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What memory capacity and bandwidth tell you
Capacity: whether the workload can fit
Memory capacity is the amount of high-bandwidth memory (HBM) on one accelerator. It matters when placing model weights, runtime overhead, and the memory needed for the intended context length or batch size. A larger pool may let a workload fit that otherwise would not, but capacity alone does not show how quickly it will run.
Bandwidth: the published transfer ceiling
Memory bandwidth describes how quickly data can move between the accelerator and its memory. Vendor pages publish peak or theoretical figures, not a promise of application throughput. The outcome for a particular model also depends on workload, software, and system configuration. Compare bandwidth as a specification, and use benchmarks with matching model, precision, software, and hardware setup to evaluate performance.
Compare exact accelerator specifications
The figures below are manufacturer-published specifications, not independent measurements. Capacity and bandwidth are per accelerator unless identified as a platform total.
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- System Compatibility Note: This 2-slot card measures 271 x 112 x 39 mm and requires a single 12V-2x6-pin power connector. Please verify chassis and PSU compatibility before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- Professional Intel Arc Pro B70 GPU: Built on the Intel Xe2-HPG architecture, it features 32 Xe cores and 256 XMX engines, designed to accelerate AI, rendering, and complex visualization workloads.
- Massive 32GB GDDR6 VRAM: Equipped with 32GB of high-speed GDDR6 memory on a 256-bit bus, running at 19 Gbps, which allows for handling large AI models and complex datasets locally.
- High-Performance Engine Clock: Delivers an engine clock of 2540 MHz, providing the compute power needed for demanding professional applications and AI inference.
| Accelerator | Memory type | Capacity per device | Published peak bandwidth | Form factor / configuration | Availability evidence |
|---|---|---|---|---|---|
| NVIDIA H100 SXM5 | Not stated in the cited AMD comparison | 80 GB | 3.35 TB/s | SXM5, as named in AMD’s comparison | Not established by the cited specification pages |
| NVIDIA H200 SXM | HBM3e | 141 GB | 4.8 TB/s | SXM, as named in AMD’s comparison | Not established by the cited specification pages |
| AMD Instinct MI325X | HBM3e | 256 GB | 6 TB/s peak theoretical | Accelerator; AMD also describes an eight-module baseboard | Not established by the cited specification pages |
AMD’s comparison gives H100 SXM5 and H200 SXM figures alongside MI300- and MI350-series products; check the exact model column and page revision when comparing other variants. AMD ROCm workload optimization and AMD’s MI300 series page provide comparison context. NVIDIA lists H200’s 141 GB HBM3e and 4.8 TB/s on its H200 product page; AMD lists MI325X’s 256 GB HBM3e and 6 TB/s peak theoretical bandwidth in its MI325X product article.
Keep device memory separate from system totals
A server or baseboard may combine memory across multiple accelerators. That aggregate is not the memory capacity of one GPU, and it does not automatically mean a workload can use the entire pool as one seamless allocation; the platform and software configuration matter.
Rank #2
- NVIDIA Volta GV100 Architecture — 4,608 CUDA Cores, 640 1st-Gen Tensor Cores delivering 14 TFLOPS FP32 and 112 TFLOPS deep learning performance for AI training, inference, HPC, and scientific computing workloads
- 32GB HBM2 ECC Memory — 900 GB/s Bandwidth — High-bandwidth memory on a 4096-bit bus with ECC error correction provides the memory capacity and throughput required for the largest AI models, simulations, and datasets
- PCIe 3.0 x16 Interface — 250W TDP — Standard PCIe Gen3 connectivity with passive cooling designed for enterprise rack server deployment in HPE ProLiant, Dell PowerEdge, and Supermicro platforms with adequate chassis airflow
- NVLink — Scale to 96GB Unified Memory — Connect two V100 GPUs via NVLink at 300 GB/s bi-directional bandwidth to scale GPU memory from 32GB to 96GB for larger AI training and HPC workloads
- Multi-Precision Computing — Supports FP64 (7 TFLOPS), FP32 (14 TFLOPS), FP16 (112 TFLOPS) and INT8 precision modes for flexible deployment across training, inference, and scientific simulation workloads
| Platform configuration | Accelerator count | Published aggregate memory | How to interpret it |
|---|---|---|---|
| AMD MI325X baseboard | 8 modules | 2 TB HBM3e | Platform total, not one accelerator’s capacity |
| NVIDIA HGX H100 baseboard configuration | Multi-GPU; check the specified configuration | Up to 640 GB | Platform total, not one accelerator’s capacity |
| NVIDIA HGX H200 baseboard configuration | Multi-GPU; check the specified configuration | 1,128 GB | Platform total, not one accelerator’s capacity |
NVIDIA’s HGX figures describe particular multi-GPU configurations, so confirm the exact baseboard and system before using them in a comparison. See NVIDIA HGX components documentation. AMD’s eight-module MI325X baseboard total is described in its MI325X product article.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Check availability as a procurement question
A manufacturer’s specification page is not evidence that a GPU is in stock, orderable in your country, or deliverable by a particular date. The cited product materials do not establish current stock, price, delivery windows, or regional orderability for these models.
Rank #3
- Powered by Radeon AI PRO R9700 - Supercharge you workflow with the cutting-edge RDNA 4 Architecture and 2nd-gen AI Accelerators.
- 32GB GDDR6 with 256-bit memory bus - Tackle larger, more complex projects without limits.
- PCIe Gen 5 - Unlock lightning-fast data transfers with PCIe Gen 5 support.
- GIGABYTE TURBO Fan Cooling System - Indented metal cover and blower fan increase airflow intake, while the vapor chamber, all copper heat sink, and metal frame offer efficient heat dissipation. Optimized airflow design allows for easy multi-GPU scalability.
- Double Ball Bearing Fan - Delivers superior heat resistance and rotational efficiency for better performance and a longer lifespan compared to conventional sleeve fans.
Ask a supplier to confirm the exact accelerator SKU and complete system configuration, your region, quantity, price basis, and estimated delivery window. Get confirmation for the offered system rather than assuming that a published accelerator specification describes what the supplier can provide.
Quick Recap
Best Value
- Professional AI & Creator Workstation: AMD Radeon AI PRO R9700 GPU with 32GB GDDR6 is engineered for AI development, professional content creation, and compute-intensive workloads.
- Massive 32GB Memory Capacity: 32GB of GDDR6 memory on a 256-bit bus provides ample bandwidth for large AI models, 8K video editing, and complex 3D rendering.
- Advanced RDNA 4 with AI Accelerators: 64 Compute Units with 3rd Gen Ray Tracing and dedicated 2nd Gen AI Accelerators for groundbreaking AI performance and visual computing.
- Professional Blower Cooling: Efficient single blower design exhausts heat directly out of the chassis, ideal for multi-GPU workstation and server configurations.
- Enterprise-Grade Thermal Solution: Vapor chamber heatsink with industrial Honeywell PTM7950 thermal interface material ensures reliable cooling under sustained professional loads.
Rank #4
- System Compatibility Note: 2-slot card, 271x112x39mm, single 8-pin power, 200W TDP. Verify chassis clearance and PSU capacity before purchase.
- Dedicated Support: Please contact us directly through Amazon for any product questions or assistance you may require.
- 24GB GDDR6 on 192-Bit Bus: Massive 24GB memory with 456 GB/s bandwidth – ideal for LLMs, AI inference, 3D rendering, and generative design.
- Intel Xe2-HPG Architecture: Built on Intel's next-gen architecture with 20 Xe cores and 160 XMX engines for AI acceleration (197 INT8 TOPS).
- PCIe 5.0 Support: PCI Express 5.0 x16 interface for maximum bandwidth with the latest workstation platforms.
A practical comparison checklist
- Set the workload requirement. Estimate memory needed for weights, runtime overhead, and the target context length or batch size.
- Compare per-device capacity. Check the exact SKU and memory type; do not substitute a node or baseboard total for a device figure.
- Record published bandwidth accurately. Label peak or theoretical values, and do not treat them as measured throughput.
- Match configurations. Compare accelerator count, form factor, interconnect, and platform design when evaluating system-level memory or performance.
- Validate performance separately. Use relevant benchmark results only when model, precision, software, and system setup are documented and comparable.
- Confirm procurement details. Obtain supplier confirmation for the exact model, region, order quantity, complete system, price basis, and delivery estimate.
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
