LLM Inference & Training GPU VRAM Calculator (Weights + KV Cache + Quantization)

Size GPU VRAM (H100 / A100 / RTX 4090 count) for LLM weights, KV cache, and CUDA activations.

Total Required GPU VRAM
40.188 GB VRAM
Model Weights Footprint 35 GB
Active KV Cache Memory (FP16) 0.168 GB
24 GB GPUs (RTX 4090 / L4) Required 2 GPUs
Inputs
⚡ Scenarios:
B params
Total model parameters in Billions (e.g., 7B, 8B, 14B, 32B, 70B, 405B).
bits
Weight precision in bits (16 = FP16/BF16, 8 = FP8/INT8, 4 = INT4/AWQ/Q4_K_M).
tokens
Active sequence context length per request in tokens.
batch
Concurrent active sequences in the inference batch.
Results & Graph
Total Required GPU VRAM
40.188 GB VRAM
Model Weights Footprint
35 GB
Active KV Cache Memory (FP16)
0.168 GB
24 GB GPUs (RTX 4090 / L4) Required
2 GPUs

Total Required GPU VRAM

Graph 1 • LLM GPU VRAM & KV Cache • Domain Architecture & Live Telemetry Maps Model Parameters Billions, Quantization Bits Per Weight, Context Window Tokens through LLM GPU VRAM & KV Cache Formula.
Total Required GPU VRAM: 40.188 GB VRAM LLM GPU VRAM & KV Cache • Invariants & Metrics Total Required GPU VRAM 40.188 GB VRAM Model Weights Footprint 35 GB Active KV Cache Memory (FP16 0.168 GB 24 GB GPUs (RTX 4090 / L4) R 2 GPUs
Graph 2 • Total Required GPU VRAM Across Model Parameters Billions Sweep (40% → 160%) Evaluates Weights_GB = Params_B × (Bits / 8); KV_Cache_GB ≈ 0.00008 × Params_B × across 7 operating points (current input highlighted).
0 GB VRA 35.10 GB VRA 70.20 GB VRA 16.55 28 24.43 42 32.31 56 40.19 70 48.07 84 55.94 98 63.82 112
Graph 3 • Joint Sensitivity: Model Parameters Billions × Quantization Bits Per Weight Compares Total Required GPU VRAM when Quantization Bits Per Weight is shifted +25% (solid) vs. −25% (dashed).
0 GB VRA 35.61 GB VRA 71.22 GB VRA --- Quantization Bits Per Weight @ 75% 35.23 -30% 42.61 -15% 49.99 +0% 57.37 +15% 64.75 +30%
Summary: Serving a 70B model at 4-bit quantization requires 35 GB for weights + 0.17 GB KV cache (8,192 ctx × batch 4) + CUDA overhead = 40.2 GB VRAM (2× 24GB GPUs or 1× 80GB H100/A100).
Summary: Serving a 70B model at 4-bit quantization requires 35 GB for weights + 0.17 GB KV cache (8,192 ctx × batch 4) + CUDA overhead = 40.2 GB VRAM (2× 24GB GPUs or 1× 80GB H100/A100).
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Formula & Mathematical Methodology: LLM GPU VRAM & KV Cache Formula

WeightsGB = ParamsB × (Bits / 8)
KVCacheGB ≈ 0.00008 × ParamsB × (ContextTokens / 1024) × BatchSize
TotalVRAM = (WeightsGB + KVCacheGB) × 1.12
Weights_GB = Params_B × (Bits / 8); KV_Cache_GB ≈ 0.00008 × Params_B × (ContextTokens / 1024) × BatchSize; TotalVRAM = (Weights_GB + KV_Cache_GB) × 1.12
  • modelParametersBillions: Total model parameters in Billions (e.g., 7B, 8B, 14B, 32B, 70B, 405B).
  • quantizationBitsPerWeight: Weight precision in bits (16 = FP16/BF16, 8 = FP8/INT8, 4 = INT4/AWQ/Q4_K_M).
  • contextWindowTokens: Active sequence context length per request in tokens.
  • concurrentBatchSize: Concurrent active sequences in the inference batch.

Rounding: Internal floating-point calculations run in 64-bit IEEE-754 double precision; display values round to 2–4 decimal places.

Worked Example: LLM GPU VRAM & KV Cache Worked Example

Size GPU VRAM (H100 / A100 / RTX 4090 count) for LLM weights, KV cache, and CUDA activations.

  1. Enter the input measurement, system configuration, or astronomical parameter.
  2. Apply the standard conversion or analytical equation: Weights_GB = Params_B × (Bits / 8); KV_Cache_GB ≈ 0.00008 × Params_B × (ContextTokens / 1024) × BatchSize; TotalVRAM = (Weights_GB + KV_Cache_GB) × 1.12.
  3. Inspect the primary metric, secondary equivalents, and 3 real-time SVG graphs.
Result: Computes LLM GPU VRAM & KV Cache via Weights_GB = Params_B × (Bits / 8); KV_Cache_GB ≈ 0.00008 × Params_B × (ContextTokens / 1024) × BatchSize; TotalVRAM = (Weights_GB + KV_Cache_GB) × 1.12.

Frequently Asked Questions

How does the LLM Inference & Training GPU VRAM Calculator (Weights + KV Cache + Quantization) perform the calculation?

It evaluates Weights_GB = Params_B × (Bits / 8); KV_Cache_GB ≈ 0.00008 × Params_B × (ContextTokens / 1024) × BatchSize; TotalVRAM = (Weights_GB + KV_Cache_GB) × 1.12 directly in your browser and renders three custom SVG charts in real time.

Can I solve backwards for an input parameter using Goal Seek?

Yes. Click "🎯 Goal Seek" in the results toolbar to solve backwards for any numeric input field.

Does this tool work offline?

Yes. All calculations run 100% locally in your browser with full offline PWA support.

Developer Tools · AI, Cloud & Distributed Systems

LLM Inference & Training GPU VRAM Calculator (Weights + KV Cache + Quantization): inputs, method, and how to read the result

Size GPU VRAM (H100 / A100 / RTX 4090 count) for LLM weights, KV cache, and CUDA activations.

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.

  • Model Parameters Billions: Total model parameters in Billions (e.g., 7B, 8B, 14B, 32B, 70B, 405B).
  • Quantization Bits Per Weight: Weight precision in bits (16 = FP16/BF16, 8 = FP8/INT8, 4 = INT4/AWQ/Q4_K_M).
  • Context Window Tokens: Active sequence context length per request in tokens.
  • Concurrent Batch Size: Concurrent active sequences in the inference batch.

What the calculation includes

With the default values shown, Total Required GPU VRAM is 40.188 GB VRAM. The calculation follows LLM GPU VRAM & KV Cache Formula; the formula and variable definitions above show how the inputs combine.

One-input check: With the other defaults held constant, changing Model Parameters Billions from 70 B params to 77 B params changes Total Required GPU VRAM from 40.188 GB VRAM to 44.127 GB VRAM. This is a comparison inside the calculator's stated model, not a prediction.

Where to use caution

  • Uses official NIST Special Publication 811, IEEE/RFC systems engineering, APA psychometric, and USNO astronomical formulas.
  • Empirical metallurgy and calendar conversions represent standard reference-condition approximations.

Scope: Global NIST, IEEE, APA & Astronomical Standard Check the assumptions and limitations against your situation before relying on the estimate.

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