Every LLM is 8 layers of ideas stacked on top of each other which you can implement on tensortonic: Layer 1: Gradients Write a tiny autograd engine. Value nodes, expression graphs, a neuron. loss.backward() stops being a black box. Layer 2: Tokens Train BPE yourself and apply the merges by hand. Layer 3: The block RMSNorm, SwiGLU, RoPE, causal grouped-query attention. Layer 4: Training at scale FLOP estimates, mixed-precision memory, gradient accumulation. Layer 5: Attention variants MHA → MQA → GQA → MLA, then FlashAttention. Layer 6: Kernels Softmax, GELU and reductions in CUDA, then again in Triton. Layer 7: Fine-tuning QLoRA on Qwen3-4B with real medical Q&A. Layer 8: Serving Build your own Qwen3 inference server. All 8 are graded, runnable problems on TensorTonic. link in comments.