Unverified50% confidenceBenchmarkExact time
Early 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.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/14/2026
First Seen
Valid until: 1/12/2027
Sources
Related Entities
Related Claims
UnverifiedOjasX 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.73% similarUnverifiedFor 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.71% similarUnverifiedOjasX 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.69% similarUnverifiedThe Tesla P100 is equipped with 16 GB HBM2 memory and 732 GB/s bandwidth.63% similarUnverifiedThe Tesla V100's FP16 compute performance is approximately 31.4 TFLOPS, and up to 125 TFLOPS with Tensor Cores enabled.62% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1025337API
curl https://kongchang.com/api/v1/knowledge/claims/1025337MCP
get_claim(id=1025337)