🔌
New: connect Claude & other AIs to GenAIList over MCP — research the catalog and contribute to the shared knowledge base. Learn how →
// RUN — FITS
Can the NVIDIA GeForce RTX 2050 run Tensor-Transformer(1core)+PN (WT103)?
Yes — here's how fast and up to what context, with real measured numbers.
Tensor-Transformer(1core)+PN (WT103) on the NVIDIA GeForce RTX 2050
| Quant | Fits? | Max context | Speed | Beyond context |
|---|---|---|---|---|
| FP16 | yes | 74k | 479.2-571.2 t/s | ~42.8-51 (slow) |
| Q8 | yes | 75k | 958.5-1142.3 t/s | ~85.6-102 (slow) |
| Q4 | yes | 76k | 1711.6-2039.9 t/s | ~152.8-182.1 (slow) |
Estimate (memory-bound). Beyond "max context" the KV cache spills to SSD swap and speed drops to the "beyond" figure.
→ All models on the NVIDIA GeForce RTX 2050
→ Tensor-Transformer(1core)+PN (WT103) specs
→ Other hardware
FAQ
Can the NVIDIA GeForce RTX 2050 run Tensor-Transformer(1core)+PN (WT103)?▶
Yes. At Q4 it generates ~1711.6-2039.9 tok/s and fits up to 76k context; beyond that it spills to SSD swap and slows to ~152.8-182.1 tok/s.
How much context fits?▶
At Q4, up to about 76k tokens stay in fast memory; longer context spills to SSD swap and throughput collapses.