RTX 4070 NanoChat Training Time Estimate | Generated by AI
To estimate the training time for nanochat d32 (1.9 billion parameters, 38 billion tokens) on a single NVIDIA RTX 4070 GPU, we can scale from the original setup: 33 hours on an 8x H100 node.
Based on performance comparisons for transformer-based workloads (relevant to LLM training), an H100 SXM GPU is approximately 8-9 times faster than an RTX 4070 in compute-intensive tasks like matrix multiplications in FP16/BF16 precision. This accounts for differences in tensor core throughput, memory bandwidth, and effective utilization.
The original training used 8 H100s, equivalent to about 264 H100-hours. Scaling to a single RTX 4070 would take roughly 2,100-2,400 hours, or about 88-100 days of continuous running.
This assumes the training script (like run1000.sh) can fit and run on the 4070’s 12 GB VRAM, possibly with adjustments like smaller batch sizes, gradient checkpointing, or optimizer offloading to avoid memory issues. In practice, training might be memory-bound or require tweaks, potentially increasing time further due to lower efficiency on consumer hardware. Power consumption would be around 200W, keeping electricity costs low compared to cloud.
References:
Which GPU(s) to Get for Deep Learning
GPU Benchmarks NVIDIA H100 NVL (PCIe) vs. NVIDIA RTX 4090 vs. NVIDIA RTX 4080
NVIDIA H100 PCIe vs SXM: Performance and Use Cases Compared