Estimating the Theoretical Parameter Capacity of a Hypothetical GPT-6-Class Model [D]
I've been exploring a hypothetical question: How large could a model's parameter count be if a major AI provider allocated a substantial GPU cluster to inference while targeting 66 output tokens per second per active user?
Proof1 community thread
Key points
- Estimating the Theoretical Parameter Capacity of a Hypothetical GPT-6-Class Model
- GPU resources: 800,000 H100-equivalent GPUs available across the broader infrastructure.
- Weight format: FP8, approximately one byte per parameter.
- If 30% of the assumed GPU pool were allocated to inference:
Sources (1)
- [1]Estimating the Theoretical Parameter Capacity of a Hypothetical GPT-6-Class Model [D]r/MachineLearning (top, daily) · Oct 17, 12:39 PM
I've been exploring a hypothetical question: How large could a model's parameter count be if a major AI provider allocated a substantial GPU cluster to inference while targeting 66 output tokens per second per active user?
Estimating the Theoretical Parameter Capacity of a Hypothetical GPT-6-Class Model
Extractive summary: sentences quoted from the sources.
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