Hardware Selection Advice - Revision history

Jaap.vandervelde: Removing statement on misinformation, added links, made testing advice specific to non-ECC memory

2026-03-23T04:33:38Z

Removing statement on misinformation, added links, made testing advice specific to non-ECC memory

← Older revision		Revision as of 14:33, 23 March 2026
Line 43:		Line 43:

	=== RAM Reliability (ECC vs non-ECC) ===		=== RAM Reliability (ECC vs non-ECC) ===
	ECC (Error-Correcting Code) RAM detects and corrects memory errors, improving reliability, while non-ECC cannot. ~~There~~ is ~~some~~ ~~misinformation~~ about ~~random~~ ~~cosmic~~ ~~ray~~ ~~bit~~ ~~flips~~. ~~Large~~ field studies show errors are usually caused by physical faults in specific DIMMs (Dual In-line Memory Modules, the removable RAM sticks), not uniform random events. Most DIMMs experience no errors, while a small number produce the vast majority of faults. Modern DDR5 memory also includes on-die correction that silently fixes some errors before they leave the chip.		ECC (Error-Correcting Code) RAM detects and corrects memory errors, improving reliability, while non-ECC cannot. Use of non-ECC memory may raise worries about global error rates affecting simulation results. However, large [https://www.cs.toronto.edu/~bianca/papers/sigmetrics09.pdf field studies] show errors are usually caused by physical faults in specific DIMMs (Dual In-line Memory Modules, the removable RAM sticks), not uniform random events. Most DIMMs experience no errors, while a small number produce the vast majority of faults. Modern DDR5 memory also includes on-die correction that silently fixes some errors before they leave the chip.

	A failing DIMM on a non-ECC system is more likely to cause crashes or obvious corruption than a silent incorrect result. In numerical solvers, bit flips often trigger instability or failure rather than plausible but wrong outputs. For a single TUFLOW workstation, ECC is generally not required solely to protect result quality, though it may be beneficial for servers, critical workloads, or environments operating many machines.		A failing DIMM on a non-ECC system is more likely to cause crashes or obvious corruption than a silent incorrect result. In numerical solvers, bit flips often trigger instability or failure rather than plausible but wrong outputs. For a single TUFLOW workstation, ECC is generally not required solely to protect result quality, though it may be beneficial for servers, critical workloads, or environments operating many machines.

	If additional confidence is desired, run a memory test (for example Memtest86+) for multiple passes after installation. Consistent errors indicate defective hardware that should be replaced.		If additional confidence is desired, run a memory test (for example [https://www.memtest.org/ Memtest86+]) for multiple passes after installation, for non-ECC memory. Consistent errors indicate defective hardware that should be replaced.

	===CUDA Cores, GPU Clock speed, and FLOPs ===		===CUDA Cores, GPU Clock speed, and FLOPs ===

Abrar.Alttahir: /* RAM Reliability (ECC vs non-ECC) */

2026-03-23T04:12:05Z

RAM Reliability (ECC vs non-ECC)

← Older revision		Revision as of 14:12, 23 March 2026
Line 43:		Line 43:

	=== RAM Reliability (ECC vs non-ECC) ===		=== RAM Reliability (ECC vs non-ECC) ===
	ECC (Error-Correcting Code) RAM detects and corrects memory errors, improving reliability, while non-ECC cannot. There is ~~significant~~ misinformation about random ~~“cosmic~~ ~~ray”~~ bit flips. Large field studies show errors are usually caused by physical faults in specific DIMMs (Dual In-line Memory Modules, the removable RAM sticks), not uniform random events. Most DIMMs experience no errors, while a small number produce the vast majority of faults. Modern DDR5 memory also includes on-die correction that silently fixes some errors before they leave the chip.		ECC (Error-Correcting Code) RAM detects and corrects memory errors, improving reliability, while non-ECC cannot. There is some misinformation about random cosmic ray bit flips. Large field studies show errors are usually caused by physical faults in specific DIMMs (Dual In-line Memory Modules, the removable RAM sticks), not uniform random events. Most DIMMs experience no errors, while a small number produce the vast majority of faults. Modern DDR5 memory also includes on-die correction that silently fixes some errors before they leave the chip.

	A failing DIMM on a non-ECC system is more likely to cause crashes or obvious corruption than a silent incorrect result. In numerical solvers, bit flips often trigger instability or failure rather than plausible but wrong outputs. For a single TUFLOW workstation, ECC is generally not required solely to protect result quality, though it may be beneficial for servers, critical workloads, or environments operating many machines.		A failing DIMM on a non-ECC system is more likely to cause crashes or obvious corruption than a silent incorrect result. In numerical solvers, bit flips often trigger instability or failure rather than plausible but wrong outputs. For a single TUFLOW workstation, ECC is generally not required solely to protect result quality, though it may be beneficial for servers, critical workloads, or environments operating many machines.

Abrar.Alttahir: /* GPU Advice */

2026-03-23T04:00:55Z

GPU Advice

← Older revision		Revision as of 14:00, 23 March 2026
Line 24:		Line 24:
	*TUFLOW FV - Run on GPU Hardware: A single model run uses the GPU(s) for computation. In general terms: The maximum model size is dependent on the available GPU and CPU RAM and the runtime is driven by the CUDA core speed, the number of CUDA cores available and the GPU architecture. GPU performance is complex and is not easily inferred from GPU clock speed and number of cores, it is also very dependent on the ‘generation’ or architecture of GPU. As TUFLOW FV requires some data exchange between GPU and CPU, the motherboard bus speeds and CPU speeds also play a role but typically a much lesser role compared to the GPU CUDA compute.<br>		*TUFLOW FV - Run on GPU Hardware: A single model run uses the GPU(s) for computation. In general terms: The maximum model size is dependent on the available GPU and CPU RAM and the runtime is driven by the CUDA core speed, the number of CUDA cores available and the GPU architecture. GPU performance is complex and is not easily inferred from GPU clock speed and number of cores, it is also very dependent on the ‘generation’ or architecture of GPU. As TUFLOW FV requires some data exchange between GPU and CPU, the motherboard bus speeds and CPU speeds also play a role but typically a much lesser role compared to the GPU CUDA compute.<br>

	The <u>[[Hardware_Benchmarking_-_Results#CPU_Results \| Hardware Benchmarking]]</u> page shows recently run combinations of GPU, CPU and RAM. These can be compared with the system intended for purchase. The recommendation is to seek advise from an appropriate computer hardware vendor who can advise on the compatibility and optimisation of the setup.~~<br>~~		The <u>[[Hardware_Benchmarking_-_Results#CPU_Results \| Hardware Benchmarking]]</u> page shows recently run combinations of GPU, CPU and RAM. These can be compared with the system intended for purchase. The recommendation is to seek advise from an appropriate computer hardware vendor who can advise on the compatibility and optimisation of the setup.
	<br>

	=GPU Advice=		=GPU Advice=
Line 33:		Line 32:
	*TUFLOW HPC on GPU Hardware can be run in either single or double precision. However, for the vast majority of flood applications single precision is sufficient. We typically run our models on single precision. If you are unsure we recommend running with both the single and double precision solvers and comparing your results.		*TUFLOW HPC on GPU Hardware can be run in either single or double precision. However, for the vast majority of flood applications single precision is sufficient. We typically run our models on single precision. If you are unsure we recommend running with both the single and double precision solvers and comparing your results.
	The precision solver you require will determine the type of GPU card that is best suited for your compute. For any given generation/architecture of cards, the “gaming” cards such as the GTX GeForce and RTX provide excellent single precision performance – typically comparable to that of the “scientific” cards such as the Tesla series. If double precision is required then the scientific cards are substantially faster, but these are also significantly more expensive. The Quadro series cards sit in between for both double precision performance and cost. When checking the specifications of the card it should provide you with a breakdown of the single and double precision throughput in flops. Single precision compute is typically sufficient for TUFLOW HPC modelling.		The precision solver you require will determine the type of GPU card that is best suited for your compute. For any given generation/architecture of cards, the “gaming” cards such as the GTX GeForce and RTX provide excellent single precision performance – typically comparable to that of the “scientific” cards such as the Tesla series. If double precision is required then the scientific cards are substantially faster, but these are also significantly more expensive. The Quadro series cards sit in between for both double precision performance and cost. When checking the specifications of the card it should provide you with a breakdown of the single and double precision throughput in flops. Single precision compute is typically sufficient for TUFLOW HPC modelling.

	For the higher end GPU cards, users may wish to consider server-based computers rather than workstations, and also weigh the cost of an extra TUFLOW licence against the cost of the high end hardware.		For the higher end GPU cards, users may wish to consider server-based computers rather than workstations, and also weigh the cost of an extra TUFLOW licence against the cost of the high end hardware.

Line 41:		Line 41:
	===CPU RAM===		===CPU RAM===
	TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that anything more than 256GB of CPU RAM will exceed the limitations of consumer chipsets that are available in 2025 and requires more expensive workstation hardware - additionally, users should consult a hardware expert to check limitations of specific hardware.		TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that anything more than 256GB of CPU RAM will exceed the limitations of consumer chipsets that are available in 2025 and requires more expensive workstation hardware - additionally, users should consult a hardware expert to check limitations of specific hardware.

			=== RAM Reliability (ECC vs non-ECC) ===
			ECC (Error-Correcting Code) RAM detects and corrects memory errors, improving reliability, while non-ECC cannot. There is significant misinformation about random “cosmic ray” bit flips. Large field studies show errors are usually caused by physical faults in specific DIMMs (Dual In-line Memory Modules, the removable RAM sticks), not uniform random events. Most DIMMs experience no errors, while a small number produce the vast majority of faults. Modern DDR5 memory also includes on-die correction that silently fixes some errors before they leave the chip.

			A failing DIMM on a non-ECC system is more likely to cause crashes or obvious corruption than a silent incorrect result. In numerical solvers, bit flips often trigger instability or failure rather than plausible but wrong outputs. For a single TUFLOW workstation, ECC is generally not required solely to protect result quality, though it may be beneficial for servers, critical workloads, or environments operating many machines.

			If additional confidence is desired, run a memory test (for example Memtest86+) for multiple passes after installation. Consistent errors indicate defective hardware that should be replaced.

	===CUDA Cores, GPU Clock speed, and FLOPs ===		===CUDA Cores, GPU Clock speed, and FLOPs ===

Pavlina Monhartova at 03:57, 5 September 2025

2025-09-05T03:57:57Z

← Older revision		Revision as of 13:57, 5 September 2025
Line 33:		Line 33:
	*TUFLOW HPC on GPU Hardware can be run in either single or double precision. However, for the vast majority of flood applications single precision is sufficient. We typically run our models on single precision. If you are unsure we recommend running with both the single and double precision solvers and comparing your results.		*TUFLOW HPC on GPU Hardware can be run in either single or double precision. However, for the vast majority of flood applications single precision is sufficient. We typically run our models on single precision. If you are unsure we recommend running with both the single and double precision solvers and comparing your results.
	The precision solver you require will determine the type of GPU card that is best suited for your compute. For any given generation/architecture of cards, the “gaming” cards such as the GTX GeForce and RTX provide excellent single precision performance – typically comparable to that of the “scientific” cards such as the Tesla series. If double precision is required then the scientific cards are substantially faster, but these are also significantly more expensive. The Quadro series cards sit in between for both double precision performance and cost. When checking the specifications of the card it should provide you with a breakdown of the single and double precision throughput in flops. Single precision compute is typically sufficient for TUFLOW HPC modelling.		The precision solver you require will determine the type of GPU card that is best suited for your compute. For any given generation/architecture of cards, the “gaming” cards such as the GTX GeForce and RTX provide excellent single precision performance – typically comparable to that of the “scientific” cards such as the Tesla series. If double precision is required then the scientific cards are substantially faster, but these are also significantly more expensive. The Quadro series cards sit in between for both double precision performance and cost. When checking the specifications of the card it should provide you with a breakdown of the single and double precision throughput in flops. Single precision compute is typically sufficient for TUFLOW HPC modelling.
	For the higher end ~~(costly)~~ GPU cards, users may wish to consider server-based computers rather than workstations, and also weigh the cost of an extra TUFLOW licence against the cost of the high end hardware.		For the higher end GPU cards, users may wish to consider server-based computers rather than workstations, and also weigh the cost of an extra TUFLOW licence against the cost of the high end hardware.

	===GPU RAM===		===GPU RAM===

Pavlina Monhartova: /* The TUFLOW Software Suite */

2025-09-03T07:29:32Z

The TUFLOW Software Suite

← Older revision		Revision as of 17:29, 3 September 2025
Line 24:		Line 24:
	*TUFLOW FV - Run on GPU Hardware: A single model run uses the GPU(s) for computation. In general terms: The maximum model size is dependent on the available GPU and CPU RAM and the runtime is driven by the CUDA core speed, the number of CUDA cores available and the GPU architecture. GPU performance is complex and is not easily inferred from GPU clock speed and number of cores, it is also very dependent on the ‘generation’ or architecture of GPU. As TUFLOW FV requires some data exchange between GPU and CPU, the motherboard bus speeds and CPU speeds also play a role but typically a much lesser role compared to the GPU CUDA compute.<br>		*TUFLOW FV - Run on GPU Hardware: A single model run uses the GPU(s) for computation. In general terms: The maximum model size is dependent on the available GPU and CPU RAM and the runtime is driven by the CUDA core speed, the number of CUDA cores available and the GPU architecture. GPU performance is complex and is not easily inferred from GPU clock speed and number of cores, it is also very dependent on the ‘generation’ or architecture of GPU. As TUFLOW FV requires some data exchange between GPU and CPU, the motherboard bus speeds and CPU speeds also play a role but typically a much lesser role compared to the GPU CUDA compute.<br>

	The <u>[[Hardware_Benchmarking_-_Results#CPU_Results \| Hardware Benchmarking]]</u> page shows recently run combinations of GPU, CPU and RAM. These can be compared with the system ~~planned~~ for purchase. The recommendation is to seek advise from an appropriate computer hardware vendor who can advise on the compatibility and optimisation of the setup.<br>		The <u>[[Hardware_Benchmarking_-_Results#CPU_Results \| Hardware Benchmarking]]</u> page shows recently run combinations of GPU, CPU and RAM. These can be compared with the system intended for purchase. The recommendation is to seek advise from an appropriate computer hardware vendor who can advise on the compatibility and optimisation of the setup.<br>
	<br>		<br>

Teegan.Burke: /* CPU RAM */

2025-09-03T01:40:33Z

CPU RAM

← Older revision		Revision as of 11:40, 3 September 2025
Line 40:		Line 40:

	===CPU RAM===		===CPU RAM===
	TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that anything more than 256GB of CPU RAM will exceed the limitations of consumer chipsets that are available in 2025 and requires more expensive workstation hardware - additionally, users should ~~engage~~ ~~their~~ IT ~~department~~ to check limitations of specific hardware.		TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that anything more than 256GB of CPU RAM will exceed the limitations of consumer chipsets that are available in 2025 and requires more expensive workstation hardware - additionally, users should consult a hardware expert to check limitations of specific hardware.

	===CUDA Cores, GPU Clock speed, and FLOPs ===		===CUDA Cores, GPU Clock speed, and FLOPs ===

Teegan.Burke: /* CPU RAM */

2025-09-03T01:39:16Z

CPU RAM

← Older revision		Revision as of 11:39, 3 September 2025
Line 40:		Line 40:

	===CPU RAM===		===CPU RAM===
	TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that anything more than 256GB of CPU RAM requires more expensive workstation hardware - users should engage their IT department to check limitations of specific hardware.		TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that anything more than 256GB of CPU RAM will exceed the limitations of consumer chipsets that are available in 2025 and requires more expensive workstation hardware - additionally, users should engage their IT department to check limitations of specific hardware.

	===CUDA Cores, GPU Clock speed, and FLOPs ===		===CUDA Cores, GPU Clock speed, and FLOPs ===

Teegan.Burke: /* CPU RAM */

2025-09-03T01:28:29Z

CPU RAM

← Older revision		Revision as of 11:28, 3 September 2025
Line 40:		Line 40:

	===CPU RAM===		===CPU RAM===
	TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that ~~for~~ more than 256GB of CPU RAM, a workstation ~~level~~ ~~motherboard~~ ~~may~~ be ~~required~~ at a ~~higher~~ ~~price~~ ~~class~~.		TUFLOW HPC on GPU hardware still uses the CPU to compute and store data (in CPU RAM) during model initialisation and for all 1D calculations. While we are working on improving our CPU RAM usage, currently we tend to find that CPU RAM is often the limiter to the size of the model domain you can run, particularly if using running over multiple GPU cards. During initialisation and simulation a model will typically require 4-6 times the amount of CPU RAM relative to GPU RAM. As an example, a model that utilises 11GB of GPU RAM (typical memory for high-end gaming card, and corresponds to about a 50 million cell model) the CPU RAM required during initialisation will typically be in range 44GB to 66GB. A model that fully utilises two 11 GB GPUs (i.e. a 100 million cell model) may require as much as 128GB of CPU RAM during initialisation. Note that anything more than 256GB of CPU RAM requires more expensive workstation hardware - users should engage their IT department to check limitations of specific hardware.

	===CUDA Cores, GPU Clock speed, and FLOPs ===		===CUDA Cores, GPU Clock speed, and FLOPs ===

Teegan.Burke: /* GPU Advice */

2025-09-03T01:17:26Z

GPU Advice

← Older revision		Revision as of 11:17, 3 September 2025
Line 33:		Line 33:
	*TUFLOW HPC on GPU Hardware can be run in either single or double precision. However, for the vast majority of flood applications single precision is sufficient. We typically run our models on single precision. If you are unsure we recommend running with both the single and double precision solvers and comparing your results.		*TUFLOW HPC on GPU Hardware can be run in either single or double precision. However, for the vast majority of flood applications single precision is sufficient. We typically run our models on single precision. If you are unsure we recommend running with both the single and double precision solvers and comparing your results.
	The precision solver you require will determine the type of GPU card that is best suited for your compute. For any given generation/architecture of cards, the “gaming” cards such as the GTX GeForce and RTX provide excellent single precision performance – typically comparable to that of the “scientific” cards such as the Tesla series. If double precision is required then the scientific cards are substantially faster, but these are also significantly more expensive. The Quadro series cards sit in between for both double precision performance and cost. When checking the specifications of the card it should provide you with a breakdown of the single and double precision throughput in flops. Single precision compute is typically sufficient for TUFLOW HPC modelling.		The precision solver you require will determine the type of GPU card that is best suited for your compute. For any given generation/architecture of cards, the “gaming” cards such as the GTX GeForce and RTX provide excellent single precision performance – typically comparable to that of the “scientific” cards such as the Tesla series. If double precision is required then the scientific cards are substantially faster, but these are also significantly more expensive. The Quadro series cards sit in between for both double precision performance and cost. When checking the specifications of the card it should provide you with a breakdown of the single and double precision throughput in flops. Single precision compute is typically sufficient for TUFLOW HPC modelling.
			For the higher end (costly) GPU cards, users may wish to consider server-based computers rather than workstations, and also weigh the cost of an extra TUFLOW licence against the cost of the high end hardware.

	===GPU RAM===		===GPU RAM===

Teegan.Burke: /* Introduction */

2025-09-03T01:10:22Z

Introduction

← Older revision		Revision as of 11:10, 3 September 2025
Line 7:		Line 7:
	In the sections below you will find more detailed advice on GPU and CPU but generally:<br>		In the sections below you will find more detailed advice on GPU and CPU but generally:<br>

	* The amount of RAM in the computer will be the limiter for the size of model you can run. This applies to CPU RAM (TUFLOW Classic, TUFLOW FV and TUFLOW HPC with Hardware == CPU) and also GPU RAM (TUFLOW HPC and TUFLOW FV with Hardware == GPU).If available RAM becomes a limitation, users should also investigate improvements to their model configuration to reduce RAM requirements (see <u>[[TUFLOW Simulation Speed \| TUFLOW Simulation Speed]]</u>).		* The amount of RAM in the computer will be the limiter for the size of model you can run. This applies to CPU RAM (TUFLOW Classic, TUFLOW FV and TUFLOW HPC with Hardware == CPU) and also GPU RAM (TUFLOW HPC and TUFLOW FV with Hardware == GPU). If available RAM becomes a limitation, users should also investigate improvements to their model configuration to reduce RAM requirements (see <u>[[TUFLOW Simulation Speed \| TUFLOW Simulation Speed]]</u>).

	* The processing speed of the CPU, the architecture, cache size, speed and number of processors play a role.		* The processing speed of the CPU, the architecture, cache size, speed and number of processors play a role.