM GPU VRAM Scales: Weights vs. GQA KV-Cache & CUDA Overhead
Sizing GPU infrastructure solely by multiplying parameter count by quantization width is the number-one cause of unexpected CUDA Out-Of-Memory (OOM) crashes in production inference clusters. Total GPU memory consumption is the sum of three distinct allocations: static model weights (including block-scale metadata of ~4% for sub-8-bit GGUF/AWQ formats), dynamic Key-Value (KV) attention cache, and runtime activation + CUDA/NCCL workspace buffers.
While modern Transformer architectures use Grouped-Query Attention (GQA) or Multi-Head Latent Attention (MLA) to shrink KV-cache footprints by 4× to 8× compared to classic Multi-Head Attention, KV-cache still scales linearly with both context length and concurrent batch size: 2 × layers × hidden_dim × (kv_heads / q_heads) × bytes_per_kv × context_tokens × batch_size. At 64K–128K context across multiple concurrent users, KV-cache routinely rivals or exceeds the quantized model weights themselves—making FP8 KV-cache quantization (--kv-cache-dtype fp8 in vLLM) essential on 24 GB–80 GB accelerators. Planning to stream inference tokens through a reverse proxy or download multi-hundred-gigabyte Hugging Face checkpoints? Check our Nginx SSE Streaming Config Generator and Checkpoint Transfer Bandwidth Calculator.