LLM VRAM Calculator

Calculate VRAM requirements for any LLM including model weights, KV cache, and overhead.

📊 Model Specifications

📈 VRAM Breakdown

Model Weights1.3 GB
KV Cache (1× batch)0.7 GB
Overhead (~10%)0.1 GB
Total VRAM Required2.2 GB
H100 GPUs Needed (80 GB)1

💡 Tips

  • Use 4-bit quantization to reduce VRAM by 75%
  • KV cache grows linearly with context length and batch size
  • For inference, 4-bit is usually sufficient
  • For training, 16-bit or 32-bit is recommended

📐 Formula

VRAM = weights + KV cache + overhead
Weights = P × Q / 8
KV cache = B × L × P × 2 × Q / 8
Where: P=parameters, Q=bits, B=batch, L=context

Plugsky · Tools · Pricing · Status

What the LLM VRAM Calculator | Plugsky does

The LLM VRAM Calculator estimates the GPU memory an LLM needs for inference. It sums model weights, KV cache and overhead, and reports the total plus how many 80 GB GPUs are required. Inputs are parameter count, quantization, context length and batch size. It is aimed at engineers choosing hardware or planning local deployment. The page states its formula, and results are estimates, so leave headroom.

How to use it

  1. Enter the model's parameter count in billions.
  2. Select the quantization you plan to use.
  3. Set the context length and batch size you will serve.
  4. Read the weights, KV cache, overhead and total breakdown.
  5. Add a safety margin for allocator and runtime overhead.

FAQ

How much VRAM does an LLM need?

Weights alone need roughly parameter count times bits per parameter divided by eight. KV cache grows with context and batch, and runtimes add overhead. A 7B model at 4-bit can fit in a few gigabytes, while long-context or high-batch serving needs multiples of that. The calculator shows each part.

Does quantization reduce quality?

4-bit quantization cuts weight memory by roughly 75% against 16-bit and is usually fine for inference, with modest quality loss on some tasks. 8-bit is closer to full precision. Test on your workload, because coding, maths and long-context tasks are often most sensitive.

Can I run large models without local GPUs?

Yes. A hosted API removes hardware planning, and Plugsky exposes 30+ models through an OpenAI-compatible interface with a free plan including 2 free models. Compare that against GPU purchase, power and maintenance before deciding to self-host.

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Canonical pricing and plans: plugsky.com/#sec-pricing · Terms · SLA · Docs

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