GPU & Hardware Last Updated Date:

LLM VRAM Calculator: Check How Much GPU Memory You Need (7B to 671B Models)

Find out how many gigabytes of GPU VRAM you need to host Llama 3, Qwen, Mistral, or DeepSeek-R1 across FP16, FP8, INT8, and 4-bit quantization.

Minimum Required GPU VRAM
84.2 GB VRAM

How to Estimate Your Monthly Bill With This LLM VRAM Calculator

Use the free LLM VRAM Calculator above to see what your monthly bill will look like before you scale up traffic. Just pick your plan or model tier and type in your expected usage—the calculator updates your cost breakdown right away.

Want to bring that monthly bill down to $0? Check our free startup credit guide or browse ready-to-use accounts in our Pre-Approved Credit Marketplace.

1. 2026 Pricing & Rate Table

Plan / Item 2026 Rate & Included Quota
FP16 / BF16 (16-bit)2 bytes per parameter (70B model ≈ 140 GB weights + KV cache)
FP8 / INT8 (8-bit)~1.05 bytes per parameter (70B model ≈ 73.5 GB weights + KV cache)
INT4 / AWQ / GGUF (4-bit)~0.58 bytes per parameter (70B model ≈ 40.6 GB weights + KV cache)
KV Cache & CUDA OverheadAdds 10% to 35%+ extra VRAM based on context length and batch size

2. Easy Ways to Lower Your Monthly Cost

  • For a quick 4-bit estimate with an 8K context window, multiply the model size in billions by 0.7 (70B × 0.7 ≈ 49 GB VRAM).
  • A 70B model in 4-bit fits comfortably on two 24GB GPUs (48GB total) or a single 80GB A100/H100 GPU.
  • Long context windows (32K to 128K) increase KV cache memory fast—turn on KV cache quantization in vLLM to save memory.
  • Claim free GPU credits from Modal, Vultr, or DigitalOcean on StartupCreditHub before renting servers at full price.

Frequently Asked Questions — LLM VRAM Calculator

How is LLM GPU VRAM calculated?

Total inference VRAM = Model Weights (Parameters × Bytes per Parameter) + KV Cache Memory (which scales linearly with Context Length × Batch Size) + ~8%–10% CUDA kernel & activation overhead.

Can I run Llama 3.3 70B on a single 24GB GPU?

No. Even at 4-bit quantization (INT4 / Q4_K_M), a 70B model requires ~40.5 GB just for the weights plus 4–8 GB for the KV cache (~46 GB total). You need at least 2x 24GB GPUs (48GB total) or 1x 48GB/80GB GPU.

How much VRAM does DeepSeek-R1 (671B) require?

At 4-bit quantization, DeepSeek-R1 671B requires roughly 390–420 GB of VRAM (typically 6x or 8x 80GB H100/A100 GPUs). At FP8, it requires an 8x H200 (141GB) node or two 8x H100 nodes.

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