CAI-GPU-MI210 - AMD Instinct MI210 64GB HBM2e GPU | AMD
N.º da peça: CAI-GPU-MI210 ✓ Ativo| Qtd. | Preço unitário | Total |
|---|---|---|
| 1 | $0 | $0,00 |
| 10 | $0 | $0,00 |
| 100 | $0 | $0,00 |
| 500 | $0 | $0,00 |
| 1.000 | $0 | $0,00 |
CAI-GPU-MI210 Visão geral
A data center GPU accelerator is a specialized compute card designed to offload parallel workloads such as AI training, AI inference, and high-performance computing (HPC) from the host CPU. Within the system hierarchy, it sits above general-purpose processors in the compute stack, functioning as a coprocessor that connects through the PCIe bus and is programmed via ecosystems such as ROCm and OpenCL 2.0 rather than consumer graphics APIs.
Key differentiating specifications include 64 GB of HBM2e memory on a 4096-bit memory bus running at 1600 MHz (3.2 Gbps effective), producing approximately 1600 GB/s of memory bandwidth. The Aldebaran die integrates 58.2 billion transistors on a 724 mm2 die fabricated on TSMC 6 nm process technology, with a base clock of 1000 MHz and a boost clock up to 1700 MHz.
Architecturally, the MI210 belongs to the Radeon Instinct MI200 family (CDNA 2.0), which pairs large on-package HBM2e capacity with matrix-oriented compute units tailored for mixed-precision AI and double-precision scientific workloads. The single-GPU PCIe card form factor makes it deployable in standard servers without OAM module carrier boards.
Typical applications include enterprise and academic HPC clusters, single-server AI training and inference, and compute workloads that benefit from large memory capacity per accelerator.
Design consideration: the 300 W maximum board power is supplied through a single 8-pin PCIe power connector, so host platforms must verify both PSU headroom and dual-slot thermal clearance.
This page synthesizes distributor sourcing data, cross-reference alternatives, and practical deployment notes not found in the manufacturer product page.
Alternativas drop-in para CAI-GPU-MI210 — mesmo encapsulamento, compatível pino a pino. Peças de encapsulamento diferente que exigem retrabalho na PCB são excluídas.
Ferramenta de comparação rápida — selecione peças alternativas para comparação lado a lado:
AMD Instinct MI250X
✅ Drop-In ⚠️ Especificações não verificadas📋 Alternativa de referência (fora do catálogo)
AMD Instinct MI250
✅ Drop-In ⚠️ Especificações não verificadas📋 Alternativa de referência (fora do catálogo)
AMD Instinct MI100
✅ Drop-In ⚠️ Especificações não verificadas📋 Alternativa de referência (fora do catálogo)
NVIDIA A100 PCIe 40 GB
✅ Drop-In📋 Alternativa de referência (fora do catálogo)
CAI-GPU-MI210 Especificações (publicadas pelo fabricante)
| GPU Architecture | AMD CDNA 2.0 |
| GPU Processor | Aldebaran |
| Compute Units | 112 |
| Process Technology | 6 nm |
| Transistor Count | 58.2 billion |
| Die Size | 724.0 mm2 |
| Base Clock | 1000 MHz |
| Boost Clock | 1700 MHz |
| Memory Capacity | 64 GB HBM2e |
| Memory Bus Width | 4096-bit |
| Memory Speed | 1600 MHz (3.2 Gbps effective) |
| Memory Bandwidth | 1600 GB/s |
| FP32 Performance | 22.6 TFLOPS |
| FP16 Performance | 181 TFLOPS |
| Bus Interface | PCIe 4.0 x16 |
| Board Power (TDP) | 300 W maximum |
| Power Connector | 1x 8-pin |
| Form Factor | Dual-slot PCIe card |
| Software Support | OpenCL 2.0, ROCm |
| Target Workloads | HPC and AI (no consumer DirectX gaming support) |
| Release Date | 2021-12-01 (architecture launch); card launch 2022-03-22 |
Interfaces e Conectores do CAI-GPU-MI210
Não há lista de interfaces publicada pelo fabricante disponível para CAI-GPU-MI210. Consulte a documentação do fabricante para detalhes de conectores e headers.
Consulte a documentação do fabricante para a pinagem completa do conector de expansão e a numeração dos pinos do conector.
Aplicações típicas
CAI-GPU-MI210 é adequado para 6 aplicaçãos: Enterprise HPC Clusters, AI Training and Inference Servers, Academic and Research Computing, Single-Server AI Deployments, Scientific Double-Precision Simulation, Cloud GPU Instances.
Enterprise HPC Clusters
The CAI-GPU-MI210 fits enterprise HPC clusters because its Aldebaran CDNA 2.0 die delivers 22.6 TFLOPS FP32 and strong double-precision capability in a standard dual-slot PCIe 4.0 x16 form factor, allowing deployment in existing x86 servers without OAM carrier hardware. Its 64 GB of HBM2e at roughly 1600 GB/s bandwidth keeps large CFD, molecular dynamics, and weather-modeling datasets resident on-card, avoiding PCIe transfer bottlenecks that throttle GPUs with smaller memory. At a 300 W maximum board power fed by a single 8-pin connector, cluster builders can populate multiple nodes on standard 1U-2U server power budgets. Programming uses ROCm/HIP and OpenCL 2.0, so porting existing CUDA or OpenACC codes follows a documented migration path.
Recomendado
AI Training and Inference Servers
For AI workloads, the MI210 provides 181 FP16 TFLOPS and 64 GB of HBM2e memory, a combination well suited to memory-bound transformer inference and mid-size training jobs. The 64 GB capacity accommodates larger batch sizes and model states than 40 GB-class competitors such as the NVIDIA A100 PCIe 40 GB, reducing checkpoint offloading. Community benchmarks (Level1Techs forums) report the card running models such as Mixtral 8x7B in single-server setups. The card's 300 W envelope allows several units per server on standard power, and ROCm-supported PyTorch/TensorFlow builds expose matrix cores for mixed-precision training. The key trade-off versus CUDA-class parts is software ecosystem maturity, so teams should validate framework versions before fleet deployment.
Recomendado
Academic and Research Computing
University research computing centers choose the MI210 for its balance of 64 GB HBM2e, open software (ROCm and OpenCL 2.0), and standard PCIe integration that fits heterogeneous clusters shared across many research groups. The 1000 MHz base / 1700 MHz boost clock profile with 22.6 TFLOPS FP32 handles simulation codes, while the 1600 GB/s memory bandwidth accelerates data-intensive genomics and imaging pipelines. Because the card draws at most 300 W through one 8-pin connector, existing academic server rooms can adopt it without power infrastructure upgrades. Its CDNA 2.0 architecture is also instruction-compatible with newer Instinct parts, protecting student and researcher code investments as clusters refresh toward MI300-class hardware.
Recomendado
Single-Server AI Deployments
The MI210 was explicitly positioned by AMD for single-server solutions, and the CAI-GPU-MI210 inherits that role: a standard PCIe 4.0 x16, dual-slot card that installs in mainstream rack or tower servers without specialized OAM baseboards. For a small enterprise running one inference or fine-tuning server, 64 GB HBM2e and 181 FP16 TFLOPS deliver substantial throughput, while 1600 GB/s bandwidth keeps KV-cache-heavy LLM inference responsive. The single 8-pin power connector and 300 W rating match ordinary server PSU provisions. Compared with multi-GPU OAM solutions, the trade-off is lower aggregate throughput per node, but deployment simplicity, standard cooling, and ROCm driver support make it a low-friction entry point.
Recomendado
Scientific Double-Precision Simulation
HPC simulation codes in computational chemistry, seismic processing, and physics rely on FP64 throughput and large device memory, both strengths of the MI200 family. The Aldebaran die's 58.2 billion transistors on a 724 mm2 6 nm die integrate matrix and vector units whose FP64 rates exceed same-generation PCIe-class parts, while 64 GB HBM2e holds full problem grids on-card at 1600 GB/s. PCIe 4.0 x16 at roughly 32 GB/s per direction is adequate when datasets stay resident between iterations. Developers can port CUDA Fortran or OpenACC codes through HIP and the ROCm compiler toolchain. The main design consideration is thermals: sustaining 300 W for hours requires verified server airflow and fan curves.
Recomendado
Cloud GPU Instances
Cloud and colocation providers deploy the MI210 in GPU instance offerings because the PCIe card format slots into standard server SKUs, enabling elastic multi-tenant AI and HPC services. Per-card 64 GB HBM2e allows partitioning memory-heavy models across tenants, and 181 FP16 TFLOPS positions the instance competitively against 40 GB A100 tiers at typically lower cost. Virtualization and partitioning are managed through the ROCm stack and SR-IOV-capable host platforms. At 300 W per card with a single 8-pin feed, density planning follows ordinary PCIe GPU server rules. Providers should monitor ROCm driver release cadence, as framework compatibility updates determine which customer AI stacks the instance can advertise.
Recomendado
Resumo dos produtos recomendados
Dados de referência de engenharia para CAI-GPU-MI210 — comparação, orientação de projeto e informações de conformidade.
Guia de seleção
Comparação com alternativas
| Parâmetro | Este produto | AMD Instinct MI250X | AMD Instinct MI250 | AMD Instinct MI100 | NVIDIA A100 PCIe 40 GB |
|---|---|---|---|---|---|
| Package / Form Factor | Dual-slot PCIe 4.0 x16 card | OAM module (not standard PCIe slot) | OAM module (not standard PCIe slot) | Dual-slot PCIe card / OAM | Dual-slot PCIe 4.0 x16 card - same |
| Brand | AMD | AMD | AMD | AMD | NVIDIA |
| Memory Capacity | 64 GB HBM2e | up to 128 GB HBM2e | up to 128 GB HBM2e | 32 GB HBM2e | 40 GB HBM2e |
| Architecture | CDNA 2.0 (Aldebaran) | CDNA 2.0 (dual-die Aldebaran) | CDNA 2.0 (dual-die Aldebaran) | CDNA 1.0 | NVIDIA Ampere |
| Software Stack | ROCm / OpenCL 2.0 | ROCm / OpenCL 2.0 - same | ROCm / OpenCL 2.0 - same | ROCm / OpenCL 2.0 - same | CUDA / cuDNN |
| Bus Interface | PCIe 4.0 x16 | OAM / Infinity Fabric interconnect | OAM / Infinity Fabric interconnect | PCIe 4.0 x16 | PCIe 4.0 x16 - same |
Principais diferenciais
- 64 GB HBM2e in a standard PCIe card (vs NVIDIA A100 PCIe 40 GB)
- Single-die simplicity vs dual-die OAM parts (vs AMD Instinct MI250X)
- Current-generation CDNA 2.0 vs previous CDNA 1.0 (vs AMD Instinct MI100)
Notas de projeto
The MI210 draws up to 300 W through a single 8-pin PCIe power connector, with the remaining envelope from the slot's 75 W budget. An 8-pin connector alone is conventionally rated near 150 W, so use the OEM-specified cabling or a proper 8-pin dual-source adapter, and size the PSU with at least 350-400 W of headroom for the card. Verify server PSU derating at high ambient temperature and confirm breaker/inrush behavior when multiple accelerators power on simultaneously.
As a dual-slot card dissipating up to 300 W, the MI210 requires two adjacent slot widths of unobstructed airflow. In server integration, confirm fan curves support sustained 300 W under full AI/HPC load, not just burst. Estimate: at 300 W with a target 25 C inlet and card-to-air thermal resistance typical of blower-cooled dual-slot cards, sustained full load drives exhaust well above ambient, so recirculation in dense chassis must be avoided. Monitor GPU junction temperature via ROCm management tooling during acceptance testing.
The MI210 provides no DirectX support and is not a gaming or display card - software support is via OpenCL 2.0 and ROCm only. Before fleet deployment, validate your PyTorch/TensorFlow versions against the target ROCm release, since CUDA-optimized code requires porting (HIP). Firmware and VBIOS updates should follow AMD's Instinct release notes for the MI210 specifically. When sourcing OEM part numbers such as CAI-GPU-MI210, confirm warranty terms, as support may route through the system integrator rather than AMD directly.
Informações de conformidade
Compliance declarations for the OEM CAI-GPU-MI210 configuration were not present in the provided web data; request regulatory documentation (RoHS/REACH declarations) from AMD or the system integrator at quotation.