AMD · 2023-12-06
Instinct MI210
PCIe Gen4 Passive Accelerator
The AMD Instinct MI210 PCIe Gen4 Passive Accelerator is a compute workhorse optimized for accelerating single precision and double-precision HPC-class systems. It offers Exascale-Class Technologies, Purpose-built Accelerators for HPC & AI Workloads, and Innovations Delivering Performance Leadership.
Provider Marketplace
All Cloud Providers
Compute Performance
Architecture
Memory & VRAM
Connectivity & Scaling
Virtualization
Power & Efficiency
Physical Design
Thermals & Cooling
Software Ecosystem
Server & Deployment
System Compatibility
Benchmarks & Throughput
Structured Sparsity
Not Supported
Multi-GPU Scalability
Scaling Efficiency
Scaling Characteristics
Workload Readiness
LLM Training
The Instinct MI210, based on the CDNA2 architecture, is suitable for training large models up to 70B parameters in a multi-node setup due to its high memory bandwidth and scalability features.
LLM Inference
With its substantial VRAM and high throughput, the MI210 is capable of efficient inference for large language models, providing good token-per-second performance and ample KV cache headroom.
Vision Training
The MI210 is well-suited for vision training tasks, leveraging its high compute capabilities and memory bandwidth to efficiently train complex models.
Diffusion Models
The MI210 can handle diffusion models effectively, benefiting from its robust architecture and memory capacity to manage the computational demands of these models.
Multimodal AI
The MI210's architecture supports multimodal AI tasks, offering the necessary compute power and memory bandwidth to process diverse data types simultaneously.
Reinforcement Learning
The MI210 is capable of handling reinforcement learning workloads, providing the necessary compute power and memory bandwidth for complex simulations and model updates.
HPC / Simulation
The MI210 excels in HPC simulations due to its strong FP64 performance, making it ideal for scientific and engineering computations requiring double precision.
Scientific Computing
With excellent FP64 support, the MI210 is highly suitable for scientific computing tasks that demand high precision and computational power.
Edge Inference
The MI210, with its passive cooling and higher power consumption, is not optimized for edge inference scenarios where low power and compact form factors are critical.
Real-Time Serving
The MI210 can serve real-time AI applications effectively, thanks to its high throughput and ability to handle large models efficiently.
Fine-Tuning
The MI210 is efficient for full fine-tuning tasks, leveraging its high VRAM capacity to manage large model weights and gradients.
LoRA Efficiency
The MI210 can efficiently handle LoRA fine-tuning, benefiting from its architecture to support parameter-efficient training methods.
Market Authority
Supercomputer Usage
Oak Ridge National Laboratory's Frontier supercomputer uses MI250X, not MI210; no top 10 supercomputer publicly lists MI210 as primary accelerator.
Research Citations
Limited; a small number of academic papers reference MI210 for benchmarking or comparative studies, but it is not widely cited as a primary accelerator.
Community Benchmarks
Sparse; a few independent benchmarks (e.g., on forums or blogs) exist, but no large-scale or widely recognized community benchmarks are available.
GitHub Support
Minimal; ROCm and HIP support MI210, but few repositories specifically optimize for MI210 versus other AMD Instinct GPUs.
Key Strengths
Excels in high-performance and AI workloads.
- ·AI Training: Optimized for large-scale AI model training.
- ·HPC Performance: Delivers strong performance in scientific computing tasks.
- ·Data Analytics: Efficient for large-scale data processing and analytics.
Limitations
Some limitations in software ecosystem compared to NVIDIA.
- ·Software Ecosystem: Less mature software stack compared to NVIDIA CUDA.
- ·Availability: May have limited availability in certain regions.
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Expert Insight
The Instinct MI210 represents a powerful alternative for diversified workloads. When comparing cloud providers, consider not just the hourly rate, but also the interconnect bandwidth (InfiniBand/NVLink) and regional availability which can significantly impact total cost of ownership for large-scale training.