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Spheron Network

@spheron
One platform, every GPU cloud. Deploy H100s to B300s across multiple providers. On-demand, no lock-in, from a single dashboard.
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spheron.network
Joined January 2023
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  • user avatar
    Spheron Network
    @spheron
    8h
    Spot on Spheron: $1.29/hr for RTX PRO 6000. On-demand for the same card is $2.39/hr. That's 46% cheaper, as of July 2026. Job checkpoints? Take spot. Fine-tuning, batch inference, hyperparameter sweeps, rendering: all pause and resume fine. Serving live traffic or can't afford
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  • user avatar
    Spheron Network
    @spheron
    10h
    $36,000. That's the real cost to migrate a workload to AWS Trainium 3: two engineers, three weeks, a $150/hr blended rate. Most teams skip this math before they start. Trainium 3 does undercut Nvidia on inference: $0.28 per million tokens versus $0.61/M on H200 spot and $0.84/M
  • user avatar
    Spheron Network
    @spheron
    16h
    $3.84M versus $4.9M. That's the cost to train a Chinchilla-optimal 70B model (1.4 trillion tokens, about 650,000 GPU-hours) on H200 versus B200 on-demand pricing. Hardware is 47-67% of that number. R&D staff time is 29-49%. Energy is only 2-6%, which is not where the money is
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    Spheron Network
    @spheron
    18h
    325 tokens/sec. That's L40S on Llama 3.1 8B at batch size 8, up from 43.8 tok/s at batch 1. Batching is doing almost all of the work there, not the chip. For comparison, a consumer RTX 4090 hits about 6,900 tokens/sec on Llama 3 8B at Q4_K_M, because 4-bit quantization changes
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  • user avatar
    Spheron Network
    @spheron
    Aug 16
    No cloud makes you pick a lane before you've run a job. Spheron doesn't either. One marketplace, three ways to buy: Spot/on-demand, billed per minute. Reserved clusters, 8 to 512+ GPUs, InfiniBand on request. Custom sourcing turned around in 24-48 hours when you need to scale
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