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General Cloud Hosting Discussion / Sleep in bliss with our luxurious bedding sets
« Last post by JefferyMal on February 01, 2026, 10:08:30 AM »23
General Cloud Hosting Discussion / Re: Best VPS for personal VPN?
« Last post by Anna on January 31, 2026, 11:35:26 AM »I chose a KVM VPS from hostpro.com mainly for better isolation compared to container-based solutions. Having full virtualization allows running custom kernels and low-level configurations without limitations. NVMe disks handle high IOPS workloads well, even during peak usage. Backups are created daily and retained long enough to safely test deployments and upgrades. Support availability 24/7 is helpful when managing production servers.
24
General Cloud Hosting Discussion / Re: GPU dedicated servers, where can I order them?
« Last post by myresellerhome on January 30, 2026, 11:37:37 PM »Try out MyResellerHome web hosting service. Our dedicated hosting plan starts at $99/mo. It includes services like cPanel/Plesk, free SSL, free WHMCS, 100% uptime guarantee, and more. For more information, please visit our website.
25
General Cloud Hosting Discussion / just.hosting vs planethoster.com
« Last post by Egrikolla on January 30, 2026, 03:01:19 PM »Comparing deals from just.hosting and planethoster.com, which plan suits me better as I plan to host a forum?
What do you think?
What do you think?
26
General Cloud Hosting Discussion / Re: Seeking Reliable VPS Hosting Provider for Business Website
« Last post by Egrikolla on January 30, 2026, 02:37:27 PM »Reliable VPS accounts are available from inet.ws.
I’m very happy with their turnaround time and support. Other things that I like about them are these:
Amazing up time
Cheap price, yet quality hosting
Fast server speed
One click script installing
Lots of other features.
I’m very happy with their turnaround time and support. Other things that I like about them are these:
Amazing up time
Cheap price, yet quality hosting
Fast server speed
One click script installing
Lots of other features.
27
Cloud Hosting Offers / 13 Locations: North America (US & Canada), UK and Europe. The best price for SSD
« Last post by KimChen on January 30, 2026, 11:36:24 AM »Intel Xeon E5-2695v4
Ashburn, Atlanta, Chicago, Dallas, Los Angeles, Miami, New York, Phoenix, Seattle, Vancouver, Toronto, London and Frankfurt
Looking Glass for our VPS
Discover the true potential of powerful VPS at an incredibly low price!
_____
ORDER NOW
Key Features:
* Full root access
* Intel Xeon E5-2695v4
* Weekly Backup - Free
* KVM virtualization
* Choice of Linux or FreeBSD operating system
* 99.95% SLA
* Multiple hosting regions in North America (US & Canada), UK and Europe
Linux VPS vCPU-1/ RAM-2/ SSD-30GB- $4/mon
* 1 vCPU Intel Xeon E5-2695v4
* 2GB RAM (ECC)
* 30GB SSD
* 10TB Bandwidth (Each additional 2TB = $1
* Weekly Backup Free
* Snapshots for Free
* Instant Deployment
Linux VPS vCPU-2/ RAM-4/ SSD-60GB - $8/mon
* 2 vCPU Intel Xeon E5-2695v4
* 4GB RAM (ECC)
* 60GB SSD
* 20TB Bandwidth (Each additional 2TB = $1
* Weekly Backup Free
* Snapshots for Free
* Instant Deployment
All PRICES HERE
* Powerful, Affordable, Reliable, Cheap Virtual Private Servers (VPS)
* 13 Locations: North America (US & Canada), UK and Europe
* Weekly Backup - FREE
* Linux/FreeBSD
Looking Glass: https://inet.ws/lg
List of Operating Systems available:
* CloudLinux 7, 8 and 9
* CloudLinux + cPanel
* CloudLinux + Plesk
* CloudLinux + DirectAdmin
* AlmaLinux 8.7
* AlmaLinux 9.1
* CentOS 6.10
* CentOS 7.9
* Debian 8.7
* Debian 9.4
* Debian 10
* Debian 11
* Rocky Linux 8.6
* Rocky Linux 9.1
* Ubuntu 18.04
* Ubuntu 20.04
* Ubuntu 22.04
And over 100 other applications
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Available VPS Locations
INET.WS - VPS Hosting in the USA, CANADA, UK, Germany
Ashburn, Atlanta, Chicago, Dallas, Los Angeles, Miami, New York, Phoenix, Seattle, Vancouver, Toronto, London and Frankfurt
Looking Glass for our VPS
Discover the true potential of powerful VPS at an incredibly low price!
_____
ORDER NOW
Key Features:
* Full root access
* Intel Xeon E5-2695v4
* Weekly Backup - Free
* KVM virtualization
* Choice of Linux or FreeBSD operating system
* 99.95% SLA
* Multiple hosting regions in North America (US & Canada), UK and Europe
Linux VPS vCPU-1/ RAM-2/ SSD-30GB- $4/mon
* 1 vCPU Intel Xeon E5-2695v4
* 2GB RAM (ECC)
* 30GB SSD
* 10TB Bandwidth (Each additional 2TB = $1
* Weekly Backup Free
* Snapshots for Free
* Instant Deployment
Linux VPS vCPU-2/ RAM-4/ SSD-60GB - $8/mon
* 2 vCPU Intel Xeon E5-2695v4
* 4GB RAM (ECC)
* 60GB SSD
* 20TB Bandwidth (Each additional 2TB = $1
* Weekly Backup Free
* Snapshots for Free
* Instant Deployment
All PRICES HERE
* Powerful, Affordable, Reliable, Cheap Virtual Private Servers (VPS)
* 13 Locations: North America (US & Canada), UK and Europe
* Weekly Backup - FREE
* Linux/FreeBSD
Looking Glass: https://inet.ws/lg
List of Operating Systems available:
* CloudLinux 7, 8 and 9
* CloudLinux + cPanel
* CloudLinux + Plesk
* CloudLinux + DirectAdmin
* AlmaLinux 8.7
* AlmaLinux 9.1
* CentOS 6.10
* CentOS 7.9
* Debian 8.7
* Debian 9.4
* Debian 10
* Debian 11
* Rocky Linux 8.6
* Rocky Linux 9.1
* Ubuntu 18.04
* Ubuntu 20.04
* Ubuntu 22.04
And over 100 other applications
ORDER NOW
Available VPS Locations
INET.WS - VPS Hosting in the USA, CANADA, UK, Germany
28
Cloud Hosting Offers / GPU Resource Scheduling Practices for Maximizing Utilization Across Teams
« Last post by manoharparakh on January 30, 2026, 05:31:06 AM »GPU capacity has quietly become one of the most constrained and expensive resources inside enterprise IT environments. As AI workloads expand across data science, engineering, analytics, and product teams, the challenge is no longer access to GPUs alone. It is how effectively those GPUs are shared, scheduled, and utilized.
Why GPU scheduling is now a leadership concern
In many enterprises, GPUs were initially deployed for a single team or a specific project. Over time, usage expanded. Data scientists trained models. Engineers ran inference pipelines. Research teams tested experiments. Soon, demand exceeded supply.
Without structured private GPU scheduling strategies, teams often fall back on informal booking, static allocation, or manual approvals. This leads to idle GPUs during off-hours and bottlenecks during peak demand. The result is poor GPU utilization optimization, even though hardware investment continues to grow.
Understanding GPU resource scheduling in practice
GPU scheduling determines how workloads are assigned to available GPU resources. In multi-team setups, scheduling must balance fairness, priority, and utilization without creating operational complexity.
At a basic level, scheduling answers three questions:
• Who can access GPUs
• When access is granted
• How much capacity is allocated
In mature environments, scheduling integrates with orchestration platforms, access policies, and usage monitoring. This enables controlled multi-team GPU sharing without sacrificing accountability.
The cost of unmanaged GPU usage
When GPUs are statically assigned to teams, utilization rates often drop below 50 percent. GPUs sit idle while other teams wait. From an accounting perspective, this inflates the effective cost per training run or inference job.
Poor scheduling also introduces hidden costs:
• Engineers waiting for compute
• Delayed model iterations
• Manual intervention by infrastructure teams
• Tension between teams competing for resources
Effective AI resource management treats GPUs as shared enterprise assets rather than departmental property.
Measuring success through utilization metrics
Effective GPU utilization optimization depends on measurement. Without clear metrics, scheduling improvements remain theoretical.
Key indicators include:
• Average GPU utilization over time
• Job waits times by team
• Percentage of idle capacity
• Frequency of preemption or rescheduling
These metrics help leadership assess whether investments in GPUs and scheduling platforms are delivering operational value.
Overly rigid quotas can discourage experimentation. Completely open access can lead to resource hoarding. Lack of visibility creates mistrust between teams.
The most effective private GPU scheduling strategies strike a balance. They provide guardrails without micromanagement and flexibility without chaos.
For enterprises implementing structured AI resource management in India, ESDS Software Solution Ltd. GPU as a service provides managed GPU environments hosted within Indian data centers. These services support controlled scheduling, access governance, and usage visibility, helping organizations improve GPU utilization optimization while maintaining compliance and operational clarity.
For more information, contact Team ESDS through:
Visit us: https://www.esds.co.in/gpu-as-a-service
Why GPU scheduling is now a leadership concern
In many enterprises, GPUs were initially deployed for a single team or a specific project. Over time, usage expanded. Data scientists trained models. Engineers ran inference pipelines. Research teams tested experiments. Soon, demand exceeded supply.
Without structured private GPU scheduling strategies, teams often fall back on informal booking, static allocation, or manual approvals. This leads to idle GPUs during off-hours and bottlenecks during peak demand. The result is poor GPU utilization optimization, even though hardware investment continues to grow.
Understanding GPU resource scheduling in practice
GPU scheduling determines how workloads are assigned to available GPU resources. In multi-team setups, scheduling must balance fairness, priority, and utilization without creating operational complexity.
At a basic level, scheduling answers three questions:
• Who can access GPUs
• When access is granted
• How much capacity is allocated
In mature environments, scheduling integrates with orchestration platforms, access policies, and usage monitoring. This enables controlled multi-team GPU sharing without sacrificing accountability.
The cost of unmanaged GPU usage
When GPUs are statically assigned to teams, utilization rates often drop below 50 percent. GPUs sit idle while other teams wait. From an accounting perspective, this inflates the effective cost per training run or inference job.
Poor scheduling also introduces hidden costs:
• Engineers waiting for compute
• Delayed model iterations
• Manual intervention by infrastructure teams
• Tension between teams competing for resources
Effective AI resource management treats GPUs as shared enterprise assets rather than departmental property.
Measuring success through utilization metrics
Effective GPU utilization optimization depends on measurement. Without clear metrics, scheduling improvements remain theoretical.
Key indicators include:
• Average GPU utilization over time
• Job waits times by team
• Percentage of idle capacity
• Frequency of preemption or rescheduling
These metrics help leadership assess whether investments in GPUs and scheduling platforms are delivering operational value.
Overly rigid quotas can discourage experimentation. Completely open access can lead to resource hoarding. Lack of visibility creates mistrust between teams.
The most effective private GPU scheduling strategies strike a balance. They provide guardrails without micromanagement and flexibility without chaos.
For enterprises implementing structured AI resource management in India, ESDS Software Solution Ltd. GPU as a service provides managed GPU environments hosted within Indian data centers. These services support controlled scheduling, access governance, and usage visibility, helping organizations improve GPU utilization optimization while maintaining compliance and operational clarity.
For more information, contact Team ESDS through:
Visit us: https://www.esds.co.in/gpu-as-a-service
29
Miscellaneous / GPU Resource Scheduling Practices for Maximizing Utilization Across Teams
« Last post by manoharparakh on January 30, 2026, 05:10:25 AM »GPU capacity has quietly become one of the most constrained and expensive resources inside enterprise IT environments. As AI workloads expand across data science, engineering, analytics, and product teams, the challenge is no longer access to GPUs alone. It is how effectively those GPUs are shared, scheduled, and utilized.
Why GPU scheduling is now a leadership concern
In many enterprises, GPUs were initially deployed for a single team or a specific project. Over time, usage expanded. Data scientists trained models. Engineers ran inference pipelines. Research teams tested experiments. Soon, demand exceeded supply.
Without structured private GPU scheduling strategies, teams often fall back on informal booking, static allocation, or manual approvals. This leads to idle GPUs during off-hours and bottlenecks during peak demand. The result is poor GPU utilization optimization, even though hardware investment continues to grow.
Understanding GPU resource scheduling in practice
GPU scheduling determines how workloads are assigned to available GPU resources. In multi-team setups, scheduling must balance fairness, priority, and utilization without creating operational complexity.
At a basic level, scheduling answers three questions:
• Who can access GPUs
• When access is granted
• How much capacity is allocated
In mature environments, scheduling integrates with orchestration platforms, access policies, and usage monitoring. This enables controlled multi-team GPU sharing without sacrificing accountability.
The cost of unmanaged GPU usage
When GPUs are statically assigned to teams, utilization rates often drop below 50 percent. GPUs sit idle while other teams wait. From an accounting perspective, this inflates the effective cost per training run or inference job.
Poor scheduling also introduces hidden costs:
• Engineers waiting for compute
• Delayed model iterations
• Manual intervention by infrastructure teams
• Tension between teams competing for resources
Effective AI resource management treats GPUs as shared enterprise assets rather than departmental property.
Measuring success through utilization metrics
Effective GPU utilization optimization depends on measurement. Without clear metrics, scheduling improvements remain theoretical.
Key indicators include:
• Average GPU utilization over time
• Job waits times by team
• Percentage of idle capacity
• Frequency of preemption or rescheduling
These metrics help leadership assess whether investments in GPUs and scheduling platforms are delivering operational value.
Overly rigid quotas can discourage experimentation. Completely open access can lead to resource hoarding. Lack of visibility creates mistrust between teams.
The most effective private GPU scheduling strategies strike a balance. They provide guardrails without micromanagement and flexibility without chaos.
For enterprises implementing structured AI resource management in India, ESDS Software Solution Ltd. GPU as a service provides managed GPU environments hosted within Indian data centers. These services support controlled scheduling, access governance, and usage visibility, helping organizations improve GPU utilization optimization while maintaining compliance and operational clarity.
For more information, contact Team ESDS through:
Visit us: https://www.esds.co.in/gpu-as-a-service
Why GPU scheduling is now a leadership concern
In many enterprises, GPUs were initially deployed for a single team or a specific project. Over time, usage expanded. Data scientists trained models. Engineers ran inference pipelines. Research teams tested experiments. Soon, demand exceeded supply.
Without structured private GPU scheduling strategies, teams often fall back on informal booking, static allocation, or manual approvals. This leads to idle GPUs during off-hours and bottlenecks during peak demand. The result is poor GPU utilization optimization, even though hardware investment continues to grow.
Understanding GPU resource scheduling in practice
GPU scheduling determines how workloads are assigned to available GPU resources. In multi-team setups, scheduling must balance fairness, priority, and utilization without creating operational complexity.
At a basic level, scheduling answers three questions:
• Who can access GPUs
• When access is granted
• How much capacity is allocated
In mature environments, scheduling integrates with orchestration platforms, access policies, and usage monitoring. This enables controlled multi-team GPU sharing without sacrificing accountability.
The cost of unmanaged GPU usage
When GPUs are statically assigned to teams, utilization rates often drop below 50 percent. GPUs sit idle while other teams wait. From an accounting perspective, this inflates the effective cost per training run or inference job.
Poor scheduling also introduces hidden costs:
• Engineers waiting for compute
• Delayed model iterations
• Manual intervention by infrastructure teams
• Tension between teams competing for resources
Effective AI resource management treats GPUs as shared enterprise assets rather than departmental property.
Measuring success through utilization metrics
Effective GPU utilization optimization depends on measurement. Without clear metrics, scheduling improvements remain theoretical.
Key indicators include:
• Average GPU utilization over time
• Job waits times by team
• Percentage of idle capacity
• Frequency of preemption or rescheduling
These metrics help leadership assess whether investments in GPUs and scheduling platforms are delivering operational value.
Overly rigid quotas can discourage experimentation. Completely open access can lead to resource hoarding. Lack of visibility creates mistrust between teams.
The most effective private GPU scheduling strategies strike a balance. They provide guardrails without micromanagement and flexibility without chaos.
For enterprises implementing structured AI resource management in India, ESDS Software Solution Ltd. GPU as a service provides managed GPU environments hosted within Indian data centers. These services support controlled scheduling, access governance, and usage visibility, helping organizations improve GPU utilization optimization while maintaining compliance and operational clarity.
For more information, contact Team ESDS through:
Visit us: https://www.esds.co.in/gpu-as-a-service
30
General Cloud Hosting Discussion / GPU Resource Scheduling Practices for Maximizing Utilization Across Teams
« Last post by manoharparakh on January 30, 2026, 05:06:14 AM »GPU capacity has quietly become one of the most constrained and expensive resources inside enterprise IT environments. As AI workloads expand across data science, engineering, analytics, and product teams, the challenge is no longer access to GPUs alone. It is how effectively those GPUs are shared, scheduled, and utilized.
Why GPU scheduling is now a leadership concern
In many enterprises, GPUs were initially deployed for a single team or a specific project. Over time, usage expanded. Data scientists trained models. Engineers ran inference pipelines. Research teams tested experiments. Soon, demand exceeded supply.
Without structured private GPU scheduling strategies, teams often fall back on informal booking, static allocation, or manual approvals. This leads to idle GPUs during off-hours and bottlenecks during peak demand. The result is poor GPU utilization optimization, even though hardware investment continues to grow.
Understanding GPU resource scheduling in practice
GPU scheduling determines how workloads are assigned to available GPU resources. In multi-team setups, scheduling must balance fairness, priority, and utilization without creating operational complexity.
At a basic level, scheduling answers three questions:
• Who can access GPUs
• When access is granted
• How much capacity is allocated
In mature environments, scheduling integrates with orchestration platforms, access policies, and usage monitoring. This enables controlled multi-team GPU sharing without sacrificing accountability.
The cost of unmanaged GPU usage
When GPUs are statically assigned to teams, utilization rates often drop below 50 percent. GPUs sit idle while other teams wait. From an accounting perspective, this inflates the effective cost per training run or inference job.
Poor scheduling also introduces hidden costs:
• Engineers waiting for compute
• Delayed model iterations
• Manual intervention by infrastructure teams
• Tension between teams competing for resources
Effective AI resource management treats GPUs as shared enterprise assets rather than departmental property.
Measuring success through utilization metrics
Effective GPU utilization optimization depends on measurement. Without clear metrics, scheduling improvements remain theoretical.
Key indicators include:
• Average GPU utilization over time
• Job waits times by team
• Percentage of idle capacity
• Frequency of preemption or rescheduling
These metrics help leadership assess whether investments in GPUs and scheduling platforms are delivering operational value.
Overly rigid quotas can discourage experimentation. Completely open access can lead to resource hoarding. Lack of visibility creates mistrust between teams.
The most effective private GPU scheduling strategies strike a balance. They provide guardrails without micromanagement and flexibility without chaos.
For enterprises implementing structured AI resource management in India, ESDS Software Solution Ltd. GPU as a service provides managed GPU environments hosted within Indian data centers. These services support controlled scheduling, access governance, and usage visibility, helping organizations improve GPU utilization optimization while maintaining compliance and operational clarity.
For more information, contact Team ESDS through:
Visit us: https://www.esds.co.in/gpu-as-a-service
Why GPU scheduling is now a leadership concern
In many enterprises, GPUs were initially deployed for a single team or a specific project. Over time, usage expanded. Data scientists trained models. Engineers ran inference pipelines. Research teams tested experiments. Soon, demand exceeded supply.
Without structured private GPU scheduling strategies, teams often fall back on informal booking, static allocation, or manual approvals. This leads to idle GPUs during off-hours and bottlenecks during peak demand. The result is poor GPU utilization optimization, even though hardware investment continues to grow.
Understanding GPU resource scheduling in practice
GPU scheduling determines how workloads are assigned to available GPU resources. In multi-team setups, scheduling must balance fairness, priority, and utilization without creating operational complexity.
At a basic level, scheduling answers three questions:
• Who can access GPUs
• When access is granted
• How much capacity is allocated
In mature environments, scheduling integrates with orchestration platforms, access policies, and usage monitoring. This enables controlled multi-team GPU sharing without sacrificing accountability.
The cost of unmanaged GPU usage
When GPUs are statically assigned to teams, utilization rates often drop below 50 percent. GPUs sit idle while other teams wait. From an accounting perspective, this inflates the effective cost per training run or inference job.
Poor scheduling also introduces hidden costs:
• Engineers waiting for compute
• Delayed model iterations
• Manual intervention by infrastructure teams
• Tension between teams competing for resources
Effective AI resource management treats GPUs as shared enterprise assets rather than departmental property.
Measuring success through utilization metrics
Effective GPU utilization optimization depends on measurement. Without clear metrics, scheduling improvements remain theoretical.
Key indicators include:
• Average GPU utilization over time
• Job waits times by team
• Percentage of idle capacity
• Frequency of preemption or rescheduling
These metrics help leadership assess whether investments in GPUs and scheduling platforms are delivering operational value.
Overly rigid quotas can discourage experimentation. Completely open access can lead to resource hoarding. Lack of visibility creates mistrust between teams.
The most effective private GPU scheduling strategies strike a balance. They provide guardrails without micromanagement and flexibility without chaos.
For enterprises implementing structured AI resource management in India, ESDS Software Solution Ltd. GPU as a service provides managed GPU environments hosted within Indian data centers. These services support controlled scheduling, access governance, and usage visibility, helping organizations improve GPU utilization optimization while maintaining compliance and operational clarity.
For more information, contact Team ESDS through:
Visit us: https://www.esds.co.in/gpu-as-a-service