33.4 tokens per second, sustained, on a 120-billion-parameter model. Here is what that buys and where the machine's limits sit.
Strata QWEN3.8-120b · 4x Tesla P100 · tensor parallel · CUDA 12
Average human reading speed is 200–300 words per minute — roughly 4 to 6 tokens per second. A 120B model generating 33 tokens per second outruns the reader, not the reader's patience. A paragraph of agent reasoning arrives in the time it takes to pour coffee. Long documents take minutes, not hours, and the machine is billing nobody for them.
| Limit | Reality |
|---|---|
| No FP8 / new-format tricks | Pascal is FP16/FP32. Newer cards generate faster per dollar. This card generation is free-ish per dollar. |
| PCIe sync overhead | Four-way tensor parallel over PCIe costs a slice of throughput versus NVLink. Accepted at design time. |
| Model load time | Streaming ~250GB from HDD to VRAM takes minutes. Boots are scheduled, not casual. |
| Single-model residency | 64GB holds one 120B model. Swapping models means a reload from the HDD array. |
| Power | ~1.2–1.4 kW under load, 100% solar. It earns its electricity by replacing a cloud subscription, not by idling. |
For comparison: at 33.4 tps, a 2,000-token response lands in about a minute. The electricity comes from a paid-off 28.94kW solar array with 85kWh of battery behind it, which makes the operating cost $0.00 per token — the number V1 was built to prove and V2 runs every day. A cloud API at $0.50–$1.50 per million output tokens would take months of heavy use to spend what these four GPUs cost. The speed isn't the point. The ownership is.