Run open-source LLMs in real production | The Ravit Show
Capture live traffic, fine-tune, and deploy your own checkpoints to dedicated GPU endpoints. The demo phase is over.
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THE RAVIT SHOW
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In partnership
with Nebius
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Nebius Token Factory
Run open-source LLMs
in real production
Capture live traffic. Fine-tune and optimize. Deploy your own checkpoints to dedicated GPU endpoints. One platform, end to end.
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I have spent the last year asking teams the same question at every conference. Your demo works. Why is it not in production?
The answers are always the same. Latency is unpredictable. Costs swing month to month. Nobody can tell legal where the data actually lives. The model was never the problem. The system around it was.
That is the gap Nebius Token Factory is built to close. And the part I find most interesting is that it treats deployment as something you design, not something you inherit.
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The full loop, one platform
See it in action →
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1
Capture
Collect live production traffic as training signal
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→ |
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2
Fine-tune
LoRA or full training, plus distillation to cut cost
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→ |
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3
Deploy
Your checkpoints on dedicated GPU endpoints
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Why this matters right now
Most inference platforms hand you a shared endpoint and wish you luck. Token Factory hands you the controls. You choose the GPU type, define GPUs per replica, set scaling limits, and pick your region. Your fine-tuned checkpoint deploys to an isolated endpoint that behaves the way you configured it to behave.
The result is the three things every production team actually needs. Stable latency, because the endpoint is yours. Predictable cost, because pricing is transparent per token. Clear data residency, because you decide whether inference runs in the EU or the US.
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Your endpoint, your terms
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Hardware
Choose GPU type and replicas
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Scaling
Set your own limits
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Region
EU or US, you decide
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99.9%
Uptime SLA
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70%
Lower inference cost
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40+
Open-source models
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Dedicated endpoints with isolation and autoscaling. Distillation can cut inference cost and latency by up to 70%. Llama, DeepSeek, Qwen, GPT OSS and more. Explore the platform
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My take: the teams winning with open-source models in 2026 are not the ones with the best prompts. They are the ones who treat the model as one part of a production system. Infrastructure is the moat now.
Ravit Jain, The Ravit Show
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Explore Nebius Token Factory
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This edition of The Ravit Show newsletter is sponsored by Nebius. As always, the takes are mine.
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