GPU data sources
Reference material for populating and verifying boaviztapi/data/crowdsourcing/gpu_specs.csv.
See Add a new GPU for the column definitions themselves.
Every row should carry at least one URL in the source column. Prefer primary
sources (vendor datasheets, architecture whitepapers, OEM option guides) over tech
press articles, which are harder to audit and more likely to disappear.
Die size → die_surface
die_surface is not the raw die area. It is the effective wafer area per die,
including kerf, circular-wafer edge losses and yield, as computed by
_calculate_effective_area_on_circular_wafer() in
boaviztapi/models/component/gpu.py. For a multi-die package it is
effective_area(per_die_area) * unit.
So what you need to source is the raw die area in mm² for a single compute die.
| Source | Use for |
|---|---|
| TechPowerUp GPU Database | Best per-SKU source: die size, transistor count, process node, memory config, board form factor. One page per SKU |
| NVIDIA resources portal | Architecture whitepapers, e.g. Hopper. Authoritative on die size and transistor count |
| List of Nvidia GPUs | Comprehensive die-size tables with references |
| List of AMD GPUs | Same, for AMD |
| Chips and Cheese | Die-level analysis, essential for chiplet parts such as MI300X |
| Locuza | Annotated die-shot measurements |
| SemiAnalysis | Reticle limits, multi-die packaging |
Wikipedia architecture pages, useful when a SKU is not listed individually: Pascal · Volta · Turing · Ampere · Ada Lovelace · Hopper · Blackwell · CDNA · RDNA 2
Consistency check
Two SKUs built on the same die must have identical die_surface. For example
every GA100 part (A100 PCIe/SXM4, 40 GB and 80 GB) is 826 mm² of raw die and
therefore 2876.29, regardless of memory capacity or board type.
VRAM capacity and die count → vram, number
vram is the capacity per GPU in GB, never an instance or node total.
number is the count of VRAM dies (HBM stacks, or GDDR packages).
| Source | Use for |
|---|---|
| NVIDIA product pages: A100, H100, H200, L4, P100, DGX B200 | Capacity, bandwidth and memory type per SKU |
| AMD Instinct · MI300X · Radeon PRO | Official AMD specifications |
| TechPowerUp GPU Database | Memory bus width and chip count, from which number can be derived |
Mass → mass, mass_heatsink, mass_casing
The hardest fields to source. Vendor briefs often omit weight, so OEM option catalogues are usually the practical answer.
| Source | Use for |
|---|---|
| Lenovo Press GPU options | Best single source. Per-GPU product guides listing net weight and dimensions, e.g. A100 PCIe, RTX PRO 6000 |
| NVIDIA product briefs | Some state weight directly (T4, A10, L40S, V100 PCIe, M60, P4 are sourced this way today) |
| HPE QuickSpecs, Dell PowerEdge manuals | Equivalent OEM data for cards Lenovo does not carry |
Empty mass fields are not neutral
A blank mass, mass_heatsink or mass_casing silently falls back to the
DEFAULT row of boaviztapi/data/archetypes/components/gpu.csv — currently
1.69 kg / 0.90 kg / 0.79 kg, i.e. a large datacentre card. That is a
significant overestimate for single-slot or low-profile boards.
PCB area → pwb_surface
In practice this is derived from the card outline dimensions, not a measured PCB area, so the reference is the mechanical-dimensions section of a product brief. Common form factors:
| Form factor | Dimensions | Area |
|---|---|---|
| Full-height, full-length (FHFL) | 111.15 × 266.7 mm | 296.4 cm² |
| Three-quarter length | 111.15 × 210.9 mm | 234.4 cm² |
| Half-height, half-length (short) | 68.9 × 135.2 mm | 93.1 cm² |
| SXM2 / SXM4 module | — | 109.2 / 105.0 cm² |
For SXM and OAM module footprints: SXM socket · OCP Accelerator Module (OAM)
Note
234.36 currently appears on 16 of 32 rows. It functions as a default for
generic full-size PCIe cards rather than a per-card measurement. Treat it as a
modelling assumption, not sourced data.
Cloud instance ↔ GPU mapping
Not part of gpu_specs.csv, but these are the authority for which GPU SKU an
archetype in boaviztapi/data/archetypes/server.csv should name, and for how
much VRAM each instance exposes per GPU.
| Provider | Reference |
|---|---|
| AWS | Accelerated computing instance types |
| GCP | GPU machine types |
| Azure | GPU-accelerated VM sizes |
Citing sources in the CSV
Put one or more URLs in the source column, separated by ;:
NVIDIA A100 PCIe 80GB,NVIDIA,1,6,80,2876.29,234.36,19000,1000,0,0.3,0.643,1.17,https://www.nvidia.com/content/dam/en-zz/Solutions/Data-Center/a100/pdf/PB-10577-001_v02.pdf;https://en.wikipedia.org/wiki/Ampere_(microarchitecture)
The value is surfaced through the API in the completion message for each
attribute, for example
"Completed from name based on <source>.", so a row left without a source
produces a message with no usable provenance.