LLM GPU VRAM, Weight Footprint & KV-Cache Memory Sizing Calculator

Size GPU VRAM for FP16, INT8, or INT4 LLMs across batch size and context length.

Total GPU VRAM Required
45.54 GB
Quantized Model Weights Footprint 35 GB
KV-Cache Context Memory 5.14 GB
CUDA Context & Activation Buffer 5.4 GB
Inputs
B params
Model parameter count in billions (e.g., 8B, 32B, 70B).
bits
Weight precision in bits per parameter (16=FP16/BF16, 8=INT8/FP8, 4=INT4/AWQ).
tokens
Active context window length per sequence in tokens.
seqs
Number of concurrent active sequences in batch.
Results & Graph
Total GPU VRAM Required
45.54 GB
Quantized Model Weights Footprint
35 GB
KV-Cache Context Memory
5.14 GB
CUDA Context & Activation Buffer
5.4 GB

Total GPU VRAM Required

Graph 1 • Parameters Billions Sensitivity Recalculates the domain equation across 50% to 150% of the primary input while holding other parameters constant.
0 GB 35.8 GB 71.6 GB 50%: 24.43 GB 24.4 50% 75%: 35.05 GB 35.0 75% 100%: 45.54 GB 45.5 100% 125%: 55.95 GB 56.0 125% 150%: 66.3 GB 66.3 150%
Graph 2 • Primary & Supporting Metric Comparison Compares the primary output alongside supporting domain benchmarks.
Total GPU VRAM Required 45.54 GB Quantized Model Weights Footprint 35 GB KV-Cache Context Memory 5.14 GB CUDA Context & Activation Buffer 5.4 GB
Graph 3 • Exact Output Table & Units Displays each calculated output in its exact domain unit.
Total GPU VRAM Required45.54 GBQuantized Model Weights Footprint35 GBKV-Cache Context Memory5.14 GBCUDA Context & Activation Buffer5.4 GB
Summary: A 70B model at 4-bit precision with 4×8192 token context requires 45.54 GB VRAM (35 GB weights + 5.14 GB KV-cache + 5.4 GB runtime overhead).
Summary: A 70B model at 4-bit precision with 4×8192 token context requires 45.54 GB VRAM (35 GB weights + 5.14 GB KV-cache + 5.4 GB runtime overhead).
⚡ Send result to: API Token Cost →Subnet & Password Entropy →Aspect Ratio & Storage →
Interactive chart & math engine powered by CalculatorGraph

Formula & Mathematical Methodology: LLM VRAM & KV-Cache Systems Engineering Formula

VRAMGB = ParamsB × (Bits / 8) × 1.15 + KVCacheGB(ContextTokens × Batch)
VRAM_GB = Params_B × (Bits / 8) × 1.15 + KV_Cache_GB(ContextTokens × Batch)
  • parametersBillions: Parameters Billions (B params)
  • quantizationBits: Quantization Bits (bits)
  • contextLengthTokens: Context Length Tokens (tokens)
  • concurrentBatchSize: Concurrent Batch Size (seqs)

Rounding: Binary/decimal capacities and engineering metrics retain full floating-point precision and round to 2 decimal places.

Worked Example: Architecture Sizing Example: LLM VRAM & KV-Cache

Size GPU VRAM for FP16, INT8, or INT4 LLMs across batch size and context length.

  1. Enter the workload parameters, model dimensions, or network link specifications.
  2. Compute the sizing equation: VRAM_GB = Params_B × (Bits / 8) × 1.15 + KV_Cache_GB(ContextTokens × Batch).
  3. Inspect the primary capacity metric, headroom buffers, and 3 SVG visualizations.
Result: Calculates LLM VRAM & KV-Cache via VRAM_GB = Params_B × (Bits / 8) × 1.15 + KV_Cache_GB(ContextTokens × Batch).

Frequently Asked Questions

How does the LLM GPU VRAM, Weight Footprint & KV-Cache Memory Sizing Calculator compute capacity?

It evaluates VRAM_GB = Params_B × (Bits / 8) × 1.15 + KV_Cache_GB(ContextTokens × Batch) directly in your browser and plots three custom SVG diagrams.

Can I compare different cluster or model configurations side by side?

Yes. Use the "Compare (0/3)" button in the results panel to pin up to three configurations.

Does this tool upload any of my architecture parameters?

No. All calculations run 100% client-side in your browser.

Developer Tools · AI & LLM Infrastructure

LLM GPU VRAM, Weight Footprint & KV-Cache Memory Sizing Calculator: inputs, method, and how to read the result

Size GPU VRAM for FP16, INT8, or INT4 LLMs across batch size and context length.

Choose inputs for the case you are checking

The defaults are illustrative. Replace them with values for your case and keep the units shown beside each field.

  • Parameters Billions: Model parameter count in billions (e.g., 8B, 32B, 70B).
  • Quantization Bits: Weight precision in bits per parameter (16=FP16/BF16, 8=INT8/FP8, 4=INT4/AWQ).
  • Context Length Tokens: Active context window length per sequence in tokens.
  • Concurrent Batch Size: Number of concurrent active sequences in batch.

What the calculation includes

With the default values shown, Total GPU VRAM Required is 45.54 GB. The calculation follows LLM VRAM & KV-Cache Systems Engineering Formula; the formula and variable definitions above show how the inputs combine.

One-input check: With the other defaults held constant, changing Parameters Billions from 70 B params to 77 B params changes Total GPU VRAM Required from 45.54 GB to 49.71 GB. This is a comparison inside the calculator's stated model, not a prediction.

Where to use caution

  • Uses industry-standard IEEE, IETF RFC, and GPU/cloud hardware sizing conventions.
  • Capacity planning estimate; benchmark under production traffic patterns before finalizing cluster or hardware procurement.

Scope: IEEE / IETF / Cloud Systems Engineering Standard Check the assumptions and limitations against your situation before relying on the estimate.

More in Developer Tools: API Token Cost Calculator, IPv4 CIDR Subnet, Password Entropy & URL/Base64 Encoding Calculator, Aspect Ratio, Image Dimensions & Storage Converter.