While using a pre-trained LLM for tasks like generating text or answering questions (inference) has its own hardware needs, the process of training an LLM from scratch or even significantly fine-tuning it requires substantially more resources. Training is where the model learns its capabilities by processing enormous amounts of data and adjusting its internal parameters. Let's look at why this process is so demanding.

The Computational Burden of Learning

Training involves repeatedly showing the model examples from a large dataset and adjusting its parameters to improve its performance. This cycle consists of several steps, each computationally expensive:

Forward Pass: Similar to inference, input data is fed through the model's network layers to produce an output.
Loss Calculation: The model's output is compared to the desired output (the "ground truth" from the training data), and a "loss" value is calculated, quantifying how inaccurate the model was.
Backward Pass (Backpropagation): This is computationally intensive. The loss value is used to calculate gradients, which are essentially directions for how each of the billions of parameters in the model should be adjusted to reduce the loss for that specific example. This requires propagating information backward through the network's layers.
Parameter Update: An optimization algorithm (like Adam or SGD) uses the calculated gradients to update the model's parameters. This step often involves additional calculations and requires storing extra information about the parameters.

This entire cycle is performed for small "batches" of data, and the process is repeated for the entire dataset multiple times (known as "epochs"). Training a large model might involve processing petabytes of data and performing trillions upon trillions of calculations.

Why Training Needs More Compute (GPUs)

The sheer scale of calculations involved in backpropagation and parameter updates for billions of parameters across vast datasets necessitates massive parallel processing capabilities.

Gradient Calculation: Calculating gradients for every single parameter is a huge task. For a 70 billion parameter model, 70 billion gradient values must be computed in each step.
Optimization: Optimization algorithms perform further calculations based on these gradients.
Repetition: This entire process is repeated billions or trillions of times during a full training run.

This is why training large language models almost exclusively relies on Graphics Processing Units (GPUs), often in large clusters:

High-End GPUs: Training requires GPUs with significant raw compute power (measured in FLOPS) and specialized cores (like Tensor Cores in NVIDIA GPUs) designed to accelerate the matrix multiplications fundamental to deep learning.
Distributed Training: Training large models is typically too slow on a single GPU. It often requires distributing the workload across tens, hundreds, or even thousands of GPUs working in parallel. This requires sophisticated software frameworks and high-speed connections between the GPUs.

While inference might be feasible on a single powerful GPU, training the same model would likely require a coordinated effort from many such GPUs running continuously for weeks or months.

Why Training Needs More Memory (VRAM and RAM)

The memory requirements for training dwarf those of inference. While inference primarily needs VRAM to hold the model parameters and activations for the current input, training requires VRAM to store several additional, large pieces of information simultaneously:

Model Parameters: Just like inference, the weights of the model itself must be in VRAM.
Gradients: A gradient value must be stored for every parameter in the model. If using 16-bit precision (FP16) for parameters, the gradients (often stored at higher precision like FP32 initially) can require double the memory of the parameters themselves.
Optimizer States: Most effective optimizers (like Adam) maintain extra information for each parameter. Adam, for instance, typically stores two additional values (momentum and variance estimates) per parameter. If these are stored in 32-bit precision (FP32), they can consume 4 times the memory of the FP16 parameters (2 values * 2 bytes/value for FP16 parameters vs 2 values * 4 bytes/value for FP32 optimizer states). In total, parameters + gradients + optimizer states can easily require 4x to 8x (or more) the VRAM needed just for the parameters alone.
Activations: The intermediate results calculated during the forward pass (activations) must be stored, as they are needed for the gradient calculations during the backward pass. The amount of memory needed for activations depends on the model architecture, batch size, and sequence length.
Data Batches: The current batch of training data being processed also needs to fit into VRAM.

This combination means that a GPU might have enough VRAM to run inference for a large model but be completely insufficient for training it, even with a small batch size.

System RAM is also critical during training. It's used to load and preprocess the massive training datasets before batches are transferred to the GPU VRAM. Training can easily be slowed down if the system doesn't have enough RAM or if the storage holding the dataset is too slow (disk I/O bottleneck).

Training demands significantly higher levels of GPU compute, VRAM, system RAM, and fast access to large dataset storage compared to running inference.

In essence, training is about building and refining the complex structure of the model, requiring immense computational effort and memory to track the learning process for billions of parameters simultaneously. Inference, on the other hand, is about utilizing that already built structure. This fundamental difference explains the vastly different hardware scales involved.