What the disclosed numbers imply
DeepSeek published its daily token volume, revenue per GPU-hour, API prices, engine throughput curves, prefill-to-decode ratio and cache size. Together they pin down the token mix behind its cheapest model. Move the output share and the operating point and watch revenue, compute and storage agree or refuse to.
Sources: DeepInfer: the cache is the business · deepinfer-lab
Instrument / What the disclosed numbers imply
155 trillion tokens a day at $38 a GPU-hour. What mix of tokens makes that true?
DeepSeek published the daily token volume, the revenue per GPU-hour, the API prices, the throughput curves of its own engine, and the fact that prefill and decode GPUs run roughly 1:1. Those five things pin down the token mix more tightly than you would expect. Pick a share of output tokens and an operating point on DeepInfer's decode chart; the instrument solves for the cache-hit rate that keeps prefill and decode balanced, then checks whether the GPU count the revenue implies can actually compute the misses and outputs in a day. A third disclosure, about 14.4 TB of cache per GPU on a store of about 100 PiB, says how many GPUs sit behind the cache. The shaded horizontal band is that count. Watch where the lines cross it.
| token class | tokens / day | share of tokens | share of revenue |
|---|
How this is computed
Let T be 155 trillion tokens a day, o the output share and h the cache-hit rate of input tokens. Then outputs are O = oT, hits H = h(1 - o)T and misses M = (1 - h)(1 - o)T. Daily revenue at off-peak prices is 0.003H + 0.15M + 0.60O per million tokens, and dividing by $38 × 24 gives the GPU count the revenue implies.
Prefill GPUs must compute the misses: M / (prefill tok/s × 86,400 × efficiency). Decode GPUs must produce the outputs the same way. The Q&A says the two pools run roughly 1:1, which fixes h for any o: 1 - h = o × prefill / (decode × (1 - o)). The sum of the two pools, at benchmark throughput all day, is the capacity count. If it is below the revenue count the mix is feasible, with the ratio being the average utilization the fleet would need.
Storage: the tokens written to the cache each day are the misses plus the outputs. Holding them for the published TTL at the chosen bytes per token, times 1.2 for the 10+2 erasure coding on 3FS, gives the PiB figure, which the verdict compares with the roughly 100 PiB DeepSeek says it runs.
Store band: the Q&A gives about 100 PiB of SSD and about 14.4 TB of logical cache per online GPU. Dividing gives 6,516 GPUs if the 100 PiB is physical (logical is 100 / 1.2) and 7,819 if it is already logical. The retention readout divides 14.4 TB by the bytes each revenue-implied GPU's share of misses and outputs adds per day.
Not modelled: vision-encoder GPUs, storage nodes, the diurnal shape of traffic, peak-hour pricing (double the off-peak rates), and whatever a "cluster" means in the original sentence. The code is in deepinfer-lab.