GPU VRAM Calculator

数据开发人员数学

[iotools_gpu_vram_calculator]

指导

GPU VRAM Calculator

Estimate how much GPU memory a transformer model needs for inference or training. Enter parameters, precision, batch size, and sequence length, and the calculator returns total VRAM along with a breakdown of weights, gradients, optimizer state, KV cache, and activations. It also compares the result against common GPUs (RTX 4090, A100, H100, H200, B200) so you can see at a glance which one fits.

如何使用

Pick a preset (Llama 3 8B, Mistral 7B, Llama 3 70B, etc.) or choose Custom and enter your own parameters, hidden dimension, and layer count.
Select Inference or Training. Training reveals optimizer, mixed-precision, and gradient-checkpointing options.
Choose a precision: float32, float16/bfloat16, int8, or int4.
Enter batch size and sequence length. The KV cache and activations scale with both.
Read the totals at the top, the breakdown table for each component, and the GPU fit table to see which GPUs hold the workload.

特征

Model presets – GPT-2, Llama 3.2 1B/3B, Mistral 7B, Llama 3 8B, Llama 2 13B, Mixtral 8x7B, Llama 3 70B, and Llama 3.1 405B with accurate hidden dimensions and layer counts.
Inference and training modes – Switches between weights+KV-cache math and the full training equation with gradients, optimizer state, and activations.
Precision options – float32, float16/bfloat16, int8, and int4 to model the impact of quantization.
Optimizer choices – Adam/AdamW (8 bytes/param), SGD with momentum (4 bytes/param), or plain SGD (0 bytes/param).
Mixed-precision support – Adds the fp32 master weight copy used by Apex, FSDP, and DeepSpeed.
Gradient checkpointing – Applies the standard sqrt(layers) reduction to activation memory.
GPU fit table – Shows utilization against RTX 4060 Ti, RTX 4090, RTX 5090, L40S, A100, H100, H200, and B200, plus how many GPUs are needed to fit the workload.
仅客户端 – Calculations run in the browser, so your model details never leave your machine.

何时使用此工具

Choosing the right GPU instance type before paying for an A100 or H100.
Deciding whether to quantize a model to int4/int8 so it fits a single consumer card.
Sizing context length and batch size for a serving workload to predict KV cache growth.
Planning a fine-tuning run with Adam vs SGD, mixed precision, or gradient checkpointing.
Validating tensor-parallel or model-parallel sharding strategies for very large models.

 常问问题

What does VRAM mean for large language models?

VRAM is the dedicated memory on a GPU. To run a transformer model, the GPU must hold the model weights, the activations used during inference or training, and any KV cache for attention. If the sum of those exceeds VRAM, the workload either errors out or spills to slower memory and slows dramatically.
Why does training use so much more memory than inference?

Inference only needs the model weights plus the KV cache for the current batch. Training also keeps gradients (one extra copy of the parameters), optimizer states (Adam/AdamW stores momentum and variance in float32, adding eight bytes per parameter), and activations from every layer for the backward pass. For an Adam-trained model, the optimizer state alone is roughly two times the weights in float32.
How does precision affect memory?

Each parameter takes four bytes in float32, two bytes in float16/bfloat16, one byte in int8, and half a byte in int4. Switching from float32 to float16 halves the weight memory. int4 quantization cuts it by eight times, which is why quantized models fit on consumer GPUs that cannot hold the full-precision version.
What is the KV cache and why does it grow with context length?

The KV cache stores the key and value tensors computed by attention so they do not need to be recomputed at every step. Its size is two (K and V) times batch size times sequence length times hidden dimension times number of layers, in whatever precision the cache uses. Long contexts can make the KV cache rival or exceed the weights in size.
What does gradient checkpointing trade off?

Gradient checkpointing only stores activations at a few checkpoints during the forward pass and recomputes the rest during the backward pass. It reduces activation memory by roughly the square root of the number of layers, in exchange for about one extra forward pass of compute per step.