Ling-3.0-flash.
*Ling-3.0-flash* is a *124B-parameter Mixture-of-Experts (MoE) model*, with approximately *5.1B parameters activated per token*. The model is designed with *token efficiency and production-scale agentic inference* as key
1× Nvidia H200 NVL · $0.50/hr.
Most-aggressive quantisation we have a working recommendation for. Lower precision = less VRAM = cheaper hardware, at a small accuracy cost.
Cheapest hosted endpoints.
Variants in the Ling family.
Ling 3.0 Flash Sante is a health and medicine-focused mixture-of-experts mode...
Ling-3.1-flash is an MIT-licensed hybrid reasoning model, 560B total and 25B ...
Ling 3.0 Flash Fin is a finance-focused mixture-of-experts model from Inclusi...
The multimodal version built on Ling-3.0-flash — 124B total / ~5.5B active pe...
Ling-2.6-1T is an instant (instruct) model from inclusionAI and the company’s...
Ling-2.6-flash is an instant (instruct) model from inclusionAI with 104B tota...
Cheapest hardware per quantisation.
Each row is one quantisation tier (the same weights compressed differently). Lower precision → lower VRAM → cheaper hardware, at the cost of small accuracy loss. $/hr refreshed hourly from each provider's API.
| Quantisation | Cheapest GPU config | Total VRAM | Live $/hr | tokens/sec | |
|---|---|---|---|---|---|
|
FP16
FP16 — half precision (default)
|
384 GB | $1.00/hr | — | Compare → | |
|
FP8
FP8 — 8-bit float (Hopper / Blackwell)
|
192 GB | $0.50/hr | 29.1 | Compare → | |
|
INT4
INT4 — 4-bit integer (~4× VRAM saving)
|
141 GB | $0.50/hr | 52.7 | Compare → |
What it costs per month across providers.
Estimate your monthly bill for Ling-3.0-flash across every host that publishes per-token pricing. Slide your token volumes; the chart + table re-rank cheapest-first.
Cheapest provider on the left.
Total monthly cost — input + output tokens combined.
Bill breakdown.
Rent the GPU instead of paying per token.
For an open-weights model like Ling-3.0-flash, you can rent a GPU and serve inference yourself. The math: cheapest GPU rental × 730 hours/month + your electricity rate × power draw.
Assumes the GPU runs 24/7 at ~85% utilisation. If your traffic is bursty, you'll pay less for the API and probably more for the GPU (idle hours still cost rental). The breakeven analysis lives on the Self-host vs API breakeven tool.