TensorFlow and PyTorch were once genuinely competing frameworks where the choice had significant practical consequences. In 2026, that competition is largely settled: PyTorch is the dominant framework for research and model development, and TensorFlow has a narrower but legitimate production deployment story. Understanding what each does well — and what the landscape actually looks like … Read more
bitsandbytes makes it possible to load large language models in 8-bit or 4-bit precision, slashing the GPU memory required without retraining. A 7B parameter model in float16 needs ~14GB VRAM; in 4-bit it needs ~4GB. This is the primary reason practitioners can now fine-tune LLaMA, Mistral, and similar models on a single consumer GPU. This … Read more
FlashAttention-2 is a rewritten attention algorithm that computes the exact same result as standard attention but 2–4x faster and with memory usage that scales linearly with sequence length instead of quadratically. It achieves this by tiling the attention computation to stay within GPU SRAM rather than reading from the slower HBM memory repeatedly. For anyone … Read more
Parameter-efficient fine-tuning (PEFT) methods adapt a pretrained large model to a new task by training only a small fraction of its parameters rather than the full model. The most widely used method is LoRA (Low-Rank Adaptation), which freezes the original weights and adds trainable low-rank matrices to specific layers. Hugging Face’s PEFT library provides a … Read more
The DataLoader is often the bottleneck that prevents your GPU from training at full utilisation. If your GPU sits at 30–60% utilisation between batches, the problem is almost certainly data loading — the CPU cannot prepare and transfer batches fast enough to keep the GPU busy. This guide covers every meaningful optimisation: worker count, pinned … Read more
PyTorch Lightning is a high-level wrapper around PyTorch that removes boilerplate from training loops while keeping you in control of the actual model logic. Vanilla PyTorch gives you maximum flexibility and transparency — every part of training is code you wrote. The decision between them is not about which is better in absolute terms: it … Read more
Distributed training lets you use multiple GPUs — on one machine or across many — to train models faster or to fit models that do not fit on a single GPU. PyTorch’s DistributedDataParallel (DDP) is the standard approach: each GPU runs a full copy of the model on a shard of the data, and gradients … Read more
A PyTorch training loop that is not learning is one of the most common and frustrating situations in deep learning. The loss is flat, or NaN, or oscillating — and the model is right, the data is right, so what is wrong? This guide covers the systematic debugging process: how to isolate whether the problem … Read more
Apple Silicon (M1, M2, M3, M4) brings a capable GPU integrated into the same die as the CPU, with shared memory that eliminates the PCIe data transfer bottleneck of discrete GPUs. PyTorch supports it via the Metal Performance Shaders (MPS) backend, available since PyTorch 1.12. For ML workloads that fit in the Mac’s unified memory, … Read more
JAX is NumPy with automatic differentiation, JIT compilation, and hardware acceleration baked in. It is not a deep learning framework in the way PyTorch is — there are no built-in layers or optimisers. Instead, JAX gives you the primitives for building them: pure functional transformations (jit, grad, vmap, pmap) that compose cleanly and run efficiently … Read more
