For those who would like to know the total and active parameter count of this model: even though Google doesn't disclose the model technicals, we can infer them within relatively tight margins based on what we do know.
We know they serve the model on TPU 8i, which we have plenty of hard specs for (so we know the key constraints: total memory and bandwidth and compute flops). We can also set a ceiling on the compute complexity and memory demand of the model based on knowing they will be at least as efficient as what is disclosed in the Deepseek V4 Technical Report.
We can also assume that the model was explicitly built to run efficiently in a RadixAttention style batched serving scenario on a single TPU 8i (so no tensor parallelism, etc. to avoid unnecessary overheads... Google explicitly designed the 8th-generation inference architecture to eliminate the need for tensor sharding on mid-sized models).
We know Google intends to serve this model at a floor speed of around 280 tok/s too.
Putting all these pieces together, we can confidently say this model is ~250-300B total, and 10-16B active parameters. Likely mostly FP4 with FP8 where it matters most.
Visual:
┌────────────────────────────────────────────────────────┐
│ TPU 8i VRAM (288 GB) │
├───────────────────────────┬────────────────────────────┤
│ Static Model Weights │ Dynamic Allocations & │
│ (250B - 300B @ Mixed │ Compressed KV Caches │
│ FP4/FP8) │ (RadixAttention / SRAM) │
│ ~110 GB - 150 GB │ ~138 GB - 178 GB │
└───────────────────────────┴────────────────────────────┘
I do model serving optimization work. This is napkin math.
Edit: There's one factor I under-rated in my initial estimate... TurboQuant. This is a compute to KV memory use tradeoff. It's plausible with TurboQuant at a quality-neutral setting they've gotten the model up to 400B with similar economics. This is a variable effecting concurrency and the the way they decided total model size was likely based on what they see for the average user's average KV cache depth in real-world usage.