SCS Flavor Naming Standard: Implementation and Testing Notes

Introduction

The three major versions of the standard that exist so far are very similar, and deliberately so. Therefore, the procedures needed to implement or test them are very similar as well. Yet, this document will only cover v3, because v1 and v2 are already obsolete by the time of writing.

Implementation notes

Every flavor whose name starts with SCS- must conform with the naming scheme laid down in the standard.

Operational tooling

Syntax check

The test suite comes with a handy command-line utility that can be used to validate flavor names, to interactively construct a flavor name via a questionnaire, and to generate prose descriptions for given flavor names. See the README for more details.

The functionality of this script is also (partially) exposed via the web page https://flavors.scs.community/, which can both parse SCS flavors names as well as generate them.

With the OpenStack tooling (python3-openstackclient, OS_CLOUD) in place, you can call cli.py -v parse v3 $(openstack flavor list -f value -c Name) to get a report on the syntax compliance of the flavor names of the cloud environment.

Flavor creation

The OpenStack Flavor Manager from OSISM will create a whole set of flavors in one go. To that end, it provides different options: either the standard mandatory and possibly recommended flavors can be created, or the user can set a file containing his flavors.

GPU table

The most commonly used datacenter GPUs are listed here, showing what GPUs (or partitions of a GPU) result in what GPU part of the flavor name. We provide these for convenience; most values are from data sheets and not based on own testing. Providers must look up the values (SMs/CUs/EUs and VRAM) really provided to users and correctly fill these into the SCS names. This is in particular true for the MIG configurations.

Nvidia (N)

We show the most popular recent generations here. Older one are of course possible as well.

Ampere (a)

One Streaming Multiprocessor on Ampere has 64 (A30, A100) or 128 Cuda Cores (A10, A40).

GPUs without MIG (one SM has 128 Cuda Cores and 4 Tensor Cores):

Nvidia GPU	Tensor C	Cuda Cores	SMs	VRAM	SCS name piece
A10	288	9216	72	24G GDDR6	GNa-72-24
A40	336	10752	84	48G GDDR6	GNa-84-48

GPUs with Multi-Instance-GPU (MIG), where GPUs can be partitioned and the partitions handed out as as pass-through PCIe devices to instances. One SM corresponds to 64 Cuda Cores and 4 Tensor Cores.

Nvidia GPU	Fraction	Tensor C	Cuda Cores	SMs	VRAM	SCS GPU name
A30	1/1	224	3584	56	24G HBM2	GNa-56-24
A30	1/2	112	1792	28	12G HBM2	GNa-28-12
A30	1/4	56	896	14	6G HBM2	GNa-14-6
A30X	1/1	224	3584	56	24G HBM2e	GNa-56h-24h
A100	1/1	432	6912	108	80G HBM2e	GNa-108h-80h
A100	1/2	216	3456	54	40G HBM2e	GNa-54h-40h
A100	1/4	108	1728	27	20G HBM2e	GNa-27h-20h
A100	1/7	60+	960+	15+	10G HBM2e	GNa-15h-10h+
A100X	1/1	432	6912	108	80G HBM2e	GNa-108-80h

[+] The precise numbers for the 1/7 MIG configurations are not known by the author of this document and need validation.

Ada Lovelave (l)

No MIG support, 128 Cuda Cores and 4 Tensor Cores per SM.

Nvidia GPU	Tensor C	Cuda Cores	SMs	VRAM	SCS name piece
L4	232	7424	58	24G GDDR6	GNl-58-24
L40	568	18176	142	48G GDDR6	GNl-142-48
L40G	568	18176	142	48G GDDR6	GNl-142h-48
L40S	568	18176	142	48G GDDR6	GNl-142hh-48

Nvidia GPU	Tensor C	Cuda Cores	SMs	VRAM	SCS name piece
RTX2000 Ada	88	2816	22	16G GDDR6	GNl-22-16
RTX4000 Ada	192	6144	48	20G GDDR6	GNl-48-20
RTX4500 Ada	240	7680	60	24G GDDR6	GNl-60-24
RTX5000 Ada	400	12800	100	32G GDDR6	GNl-100-32
RTX5880 Ada	440	14080	110	48G GDDR6	GNl-110-48
RTX6000 Ada	568	18176	142	48G GDDR6	GNl-142-48

Grace Hopper (g)

These have MIG support and 128 Cuda Cores and 4 Tensor Cores per SM.

Nvidia GPU	Fraction	Tensor C	Cuda Cores	SMs	VRAM	SCS GPU name
H100	1/1	528	16896	132	80G HBM3	GNg-132-80h
H100	1/2	264	8448	66	40G HBM3	GNg-66-40h
H100	1/4	132	4224	33	20G HBM3	GNg-33-20h
H100	1/7	72+	2304+	18+	10G HBM3	GNg-18-10h+
H200	1/1	528	16896	132	141G HBM3e	GNg-132-141h
H200	1/2	264	16896	66	70G HBM3e	GNg-66-70h
...

[+] The precise numbers for the 1/7 MIG configurations are not known by the author of this document and need validation.

Blackwell (b) and Blackwell Ultra (u)

These have MIG support and 128 Cuda Cores and 4 Tensor Cores per SM.

Nvidia GPU	Fraction	Tensor C	Cuda Cores	SMs	VRAM	SCS GPU name
GB200	1/1	640	20480	160	192G HBM3e	GNb-160-192h
GB200	1/2	320	10240	80	96G HBM3e	GNb-80-96h
GB200	2/7	88+	5632+	44+	45G HBM3e+	GNb-44-45h+
GB200	1/7	44+	2816+	22+	23G HBM3e+	GNb-22-23h+
GB300	1/1	640	20480	160	288G HBM3e	GNu-160-288h
GB300	1/2	320	10240	80	144G HBM3e	GNu-80-144h
...

[+] The precise numbers for the 1/7 MIG configurations are not known by the author of this document and need validation.

Note that Blackwell Ultra tensor cores have significant enough changes vs. Blackwell that we gave the BW Ultra GPUs a new letter u. In particular, FP4 tensor performance is over 150% of std. Blackwell and has more Special Function Units (which helps attention) but has regressed INT8 performance.

Nvidia GPU	Fraction	Tensor C	Cuda Cores	SMs	VRAM	SCS GPU name
RTX Pro2000 Blackwell	1/1	136	4352	34	16G GDDR7	GNb-34-16
RTX Pro4000 Blackwell	1/1	280	8960	70	24G GDDR7	GNb-70-24
RTX Pro4500 Blackwell	1/1	328	10496	82	32G GDDR7	GNb-82-32
RTX Pro5000 Blackwell	1/1	440	14080	110	72G GDDR7	GNb-110-72
RTX Pro5000 Blackwell	1/2	220	7040	55	36G GDDR7	GNb-55-36
RTX Pro6000 Blackwell	1/1	752	26064	188	96G GDDR7	GNb-188-96
RTX Pro6000 Blackwell	1/2	376	13032	94	48G GDDR7	GNb-94-48
RTX Pro6000 Blackwell	1/4	188	6516	47	24G GDDR7	GNb-47-24

AMD Radeon (A)

CDNA 2 (2)

One CU contains 64 Stream Processors.

AMD Instinct	Stream Proc	CUs	VRAM	SCS name piece
Inst MI210	6656	104	64G HBM2e	GA2-104-64h
Inst MI250	13312	208	128G HBM2e	GA2-208-128h
Inst MI250X	14080	229	128G HBM2e	GA2-220-128h

CDNA 3 (3)

SRIOV partitioning is possible, resulting in pass-through for up to 8 partitions, somewhat similar to Nvidia MIG. 4 Tensor Cores and 64 Stream Processors per CU.

AMD GPU	Tensor C	Stream Proc	CUs	VRAM	SCS name piece
Inst MI300X	1216	19456	304	192G HBM3	GA3-304-192h
Inst MI325X	1216	19456	304	288G HBM3	GA3-304-288h

CDNA 4 (4)

SRIOV partitioning is possible, resulting in pass-through for up to 8 partitions, somewhat similar to Nvidia MIG. 4 Tensor Cores and 64 Stream Processors per CU.

AMD GPU	Tensor C	Stream Proc	CUs	VRAM	SCS name piece
Inst MI350X	1024	16384	256	288G HBM3e	GA4-256-288h
Inst MI355X	1024	16384	256	288G HBM3e	GA4-256h-288h

The Instinct MI355X has a higher watttage and thus slightly higher clocks than the MI350X but is otherwise identical - we can thus use the h modifier to identify the higher performance version.

Workstation RDNA 3 (3.1) and 4 (4.1)

2 Tensor Cores and 64 Stream Processors per CU.

AMD Radeon	Tensor C	Stream Proc	CUs	VRAM	SCS name piece
Pro W7900	196	6144	96	48G GDDR6	GA3.1-96-48
AI Pro R9700	128	4096	64	32G GDDR6	GA4.1-64-32

Note that we previously assumed more similarity of consumer RDNA-x with server CDNA-x than actually is the case; the RDNA-x cards now use x.1 (since v3.3 as of Oct 2025) to be able to differentiate them. We will tolerate potential rare cases of old installations calling RDNA-x as generation x for the time being. If AMD executes on the merging with UDNA-5, we will avoid this split in the future.

intel Xe (I)

Xe-HPC (Ponte Vecchio) (3)

One EU corresponds to one Tensor Core and contains 128 Shading Units.

intel DC GPU	Tensor C	Shading U	EUs	VRAM	SCS name part
Max 1100	56	7168	56	48G HBM2e	GI3-56-48h
Max 1550	128	16384	128	128G HBM2e	GI3-128-128h

Workstation cards Arc B (4)

One EU has one tensor core and 16 shading units.

intel GPU	Tensor C	Shading U	EUs	VRAM	SCS name part
Arc Pro B50	128	2048	128	16G GDDR6	GI4-128-16
Arc Pro B60	160	2560	160	24G GDDR6	GI4-160-24
Arc Pro B65	160	2560	160	32G GDDR6	GI4-160-32
Arc Pro B70	256	4096	256	32G GDDR6	GI4-256-32

Consumer cards

Note that we don't recommend using consumer cards. That said, the schema allows to specify them and for example do PCI pass-through of Nvidia RTX4080S (GNl-80-16), RTX4090 (GNl-128-24), RTX5080S (GNb-84-24), RTX5090 (GNb-170-32), or AMD Radeon RX7900XTX (GA3.1-96-24).

Automated tests

The following testcases are implemented:

• scs-0100-syntax-check ensures that any name starting with SCS- adheres to the standard;
• scs-0100-semantics-check ensures that any such name is telling the truth as specified in the standard; specifically: any immediately discoverable property of a flavor (currently, CPU, RAM and disk size) matches the meaning of its name (which is usually a lower bound), such as the CPU generation or hypervisor.

Manual tests

To be determined.