Choose an accelerator by first checking whether its usable memory per device can accommodate your model and workload, then compare bandwidth, multi-device interconnect, software support, and the complete system needed to run it. Memory capacity answers “will it fit?”; it does not, by itself, answer “how quickly will it run?” The figures below are manufacturer-published specifications for named configurations, not independent benchmark results.
Start with memory per accelerator, not the server total
Per-device memory is the first screening test. If the model and its working data do not fit in one accelerator’s memory, deployment may require partitioning the workload across devices or offloading data elsewhere. Those approaches can be viable, but introduce system, software, and communication considerations.
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Keep local capacity distinct from aggregate capacity. An eight-accelerator system’s total memory is not automatically one large, interchangeable pool: the workload and software must be able to distribute data, and the devices must communicate effectively.
For inference, memory needs depend on more than the model’s parameter count. Model architecture, numerical precision, context length, batch size, concurrent requests, and runtime overhead all affect the working set. Training has its own memory demands. A universal “memory per parameter” rule cannot account for those differences, so size for the specific model and operating conditions.
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Compare exact accelerator configurations
The following are manufacturer-published specifications for the stated accelerator form factors. Capacity is per accelerator, and bandwidth is the published peak; neither peak bandwidth nor capacity alone predicts application throughput.
| Accelerator and configuration | Memory per accelerator | Memory type | Peak memory bandwidth | Source and qualification |
|---|---|---|---|---|
| NVIDIA H100 SXM | 80GB | HBM3 | 3.35TB/s | NVIDIA HGX component specification table, accessed 2026 |
| NVIDIA H200 SXM | 141GB | HBM3e | 4.8TB/s | NVIDIA HGX component specification table, accessed 2026; NVIDIA’s H200 product page labels specifications preliminary and subject to change |
| NVIDIA B200 SXM | 180GB | HBM3e | Up to 8TB/s | NVIDIA HGX component specification table, accessed 2026 |
| AMD Instinct MI300X OAM | 192GB | HBM3 | 5.325TB/s | AMD product specifications; bandwidth is based on an AMD Performance Labs calculation dated November 17, 2023, for the 750W OAM accelerator |
| AMD Instinct MI325X OAM | 256GB | HBM3e | 6TB/s | AMD product page; bandwidth is based on an AMD Performance Labs calculation dated September 26, 2024, and AMD says actual production results may vary |
Memory type is useful for identifying the configuration, but the label alone does not establish which accelerator will perform better. Compare the exact form factor and SKU rather than relying on a product-family name. In particular, NVIDIA’s cited current HGX table lists B200 SXM at 180GB per GPU; other NVIDIA search-result snippets describe 192GB product or platform configurations. Those figures refer to different configuration descriptions and should not be combined into a single B200 specification.
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Separate capacity, bandwidth, and interconnect
Capacity determines whether the working set fits
A larger local memory capacity can allow a model or workload to fit on fewer devices or reduce the need for partitioning or offload. It does not guarantee a faster result: compute, software, workload behavior, and system design also matter.
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Memory bandwidth is the peak rate at which data can move between an accelerator’s memory and processor. The product figures in the table are theoretical manufacturer specifications, not measured end-to-end throughput. Actual workload speed depends on whether the application can use that bandwidth and on other bottlenecks in the system.
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Interconnect affects multi-device work
When a workload spans accelerators, communication between them matters alongside local memory. NVIDIA reports GPU-to-GPU bandwidth of 900GB/s for HGX H100 and H200, and 1,800GB/s for HGX B200. AMD describes direct connectivity through Infinity Fabric for its eight-accelerator MI325X baseboard. These are platform and topology details, not substitutes for the per-device memory figures.
Read node totals as system specifications
System totals help estimate the resources in a complete platform, but are not the capacity available to a single accelerator. NVIDIA describes HGX H100, H200, and B200 as configurable four- or eight-GPU system designs. Its cited eight-GPU specification table lists these totals:
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| Eight-GPU HGX configuration | Aggregate GPU memory listed by NVIDIA |
|---|---|
| HGX H100 | 640GB |
| HGX H200 | 1.1TB |
| HGX B200 | 1.44TB |
NVIDIA’s DGX H100/H200 guide, a separate system reference, lists 640GB total H100 GPU memory and 1,128GB total H200 GPU memory for those systems. The difference between the HGX H200 table’s 1.1TB and the DGX guide’s 1,128GB is a reason to cite the specific platform and configuration rather than treating all system totals as interchangeable. NVIDIA’s deployment documentation also covers CPU memory, PCIe, networking, and storage; those are part of system fit, not GPU-memory capacity.
AMD says its UBB 2.0 baseboard can host up to eight MI325X accelerators and 2TB of HBM3e, with an Infinity Fabric mesh. That is a board-level aggregate, not 2TB of local memory on one accelerator.
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Benchmark the workload you intend to run
Use workload-specific evidence to decide how a configuration performs in practice. A useful comparison keeps the following conditions aligned and records them with the result:
- Model and model version, including architecture where relevant
- Precision and any quantization or other model changes
- Prompt length, output length, context length, and batch size
- Framework, kernels, runtime, compiler, and software versions
- Number of accelerators, server form factor, and interconnect topology
- Test date and whether the figure is a vendor claim or an independently measured result
Vendor performance comparisons may use scenario-specific assumptions and different software stacks. Treat them as evidence for the stated test, not as a universal ranking. The cited AMD MI300X and MI325X bandwidth figures are manufacturer calculations, and AMD’s MI325X page says actual production results may vary; they are not neutral comparative tests.
Check software and deployment fit before choosing
A device that meets a memory target can still be a poor fit if the required model, operators, kernels, or framework are not supported in the intended environment. AMD associates MI325X with ROCm. NVIDIA’s HGX and DGX materials describe complete AI systems, rather than only standalone accelerator modules.
Before committing to a platform, confirm support for the target model and framework, the required kernels and operators, and the operating environment. Then check that the server configuration can provide the necessary power, cooling, networking, and storage. These deployment constraints can change which nominally suitable accelerator is practical.
Quick Recap
A practical selection sequence
- Define the workload. Record whether the job is inference or training, the model, precision, context and output lengths, batch size, and expected concurrency.
- Check local capacity. Compare the workload’s memory needs with usable memory on the exact accelerator SKU; do not use a node total as if it belonged to one device.
- Decide whether multiple devices are acceptable. If one device is insufficient, verify that the model and software can partition the workload and examine the platform’s interconnect and topology.
- Compare bandwidth and measured behavior. Use peak bandwidth as a specification, then benchmark the real workload under recorded, comparable conditions.
- Validate the software stack. Confirm framework, model, kernel, operator, runtime, and operating-environment support for the specific platform.
- Validate the complete deployment. Check server configuration, power, cooling, networking, storage, availability, and cost for the environment in which the workload will run.
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