LLMPad

Testing the compute networks we can plug into LLMPad agents right now, and some of the early numbers are very strong in a few cases we’re seeing potential margins above 100%. The flow is simple: rent the cheapest compatible GPU → route it to the highest-paying live compute network → earn → shut it down or switch networks when the margin disappears. We’re checking Bittensor, Nosana, Akash, Hyperbolic, Render, Golem, Spheron, Aethir, io.net, OpenGPU and other inference/compute markets. Still validating the numbers before calling anything proven, but this is exactly the kind of agent flow I want LLMPad to support.

Just shipped Cost Saver. Choose a model and we automatically route it through the cheapest provider offering that exact model. Or use Auto Cheap and we’ll pick the lowest-cost model capable of handling the request. Every request shows what was used + how much you saved.

Four revenue streams, one market all designed to push more LLM credits into supply and bring the cost down.

Tested Higgsfield through the LLMPad API image generated successfully and $0.004 was deducted directly from my LLM credit balance.

Higgsfield is now supported on the LLMPad API. You can use your LLM credits with it, and also test it directly from the API page.

API tester now works across all models. Market + $LLM staking credits are universal. Model-token credits stay locked to that model. Your balance now shows total credits + breakdown by model.

$LLM staking pool is now live. Credits earned from the pool can be used across any model supported. Reward sources: 75% of $LLM creator rewards 20% from model-token 10% from trading-agen Your payout is based on your % share of the staking pool. llmpad.tech/llm

How creator rewards are distributed: Every 5 minutes, rewards are automatically claimed and distributed to stakers based on their % share of the pool. Example: I’m currently the only staker in the CLAUDE pool, so I received 100% of the rewards about $27, paid out as LLM credits