Moh Baddar

AI Engineer Building InfScale — open-source inference-time scaling for LLMs. Smarter LLM outputs on a budget.

[Moh Baddar @mbaddar2]

We’re collecting feedback for InferScale.

If you manage or deploy LLM workloads, what features would you want in a modern inference scaling platform?

Potential features:
• Smart load balancing
• Multi-cloud deployment
• Real-time monitoring
• Autoscaling
• Model version management
• Cost analytics

What would make your life easier?

https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[shouko @shoukointech]

Mathematician Sir Roger Penrose: "AI is a bad term. It's not intelligence"

[Moh Baddar @mbaddar2]

Building AI infrastructure products in public teaches you one thing quickly:
Scaling inference is harder than expected.

Between deployment complexity, GPU costs, orchestration, and latency optimization, AI builders spend too much time managing infrastructure.

That’s why we’re building InferScale.

Open-source. Focused on scalable AI inference workflows.

https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

For teams working with LLMs:

What’s your biggest challenge today when deploying or scaling inference infrastructure?

• Cost? • Throughput? • GPU scheduling? • Reliability? • Monitoring?

Would you use an open-source platform focused on solving these issues?

Curious to hear real-world feedback from the community.

https://t.co/zcrXVCoYJw
https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

Open LLM builders — have you faced challenges with:

• Scaling inference workloads?
• Managing GPU utilization?
• Handling deployment complexity?
• Optimizing latency and cost?

Would you be interested in a software package that helps simplify and scale these workflows?

We’d love to hear your biggest pain points.

https://t.co/zcrXVCpwz4

https://t.co/JpvbxE26Or

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

As LLM applications grow, inference infrastructure becomes one of the biggest operational challenges.

Teams often struggle with: • High serving costs • Scaling bottlenecks • GPU allocation inefficiencies • Latency issues • Complex deployment pipelines

InferScale is exploring a simpler and more scalable way to manage AI inference systems.

Learn more: https://t.co/zcrXVCoYJw
https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

We’re building InferScale in public and would love community feedback.

If you were using an AI inference scaling platform today, what would be your must-have features?

Examples: • Simpler deployment • Better observability • Faster scaling • Lower infrastructure costs • Easier integrations • Cleaner developer experience

What would make you actually adopt it?

https://t.co/zcrXVCoYJw
https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

For teams working with LLMs:

What’s your biggest challenge today when deploying or scaling inference infrastructure?

• Cost?
• Throughput?
• GPU scheduling?
• Reliability?
• Monitoring?

Would you use an open-source platform focused on solving these issues?

Curious to hear real-world feedback from the community.

https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

Did you ever feel that scaling and managing LLM inference pipelines becomes unnecessarily complex as usage grows?

From model orchestration to infrastructure costs and deployment bottlenecks, many teams building with Open LLMs struggle to maintain performance, scalability, and efficiency.

That’s where InferScale comes in — an open-source approach focused on simplifying scalable AI inference workflows.

Check it out here:
https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

As LLM applications grow, inference infrastructure becomes one of the biggest operational challenges.

Teams often struggle with:
• High serving costs
• Scaling bottlenecks
• GPU allocation inefficiencies
• Latency issues
• Complex deployment pipelines

InferScale is exploring a simpler and more scalable way to manage AI inference systems.

Learn more:
https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

We’re building InferScale for the Open LLM ecosystem.

If you could add any feature to an AI inference scaling platform, what would you want most?

Some ideas:
• Multi-model orchestration
• Auto GPU scaling
• Monitoring dashboards
• Cost optimization
• Low-latency routing
• Kubernetes integration

What features would make InferScale truly useful for your workflow?

https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

We’re building InferScale in public and would love community feedback.

If you were using an AI inference scaling platform today, what would be your must-have features?

Examples: • Simpler deployment • Better observability • Faster scaling • Lower infrastructure costs • Easier integrations • Cleaner developer experience

What would make you actually adopt it?

https://t.co/zcrXVCoYJw
https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

For teams working with LLMs:

What’s your biggest challenge today when deploying or scaling inference infrastructure?

• Cost? • Throughput? • GPU scheduling? • Reliability? • Monitoring?

Would you use an open-source platform focused on solving these issues?

Curious to hear real-world feedback from the community.

https://t.co/zcrXVCpwz4

https://t.co/JpvbxE26Or

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

We’re building InferScale for the Open LLM ecosystem.

If you could add any feature to an AI inference scaling platform, what would you want most?

Some ideas:
• Multi-model orchestration
• Auto GPU scaling
• Monitoring dashboards
• Cost optimization
• Low-latency routing
• Kubernetes integration

What features would make InferScale truly useful for your workflow?

https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

For teams working with LLMs:

What’s your biggest challenge today when deploying or scaling inference infrastructure?

• Cost? • Throughput? • GPU scheduling? • Reliability? • Monitoring?

Would you use an open-source platform focused on solving these issues?

Curious to hear real-world feedback from the community.

https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

Builders and founders:

Have you experienced infrastructure headaches while scaling AI or LLM products?

We’re exploring InferScale as a way to simplify inference scaling and orchestration.

Would a tool like this help your workflow? What problems would you expect it to solve first?

https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

Building AI infrastructure products in public teaches you one thing quickly: Scaling inference is harder than expected.

Between deployment complexity, GPU costs, orchestration, and latency optimization, AI builders spend too much time managing infrastructure.

That’s why we’re building InferScale.

Open-source. Focused on scalable AI inference workflows.

https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

As LLM applications grow, inference infrastructure becomes one of the biggest operational challenges.

Teams often struggle with: • High serving costs • Scaling bottlenecks • GPU allocation inefficiencies • Latency issues • Complex deployment pipelines

InferScale is exploring a simpler and more scalable way to manage AI inference systems.

Learn more: https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

Open LLM builders — have you faced challenges with:

• Scaling inference workloads? • Managing GPU utilization? • Handling deployment complexity? • Optimizing latency and cost?

Would you be interested in a software package that helps simplify and scale these workflows?

We’d love to hear your biggest pain points.

https://t.co/zcrXVCoYJw

https://t.co/JpvbxE1yYT

#AI #LLM #InfScale #Betaflow

[Moh Baddar @mbaddar2]

Did you ever feel that scaling and managing LLM inference pipelines becomes unnecessarily complex as usage grows?
From model orchestration to infrastructure costs and deployment bottlenecks, many teams building with Open LLMs struggle to maintain performance, scalability, and efficiency.
That’s where InferScale comes in — an open-source approach focused on simplifying scalable AI inference workflows.
Check it out here: https://t.co/zcrXVCoYJw
[Attach Image] https://t.co/JpvbxE1yYT
#AI #LLM #InfScale #Betaflow