๐๐ ๐๐๐ก ๐๐ ๐ฆ๐ ๐๐ฅ๐ง, ๐๐จ๐ง ๐๐ง ๐ฆ๐ง๐๐๐ ๐ก๐๐๐๐ฆ ๐๐ข๐ ๐ฃ๐จ๐ง๐ ๐ง๐ข ๐๐๐ง
โฅ ๐ง๐๐ ๐๐๐๐๐๐ก ๐๐ข๐ฆ๐ง ๐ข๐ ๐๐
When you send a prompt to an AI model and receive an answer in seconds, it can feel almost effortless.
Behind that response is a significant amount of computation.
AI inference is essentially:
Input โ model processes the request โ output
The question is, where does all that computation happen?
โข ๐๐ง๐ง๐๐ก๐๐๐ฅ๐๐ฅ๐๐ ๐ง๐๐๐๐ฆ ๐ ๐๐๐๐๐๐ฅ๐๐ก๐ง ๐๐ฃ๐ฃ๐ฅ๐ข๐๐๐
BTTInferGrid explores decentralized infrastructure for AI inference by coordinating distributed computing resources.
Instead of depending entirely on centralized infrastructure, available resources such as GPUs can become part of a broader network.
The idea is simple:
Compute providers contribute resources.
AI workloads are sent to available infrastructure.
Results can be subject to verification.
The network coordinates where workloads should run.
Participants can receive incentives for contributing useful compute.
โ ๐ง๐๐ ๐ฅ๐๐๐ ๐๐๐๐๐๐๐ก๐๐ ๐๐ฆ ๐๐ข๐ข๐ฅ๐๐๐ก๐๐ง๐๐ข๐ก
Having GPUs is only one piece of the puzzle.
A decentralized AI compute network also needs to solve for:
Performance
Reliability
Verification
Resource allocation
Economic incentives
Without coordination, distributed compute is simply a collection of disconnected machines.
โฑ ๐ง๐๐๐ฆ ๐๐ฆ ๐ช๐๐๐ฅ๐ ๐ง๐๐ ๐๐๐๐ ๐๐๐ง๐ฆ ๐๐ก๐ง๐๐ฅ๐๐ฆ๐ง๐๐ก๐
BTTInferGrid treats compute as a network resource.
Hardware can contribute.
Workloads can be distributed.
Results can be evaluated.
Resources can be coordinated.
@justinsuntron
#TRONEcoStar @BitTorrent_Official
โฅ ๐ง๐๐ ๐๐๐๐๐๐ก ๐๐ข๐ฆ๐ง ๐ข๐ ๐๐
When you send a prompt to an AI model and receive an answer in seconds, it can feel almost effortless.
Behind that response is a significant amount of computation.
AI inference is essentially:
Input โ model processes the request โ output
The question is, where does all that computation happen?
โข ๐๐ง๐ง๐๐ก๐๐๐ฅ๐๐ฅ๐๐ ๐ง๐๐๐๐ฆ ๐ ๐๐๐๐๐๐ฅ๐๐ก๐ง ๐๐ฃ๐ฃ๐ฅ๐ข๐๐๐
BTTInferGrid explores decentralized infrastructure for AI inference by coordinating distributed computing resources.
Instead of depending entirely on centralized infrastructure, available resources such as GPUs can become part of a broader network.
The idea is simple:
Compute providers contribute resources.
AI workloads are sent to available infrastructure.
Results can be subject to verification.
The network coordinates where workloads should run.
Participants can receive incentives for contributing useful compute.
โ ๐ง๐๐ ๐ฅ๐๐๐ ๐๐๐๐๐๐๐ก๐๐ ๐๐ฆ ๐๐ข๐ข๐ฅ๐๐๐ก๐๐ง๐๐ข๐ก
Having GPUs is only one piece of the puzzle.
A decentralized AI compute network also needs to solve for:
Performance
Reliability
Verification
Resource allocation
Economic incentives
Without coordination, distributed compute is simply a collection of disconnected machines.
โฑ ๐ง๐๐๐ฆ ๐๐ฆ ๐ช๐๐๐ฅ๐ ๐ง๐๐ ๐๐๐๐ ๐๐๐ง๐ฆ ๐๐ก๐ง๐๐ฅ๐๐ฆ๐ง๐๐ก๐
BTTInferGrid treats compute as a network resource.
Hardware can contribute.
Workloads can be distributed.
Results can be evaluated.
Resources can be coordinated.
@justinsuntron
#TRONEcoStar @BitTorrent_Official
