GPU Specifications
Database
Specifications for 18 NVIDIA and AMD accelerator records, each showing the manufacturer page it came from and the date it was last checked.
This database lists what NVIDIA and AMD publish for their current AI accelerators: memory, bandwidth, board power, precision performance, and form factor. Each record names the level it describes, because manufacturers publish some figures per accelerator, some only for an eight-GPU platform, and some only for a full rack. Nothing here is converted between those levels or estimated.
The records
How to read these records
- One accelerator means the figures describe a single GPU.
- Platform means the figures are totals for a multi-GPU baseboard or module, such as an eight-GPU HGX platform.
- Rack means the figures are totals for a whole rack-scale system, such as an NVL72.
- Preliminary specification means the manufacturer states the values are subject to change.
NVIDIA Rubin GPU
Preliminary specificationNVIDIA footnote: preliminary information, all values are up to and subject to change. The inference figure is sparse; the training figure is dense.
SLYD reading Announced generation. Relevant to roadmap planning, not to a deployment you are specifying now.
NVIDIA HGX Rubin NVL8
Preliminary specificationNVIDIA footnote: preliminary information, all values are up to and subject to change.
SLYD reading Announced 8-GPU server platform for the Rubin generation.
NVIDIA GB300 NVL72
ShippingNVIDIA publishes this figure as sparse then dense. The dense figure is the one to use for capacity planning unless your workload actually exploits sparsity. These are whole-rack figures across 72 GPUs, not per-accelerator figures.
SLYD reading Rack-scale unit for frontier training and large reasoning-model inference. Specified and sited as a rack, not as servers.
NVIDIA GB200 NVL72
ShippingNVIDIA publishes this figure as sparse then dense. The dense figure is the one to use for capacity planning unless your workload actually exploits sparsity. These are whole-rack figures across 72 GPUs, not per-accelerator figures.
SLYD reading Rack-scale unit for large-model training and high-throughput inference.
NVIDIA GB200 Grace Blackwell Superchip
ShippingModule-level figures covering two GPUs and one CPU together.
SLYD reading The module NVL72 racks are built from. Useful for understanding rack composition.
NVIDIA HGX B300
ShippingNVIDIA publishes this figure as sparse then dense. The dense figure is the one to use for capacity planning unless your workload actually exploits sparsity. NVIDIA publishes these as 8-GPU platform totals. Dividing by eight does not give a supported per-accelerator figure. NVIDIA states HGX B300 is shipping now.
SLYD reading Eight-GPU server platform for training and inference where a full rack-scale system is not the deployment unit.
NVIDIA HGX B200
ShippingNVIDIA publishes this figure as sparse then dense. The dense figure is the one to use for capacity planning unless your workload actually exploits sparsity. NVIDIA publishes these as 8-GPU platform totals. Dividing by eight does not give a supported per-accelerator figure. NVIDIA states HGX B200 is shipping now.
SLYD reading Eight-GPU server platform. Materially stronger FP64 than HGX B300, which matters for mixed AI and HPC sites.
NVIDIA RTX PRO 6000 Blackwell Server Edition
ShippingPassive thermal design: cooling is provided by the host server, so the server must be qualified for it.
SLYD reading Multi-GPU server deployments needing large GDDR7 capacity rather than HBM bandwidth.
NVIDIA RTX PRO 6000 Blackwell Workstation Edition
ShippingSLYD reading Single-GPU workstations. NVIDIA positions this edition for maximum single-GPU throughput.
NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
ShippingThis is a desktop workstation card, not a laptop or mobile part. NVIDIA publishes it at 300 W in the same dual-slot form factor as the Server Edition.
SLYD reading Dense workstation configurations of up to four GPUs, where total power and thermal budget constrain the build.
NVIDIA H200 SXM
ShippingNVIDIA footnotes these as preliminary specifications that may be subject to change, and marks performance figures as with sparsity. Board power is configurable, so the design figure depends on the OEM system.
SLYD reading Widely deployed generation with a mature software stack. Memory capacity suits models that do not fit an 80 GB part.
NVIDIA H200 NVL
ShippingAir-cooled PCIe form factor. The same memory and bandwidth as the SXM part at a lower configurable board power, which is what makes it deployable in air-cooled racks.
SLYD reading Sites that need H200 memory capacity but cannot take an SXM baseboard or liquid cooling.
NVIDIA H100 SXM
ShippingBoard power is configurable, so the design figure depends on the OEM system.
SLYD reading The most broadly supported datacenter part in the current installed base. Memory capacity is the usual constraint.
NVIDIA H100 NVL
ShippingMore memory and bandwidth than the SXM H100 at roughly half the board power, in an air-cooled PCIe card.
SLYD reading Air-cooled deployments and lower-density racks that cannot support 700 W SXM modules.
AMD Instinct MI355X
ShippingAMD publishes cooling for this part as passive and active. There is no universal liquid-cooling requirement across the Instinct line; the requirement comes from the OEM system.
SLYD reading Largest published memory capacity of the AMD line. Suits large-model serving where capacity per accelerator is the binding constraint.
AMD Instinct MI350X
ShippingSame published memory capacity as MI355X at a lower board power and lower peak matrix performance.
SLYD reading MI355X memory capacity in a 1000 W thermal envelope, for racks that cannot take 1400 W modules.
AMD Instinct MI325X
ShippingSLYD reading Prior generation with high memory capacity. Relevant where CDNA 3 software support is already established.
AMD Instinct MI300X
ShippingSLYD reading Lowest board power of the Instinct records here, with memory capacity above the 80 GB Hopper part.
Comparison Table
Memory and bandwidth for every record, with the level each figure describes. Precision performance is left to the cards above, because the precisions the two manufacturers publish are not the same and a shared column would invite a comparison the sources do not support.
| Record | Architecture | Level | Memory | Bandwidth | Board power | Status |
|---|---|---|---|---|---|---|
| NVIDIA Rubin GPU | Rubin | One accelerator | 288 GB HBM4 | 22 TB/s | Not published | Preliminary specification |
| NVIDIA HGX Rubin NVL8 | Rubin | 8-GPU HGX platform | 2.3 TB HBM4 | 176 TB/s | Not published | Preliminary specification |
| NVIDIA GB300 NVL72 | Grace Blackwell Ultra | 72-GPU rack-scale system | 20 TB, up to 576 TB/s | Not published | Not published | Shipping |
| NVIDIA GB200 NVL72 | Grace Blackwell | 72-GPU rack-scale system | 13.4 TB HBM3E, 576 TB/s | Not published | Not published | Shipping |
| NVIDIA GB200 Grace Blackwell Superchip | Grace Blackwell | Superchip module | 372 GB HBM3E, 16 TB/s | Not published | Not published | Shipping |
| NVIDIA HGX B300 | Blackwell Ultra | 8-GPU HGX platform | 2.1 TB | Not published | Not published | Shipping |
| NVIDIA HGX B200 | Blackwell | 8-GPU HGX platform | 1.4 TB | Not published | Not published | Shipping |
| NVIDIA RTX PRO 6000 Blackwell Server Edition | Blackwell, RTX PRO | One accelerator | 96 GB GDDR7 with ECC | Not published | 400 to 600 W | Shipping |
| NVIDIA RTX PRO 6000 Blackwell Workstation Edition | Blackwell, RTX PRO | One accelerator | 96 GB GDDR7 with ECC | Not published | 600 W | Shipping |
| NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition | Blackwell, RTX PRO | One accelerator | 96 GB GDDR7 with ECC | Not published | 300 W | Shipping |
| NVIDIA H200 SXM | Hopper | One accelerator | 141 GB HBM3e | 4.8 TB/s | Up to 700 W, configurable | Shipping |
| NVIDIA H200 NVL | Hopper | One accelerator | 141 GB HBM3e | 4.8 TB/s | Up to 600 W, configurable | Shipping |
| NVIDIA H100 SXM | Hopper | One accelerator | 80 GB HBM3 | 3.35 TB/s | Up to 700 W, configurable | Shipping |
| NVIDIA H100 NVL | Hopper | One accelerator | 94 GB HBM3 | 3.9 TB/s | 350 to 400 W, configurable | Shipping |
| AMD Instinct MI355X | CDNA 4 | One accelerator | 288 GB HBM3E | 8 TB/s peak | 1400 W TBP | Shipping |
| AMD Instinct MI350X | CDNA 4 | One accelerator | 288 GB HBM3E | 8 TB/s | 1000 W TBP | Shipping |
| AMD Instinct MI325X | CDNA | One accelerator | 256 GB HBM3E | 6 TB/s | 1000 W peak TBP | Shipping |
| AMD Instinct MI300X | CDNA | One accelerator | 192 GB HBM3 | 5.3 TB/s | 750 W peak TBP | Shipping |
Reading the Database for a Decision
What the specifications here can and cannot tell you about a workload.
Start with memory capacity
The one hard constraint
Memory capacity decides whether a model, its optimizer state, and its KV cache fit at all. Nothing else on this page can compensate for a model that does not fit. Work out your capacity requirement first, then filter the records that clear it.
Treat bandwidth as a ceiling
Not a performance score
Memory bandwidth bounds how fast weights can be streamed, which matters most for memory-bound decode. It does not predict end-to-end throughput or latency on its own: model architecture, quantization, serving software, batching, and topology all move the result.
Match the level to your unit
Accelerator, platform, or rack
If you are buying servers, compare the platform records. If you are siting a rack-scale system, compare the rack records. Comparing a rack figure against a single-accelerator figure is the most common way these tables get misread.
Check power and cooling early
Before the shortlist hardens
Board power here is the accelerator, not the server or the rack. Several parts are configurable, and the design figure comes from the OEM system. Air-cooled PCIe variants exist specifically for sites that cannot take high-power SXM or OAM modules.
Common Questions
Why do some records show figures for a rack or an eight-GPU platform instead of one accelerator?
Because that is the only level at which the manufacturer publishes them. NVIDIA publishes GB300 NVL72 and GB200 NVL72 as 72-GPU rack systems, and HGX B300 and HGX B200 as eight-GPU platforms. Dividing a platform total by eight does not produce a supported per-accelerator figure, so this database labels the scope of every record instead of converting between them.
What does sparse and dense mean in the performance figures?
A sparse figure assumes the workload can exploit structured sparsity in the model weights. A dense figure does not. NVIDIA publishes several precision figures as sparse then dense, and the two differ by up to a factor of two. Use the dense figure for capacity planning unless you have confirmed your model and serving stack actually exploit sparsity.
Why does this database not show prices?
Accelerator pricing depends on configuration, quantity, channel, geography, and the week you ask. A static range with no currency, condition, date, or source is not information a buyer can act on, and SLYD has no governed public price record to publish in its place. Contact SLYD for pricing against a specific configuration.
Does more memory bandwidth mean better performance for my workload?
Not on its own. Memory capacity determines whether a model and its KV cache fit at all, which is a hard constraint. Beyond that, real throughput and latency depend on model architecture, quantization, serving software, batching, and system topology. Treat the figures here as the constraints to design within, not as a performance ranking.
How current are these specifications?
Every record carries the manufacturer page it was read from and the date it was last checked. Those dates are shown on each record and collected in the source register at the foot of this page. Records marked as preliminary carry the manufacturer's own notice that the values are subject to change.
How this database is maintained
Each record is read from the manufacturer's own product page for that product. Reseller listings, comparison articles, and summaries are not used as the source for any figure here.
What is not done
- No conversion between levels. Where a manufacturer publishes only an eight-GPU platform total, that total is published as a platform figure. It is not divided by eight to state a per-accelerator number.
- No filling in gaps. A field the manufacturer does not publish
on the cited page is left out of that record rather than estimated or marked
TBD. - No stripping of caveats. Sparsity basis, preliminary-specification notices, and configurable-power notes travel with the figure, because several of these numbers are misleading without them.
- No price column. Price, availability, and lead time are volatile commercial facts. There is no governed public record behind them, so they are absent rather than stale.
Precision figures
NVIDIA and AMD do not publish the same set of precisions, and the precisions they share are not always measured on the same basis. Precision performance therefore appears on the individual records with its own basis attached, and not as a shared column that would invite a comparison the sources do not support.
Limits of this page
These are published specifications, not measured results on your workload, and not a substitute for the OEM system specification you will actually buy. Board power is the accelerator alone; server input power and rack design load are larger and are covered by the power and cooling calculator. Cooling requirements come from the OEM system, not from the accelerator's cooling field.
Sources and basis
Every specification on this page was read from one of the manufacturer pages below, and is reproduced with that manufacturer's own sparsity, configurability, and preliminary-specification notices attached. Each entry lists the records it supports. Per-record check dates also appear on the individual cards.
AMD Instinct MI300X
https://www.amd.com/en/products/accelerators/instinct/mi300/mi300x.html
AMD Instinct MI325X
https://www.amd.com/en/products/accelerators/instinct/mi300/mi325x.html
AMD Instinct MI350X
https://www.amd.com/en/products/accelerators/instinct/mi350/mi350x.html
AMD Instinct MI355X
https://www.amd.com/en/products/accelerators/instinct/mi350/mi355x.html
NVIDIA GB200 NVL72, NVIDIA GB200 Grace Blackwell Superchip
NVIDIA GB300 NVL72
NVIDIA H100 SXM, NVIDIA H100 NVL
NVIDIA H200 SXM, NVIDIA H200 NVL
NVIDIA Rubin GPU, NVIDIA HGX Rubin NVL8, NVIDIA HGX B300, NVIDIA HGX B200
NVIDIA RTX PRO 6000 Blackwell Server Edition, NVIDIA RTX PRO 6000 Blackwell Workstation Edition, NVIDIA RTX PRO 6000 Blackwell Max-Q Workstation Edition
https://www.nvidia.com/en-us/products/workstations/professional-desktop-gpus/rtx-pro-6000-family/
The workload-fit note on each record, and the guidance on reading the database for a decision.
SLYD's reading of the published specifications. Not a manufacturer claim and not a measured result.
The absence of price, availability, and lead time.
These are volatile commercial facts. SLYD publishes no governed public record for them, so this page omits them rather than showing a stale figure.
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