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ARC Cluster GPU Selection Guide

This guide is designed to help you navigate our range of GPU architectures on ARC clusters. Choosing the right hardware depends entirely on the constraints and requirements of your specific workload (e.g., high-accuracy simulation vs. low-power inference).

Part 1: Key Technical Concepts (The Dimensions)

Before making a selection, it helps to understand the technical dimensions that differentiate these GPUs.

Dimension	What it is	Why it Matters	When is it Critical?
Precision (FP64 vs. TF32)	Defines the mathematical accuracy. FP64 (Double Precision) is for rigorous science; TF32 (Tensor Float) and FP16 are for high-speed AI.	Determines if your calculations maintain the required level of accuracy.	HPC Simulations (Need FP64) vs. Deep Learning (Can use TF32/FP16).
Memory Capacity (VRAM)	The GPU’s local memory size (e.g., 80GB, 141GB).	Dictates the maximum size of the model or dataset that can fit onto a single GPU.	Large Language Models (LLMs) or large simulation meshes.
Raw Compute Power (TFLOPS/TF)	The sheer number of operations the GPU can perform per second.	Dictates how fast the core calculation runs.	Training Large Models where speed is the bottleneck.
Power Efficiency (FLOPS/Watt)	The ratio of work done to energy consumed.	Critical for managing cluster operating costs and thermal density.	Inference and Edge Computing where energy consumption matters most.
Interconnect	The speed at which GPUs talk to each other (NVLink, NVSwitch).	Essential when a single job must be split across multiple GPUs (distributed computing).	Any job requiring multiple GPUs (model parallelism, data parallelism).

Terminology Note: Tensor Float (TF) refers to the specialized, optimized data formats (like TF32) used in modern deep learning for faster computation.

Part 2: GPU Comparative Overview (Relative Positioning)

Instead of focusing on raw numbers, this table places the GPUs into performance tiers based on their architectural strengths, making them easier to compare at a glance.

GPU Model	Primary Strength	VRAM (Relative)	Power (TDP)^	Ideal Workload Focus	Tier
B200-180G	Maximum Compute Power	Extremely High	Very High	Training massive foundational models (LLMs).	Apex
H200-141G	Bandwidth & Scale	Very High	High	Large-scale AI training, high-throughput production.	High-End
A100-80G	Balanced Workhorse	High	Medium	Established AI training, complex HPC simulations.	Workhorse
L40S-48G	Versatility & Modernity	High	Medium	Mixed workloads, rendering, medium-scale AI.	Modern/Versatile
A30-24G	Efficiency & Inference	Medium	Low	Production inference, smaller model fine-tuning.	Efficient
V100-16G	Legacy HPC	Medium	Medium	Older codebases, foundational proof-of-concept.	Legacy
T4-16G	Ultra-Efficiency	Small	Very Low	Low-latency inference, small batch size production.	Efficiency Focus

^ TDP: Thermal design power (power consumption).

Part 3: Guidance & Use Case Matrix (Actionable Advice)

Please use this matrix to select the appropriate GPU for your job.

Scenario 1: High-Fidelity Scientific Simulation and Computational Chemistry (HPC)

• Goal: Accurate physics/chemistry simulations (e.g., Molecular Dynamics, CFD).

• Constraint: Requires strict mathematical accuracy (FP64).

• Recommended GPUs (Order by Priority): NVIDIA V100-16G –> NVIDIA A30-24G –> NVIDIA A100-80G.

• Guidance: The A30 is recommended. The A100 is a modern choice for most HPC tasks. If your code is strictly legacy or smaller in size, try the V100 nodes.

• ❌ Avoid: T4/L40S (lower FP64 performance) or B200 (overkill/too power-hungry for many simulation jobs).

Scenario 2: Training Large Language Models (LLMs)

• Goal: Training or fine-tuning models with billions of parameters from scratch.

• Constraint: Requires maximum memory (VRAM) and high interconnect bandwidth for distributed training.

• Recommended GPUs (Order by Priority): NVIDIA B200-180G –> NVIDIA H200-141G –> NVIDIA A100-80G.

• Guidance: Prioritize VRAM first. If you can fit the model on a single A100, that is the most balanced option. For multi-node, memory-intensive jobs, target the H200 or B200.

Scenario 3: General AI Research & Versatility

• Goal: Mid-sized deep learning experiments, mixed workloads (e.g., vision tasks + data processing), or fine-tuning pre-trained models.

• Constraint: Needs a strong balance of VRAM and compute without the extreme cost of the top tiers.

• Recommended GPUs: NVIDIA L40S-48G.

• Guidance: The L40S offers a fantastic modern balance—it’s highly versatile, efficient, and powerful enough for almost any research job that isn’t an extreme LLM scale-up.

Scenario 4: Deployment & Inference

• Goal: Running a trained model in real-time or production (e.g., real-time image recognition).

• Constraint: Requires high performance-per-watt and low latency.

• Recommended GPUs (Order by Priority): NVIDIA T4-16G –> NVIDIA A30-24G –> NVIDIA L40S-48G.

• Guidance: If power consumption and operational cost are a major concern, start with the T4. The A30 offers a modern jump in capability for mid-sized production models.

Scenario 5: Commercial Analysis Codes and GPUs

• Goal: Efficient use of commercial codes.

• Constraint: Many software like COMSOL and Ansys do not typically use GPUs. Some, like SU2, do.

• Guidance: Check manuals for the ability of tools to use GPUs.

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Technical Appendix (Raw Data)

GPUs available on ARC systems

Here is a list of where you can find them on ARC’s clusters, their configurations, and performance characteristics.

There are 514 GPUs over all clusters.

Architecture	Cluster	Partitions	Number	Memory	Configuration	Interconnect	TDP	FP64	TF64	FP32	TF32	INT8	FLOPS/Watt dp	FLOPS/Watt sp	FLOPS/Watt I8
NVIDIA L40S-48G	Falcon	l40s_normal_q, l40s_preemptable_q	80 (20 nodes, 4 GPU/node)	48G	PCIe, 4 GPUs per node	None	350W	–	–	91.6TF	183TF	733TO	–	523G (TF)	2094G
NVIDIA A30-24G	Falcon	a30_normal_q, a30_preemptable_q	128 (32 nodes, 4 GPU/node)	24G	PCIe, 4 GPUs per node	NVLink Bridge 2-way	165W	5.2TF	10.3TF	10.3TF	82TF	330TO	62.4G (TF)	328G (TF)	1320G
NVIDIA V100-16G	Falcon	v100_normal_q, v100_preemptable_q	72 (36 nodes, 2 GPU/node)	16G	PCIe, 2 GPUs per node	None	250W	7TF	–	14TF	–	250TO	28G (FP)	56G (FP)	1000G
NVIDIA T4-16G	Falcon	t4_normal_q, t4_preemptable_q	18 (18 nodes, 1 GPU/node)	16	PCIe, 1 GPU per node	None	70W	–	–	8.1TF	–	130TO	–	115G (FP)	1857G
NVIDIA A100-80G	TinkerCliffs	a100_normal_q, a100_preemptable_q	112 (14 nodes, 8 GPU/node)	80G	SXM, 8 GPUs per node	NVSwitch HDR IB	400W	9.7TF	19.5TF	19.5TF	156TF	624TO	48.8G (TF)	390G (TF)	1560G
NVIDIA H200-141G	TinkerCliffs	h200_normal_q, h200_preemptable_q	48 (6 nodes, 8 GPU/node)	141G	SXM, 8 GPUs per node	NVSwitch, NDR IB	700W	34TF	67TF	67TF	989TF	3958TO	95.7G (TF)	1413G (TF)	5654G
NVIDIA B200-180G	Owl	b200_normal_q, b200_preemptable_q	32 (4 nodes, 8 GPU/node)	180G	SXM, 8 GPUs per node	NVSwitch	1000W	37TF	37TF	75TF	1100TF	4500TO	37G (TF)	75G (TF)	4500G
NVIDIA A100-80G	CUI	a100_normal_q, a100_preemptable_q	16 (2 nodes, 8 GPU/node)	80G	SXM, 8 GPUs per node	NVSwitch, HDR IB	400W	9.7TF	19.5TF	19.5TF	156TF	624TO	48.8G (TF)	390G (TF)	1560G
NVIDIA A100-80G	Biomed	a100_normal_q, a100_preemptable_q	8 (1 node, 8 GPU/node)	80G	SXM, 8 GPUs per node	NVSwitch, HDR IB	400W	9.7TF	19.5TF	19.5TF	156TF	624TO	48.8G (TF)	390G (TF)	1560G

Technical Legend

• Memory: Memory available per node.

• TDP: Thermal design power (power consumption).

• Precision:

  • FP64: Double precision (high accuracy for scientific computing).

  • TF64: Tensor float 64.

  • FP32: Single precision (standard for many deep learning tasks).

  • TF32: Tensor float 32 (hybrid format for speed in deep learning).

  • INT8: Eight-bit integer representations (used in efficient inference).

• Performance Units:

  • TF: Teraflops (one trillion operations, 10^{12}).

  • TO: Tera-Operations Per Second (used for INT8).

• Efficiency (FLOPS/Watt):

  • dp / sp / I8: Double precision / Single precision / Eight-bit integer FLOPS/Watt.

  • Note: “G” means giga (10^9).

  • The numbers in parentheses (FP) or (TF) indicate the type of operation and cannot be directly compared.