Torch

Training an LLM from Scratch, Locally — Angelos Perivolaropoulos, ElevenLabs

May 4, 2026 · 1:21:26

Angelos Perivolaropoulos from ElevenLabs walks through building a small GPT-2-like LLM from scratch on a local machine, demonstrating that the core techniques used by major labs are accessible in a few hundred lines of PyTorch code. The workshop uses character-level tokenization (65 tokens) on a Shakespeare dataset to enable fast training with limited compute. The model architecture includes multi-head self-attention, MLP layers, residual connections, and layer normalization, totaling 10 million parameters across six transformer blocks with a 256-token context window. The training loop employs next-token prediction with a warm-up cosine decay learning rate schedule and validation loss to detect overfitting. Inference uses temperature sampling (default 0.7) and top-k sampling to improve creativity. Perivolaropoulos explains that audio and multimodal models share the same transformer foundation but differ in tokenization (e.g., mel-spectrograms for audio) and use specialized losses like L2 or KL divergence, while reasoning models result from post-training base models with high-quality chain-of-thought data.

Keynote: The AI developer experience doesn't have to suck – why and how we built Modal

Feb 22, 2025 · 21:38

Eric Bernhardson, CEO of Modal, explains why and how his company replaced Kubernetes and Docker with a custom container system to deliver sub-second cold starts for AI developers. Modal turns any Python function into a serverless function with a decorator, runs on thousands of H100s, and fans out to 10,000 parallel calls. To achieve fast startup, Modal built content-addressable storage for deduplication, lazy file loading with prefetching, and uses gVisor for CPU memory snapshotting, cutting Stable Diffusion startup to seconds. The company built its own scheduler and file system, and uses mixed integer programming to manage a global GPU pool across cloud vendors. Customers like Suno use Modal for AI-generated music inference. Modal offers $30/month free credits.

Harnessing the Power of LLMs Locally: Mithun Hunsur

Nov 22, 2023 · 17:09

Mithun Hunsur presents llm.rs, a Rust library for running large language models locally, arguing it gives developers ownership, lower latency, and privacy compared to cloud APIs. He explains how quantization makes inference viable on consumer hardware, and shows that llm.rs supports architectures like LLaMA and Falcon through a unified interface. Practical code examples demonstrate customization, and community projects like LocalAI and LLMchain illustrate real-world use. Hunsur shares his own date-extraction pipeline, fine-tuning a small model with GPT-3 data to replace expensive cloud calls. He also cautions about hardware requirements, trade-offs between speed and quality, and ecosystem churn from rapid innovation.