Unverified50% confidenceBenchmarkExact time
OjasX v0.6.1 can train a 1.04B parameter Transformer model at full FP32 precision with only 6.35GB RAM, whereas standard PyTorch requires 21.1GB.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/14/2026
First Seen
Valid until: 1/12/2027
Sources
Related Entities
Related Claims
UnverifiedFor 7B-8B models, standard PyTorch FP32+AdamW requires 140-160GB, whereas OjasX full training (FP32+Grad-Free SGD) requires only about 35.5-40GB, and LoRA fine-tuning about 31-35GB.76% similarUnverifiedOjasX requires only about 3.25GB of memory to train a 1.04B model at BF16/FP16 half precision, and as little as 2.6GB for LoRA fine-tuning.74% similarUnverifiedEarly versions of OjasX could already fit a 534M parameter model into the 9.5GB VRAM of a Tesla T4 for training, whereas standard PyTorch would crash at 16.4GB at that time.69% similarUnverifiedModels such as Llama-3.1-8B are built on a stacked Transformer decoder architecture, typically containing 32 decoder layers, with approximately 250 million parameters per layer and approximately 500MB of VRAM per layer at FP16 precision.67% similarUnverifiedLLaMA-2 70B 模型共有 80 个 Transformer 层，每层约 8.75 亿参数，FP16 精度下单层约占 1.75GB 显存64% similar
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