AMD Instinct MI300X Vs NVIDIA H100
Reprogramming Discovery: The Future of AI-Driven Biology Is Being Driven by AMD Instinct GPUs
A contribution from IPA Therapeutics
Part 1: Embeddings for NLP in Life Sciences
This article is the first of three that compares AMD Instinct MI300X GPUs to NVIDIA’s H100 GPUs in real-world drug discovery AI workloads. ImmunoPrecise Antibodies (IPA) and its AI subsidiary BioStrand, who work with Vultr to develop the LENSai platform for AI-powered biologics discovery, carried out the benchmarks. Vultr’s high-performance cloud infrastructure allowed for quick deployment and reproducibility across hardware designs. It assessed these GPUs’ combined performance under the real-world requirements of medicinal development, ranging from generative protein design to NLP-driven target finding.
The biological fingerprinting technique known as HYFT technology, which is the foundation of LENSai, encodes conserved sequence, structure, and function into a single index. HYFTs were developed to address AI’s basic shortcoming, which is its inability to naturally comprehend biological systems. HYFTs enable AI models to reason via biology rather than merely calculate it by integrating biological reasoning into the computational fabric.
AMD will examine how the MI300X GPUs function across the LENSai tech stack in the next three articles: generative design using RFdiffusion, protein embedding generation for structure-function inference, and NLP-driven literature mining.
AMD set out to assess the raw performance, cost effectiveness, and deployment viability of contemporary bioinformatics pipelines using real-world benchmarks in NLP, protein embeddings, and de novo protein design.
In this first episode, it especially address Natural Language Processing (NLP), focussing on how Retrieval-Augmented Generation (RAG) and massive language models speed up early-stage treatment discovery by gleaning practical insights from scientific literature. The most important lesson? In addition to their competitive speed, AMD GPUs have significant cost advantages, which is important for life science companies expanding AI-driven platforms.
Therapeutic breakthroughs are greatly enhanced by Natural Language Processing (NLP), which efficiently mines large amounts of textual data. NLP facilitates the extraction of hidden insights from genomic databases, clinical reports, and scientific literature. The FDA’s move towards computational models, which prioritise safety, efficacy, and cost-effectiveness, is in line with NLP-driven large language models (LLMs), which simplify the analysis and prediction procedures crucial for drug research.
Knowledge-aware models can reveal pertinent insights based on semantics rather than wording to vector embeddings in RAG (Retrieval-Augmented Generation) systems. These embeddings allow NLP to bridge silos in the life sciences since they enable biological sequences and structures in addition to text.
By incorporating a potent semantic layer that recognises sub-sentence units and extracts subject-predicate-object triples to reveal significant biological correlations, LENSai expands upon the current vector search capabilities. LENSai enables researchers to map disease pathways, identify therapeutic targets, and more clearly predict drug behaviour by recording the molecular interactions between targets, pathways, and compounds. Long before wet lab studies start, this depth of insight which is frequently hidden in unstructured biological data can be revealed and used to speed up discovery while lowering risk and expense.
Context of Infrastructure
GPU Specification | AMD Instinct GPU | NVIDIA H100 |
Memory Capacity | 192 GB | 80 GB |
GPU Architecture | CDNA 3 | Hopper |
Compute Power | FP64/FP32/FP16 | FP64/FP32/FP16 |
Deployment Model | Cloud-native | Cloud-native |
To ensure repeatable benchmarks and equitable comparisons across hardware generations, it deployed both the AMD Instinct MI300X and the NVIDIA H100 GPUs in a flexible, cloud-native environment.
Benchmark Results for NLP
For contextually relevant insights, it retrieval-augmented generation (RAG) algorithms employ literature vector embeddings. AMD proved to be more cost-effective and throughput-efficient:
Metric | NVIDIA H100 | AMD Instinct MI300X |
Sequences/sec | 2741.21 | 3421.22 |
Cost per 1M Samples | $2.40 | $1.46 |
Improved stability under workloads with high concurrency was another feature of the MI300X.
Technical Execution: Smooth Switch to AMD Graphics Processing Units
- It’s simple to move NLP tasks to AMD GPUs using ROCm PyTorch Docker images:
- ROCM6.3.1_ubuntu22.04_py3.10_pytorch FROM rocm/pytorch
- The Python code doesn’t need to be altered. Compatibility is guaranteed by PyTorch’s device abstraction (torch.device(“cuda”)).
- AMD Instinct MI300X GPUs provide both technical and financial benefit in one of the most basic tiers of AI-assisted drug discovery, as demonstrated by these NLP benchmarks.