AMD’s MI350 Is Forcing a Question NVIDIA Has Avoided for Years

Is AMD MI350 a real alternative to NVIDIA H200 for training large AI models? AMD MI350 series (including MI350X and MI355X) is a strong and viable alternative to NVIDIA H200 for large-scale AI training and inference workloads as of late 2025, particularly for memory-intensive and cost-sensitive deployments.

NVIDIA's ecosystem continues to lead in maturity and optimisation, but the AMD MI350 delivers superior memory capacity (288 GB HBM3E vs 141 GB on H200), higher memory bandwidth (up to 8 TB/s vs 4.8 TB/s), and competitive performance, especially in inference. With significant improvements in ROCm software maturity throughout 2025, it is increasingly reliable for enterprise-scale training, though some workloads may still favour CUDA.

What is AMD MI350 and when did it ship? AMD MI350 series is AMD’s latest Instinct accelerator family, built on the 4th Gen CDNA architecture, targeted at large-scale AI training, inference, and HPC workloads.

Broad availability began in the second half of 2025, with volume production and deployments through major cloud providers and OEMs.

Key design objectives of AMD MI350 Higher Memory Headroom: 288 GB HBM3E to support trillion-parameter models and extended contexts. Improved ROCm Stability: Enhanced enterprise-grade reliability with ROCm 7.x versions delivering major performance gains. Cost-Efficient Scaling: Competitive pricing and lower total cost of ownership for dense deployments. How does AMD MI350 compare to NVIDIA H200? Primary Use Case

AMD MI350 Series (MI350X/MI355X): Large Model Training and Inference NVIDIA H200: Large Model Training and Inference Memory

AMD MI350 Series (MI350X/MI355X): 288 GB HBM3E NVIDIA H200: 141 GB HBM3e Memory Bandwidth

AMD MI350 Series (MI350X/MI355X): Up to 8 TB/s NVIDIA H200: 4.8 TB/s Interconnect

AMD MI350 Series (MI350X/MI355X): Infinity Fabric NVIDIA H200: NVLink Software Stack

AMD MI350 Series (MI350X/MI355X): ROCm 7.x NVIDIA H200: CUDA Cost Profile

AMD MI350 Series (MI350X/MI355X): Generally lower upfront and scaling cost NVIDIA H200: Premium pricing

Why are Sydney data centres evaluating AMD MI350? Sydney data centres are evaluating AMD MI350 due to NVIDIA GPU supply constraints, attractive AMD pricing, superior memory density for demanding AI workloads, and a desire to diversify vendor dependency.

Major facilities in NSW, such as those operated by NEXTDC and AirTrunk, support multi-vendor GPU deployments, with growing interest in hybrid AMD-NVIDIA clusters to optimise cost, performance, and availability. Enterprises prioritise supply resilience, power efficiency, and compatibility with open-source frameworks.

Supply Chain Flexibility: Alternatives to NVIDIA allocation limits. Budget Predictability: Better planning for AI infrastructure expansion. Open Software Strategy: Improved ROCm integration with PyTorch and other frameworks. What are the limitations enterprises should consider? AMD MI350 trails NVIDIA H200 in overall ecosystem maturity.

Despite rapid ROCm advancements in 2025, many proprietary tools and optimisations remain CUDA-centric, potentially requiring additional porting effort.

Software Porting Overhead: Validation for custom pipelines. Talent Availability: Greater pool of CUDA-experienced engineers. Vendor Support Models: May need more in-house expertise for optimisation. Key takeaways for NSW enterprise AI teams AMD MI350 is production-viable for large model training and inference as of late 2025. NVIDIA H200 remains dominant for seamless, highly optimised deployments. Hybrid GPU strategies are increasingly adopted in Sydney data centres to balance cost, performance, and risk. For expert advice on AI infrastructure planning, procurement, and data centre strategies in NSW, consult specialised providers supporting both AMD and NVIDIA accelerators.