AMD Instinct MI300X Vs NVIDIA H100

AMD Instinct MI300X and NVIDIA H100 GPUs were benchmarked in real-world AI-driven drug discovery workloads by IPA Therapeutics and BioStrand using Vultr’s cloud infrastructure

LENSai, the AI platform tested, uses HYFT technology to encode biological sequence, structure, and function for advanced biological reasoning in AI models

The benchmarks focused on NLP tasks, especially RAG for mining scientific literature and accelerating early-stage drug discovery

NLP-driven LLMs help extract insights from genomic databases, clinical reports, and literature, aligning with FDA’s push for computational drug research

LENSai’s semantic layer extracts subject-predicate-object triples, mapping molecular interactions and revealing hidden biological correlations

AMD Instinct MI300X offers 192 GB memory, while NVIDIA H100 provides 80 GB, both using cloud-native deployment models

In NLP RAG benchmarks, the MI300X achieved higher throughput (3421.22 sequences/sec) than the H100 (2741.21 sequences/sec)

Cost per 1M samples was significantly lower on the MI300X ($1.46) compared to the H100 ($2.40), highlighting AMD’s cost-effectiveness

The MI300X demonstrated improved stability under high-concurrency workloads, making it suitable for large-scale AI tasks

Transitioning NLP workloads to AMD GPUs is straightforward using ROCm PyTorch Docker images, with no code changes required

For more details visit Govindhtech.com