What hardware do you need to run Ling 3.0 flash?
Ling 3.0 flash at Q4_K_M is 78 GB of weights, with a context ceiling of 256k tokens. Not yet rated: released after our last ratings pass. MIT-licensed 124B with only 5.1B active, for machines with a lot of memory.
How good is it, really?
On the Artificial Analysis Intelligence Index v4.3 it scores 25, which puts it in the Haiku-class band. In the same band as Anthropic's cheap, fast tier. Every current OpenAI model scores above this band. Score source. See the whole table.
- Summarising — not rated
- Translation — not rated
- Everyday coding — not rated
- Reasoning & maths — not rated
- Agentic work — not rated
What it costs either way
Renting the same model costs $0.021 per million input tokens and $0.063 per million output (OpenRouter, cheapest active endpoint, checked 2026-09-09). Buying a machine only beats that if you use it hard enough, for long enough, that the hardware price divides down below the rental bill.
Machines that run it
| Machine | Price | Speed at 32k | Pay-back | |
|---|---|---|---|---|
| Strix Halo Framework Desktop, 128GB | $3,449 | 20 tok/s estimated | Pays back in 4,468 years | Run the numbers |
| DGX Spark GB10 Grace Blackwell, 128GB | $4,699 | 26 tok/s estimated | Pays back in 4,106 years | Run the numbers |
| Mac Studio M5 Max, 128GB | $5,099 | 26 tok/s estimated | Pays back in 3,743 years | Run the numbers |
| MacBook Pro M5 Max (16-inch), 128GB | $6,999 | 26 tok/s estimated | Pays back in 5,138 years | Run the numbers |
One machine per family, cheapest first. Speeds are measured where a public benchmark exists and estimated from memory bandwidth otherwise; the calculator says which for any configuration.
The specifics
- Parameters
- 124B, of which 5.1B are active per token
- Quantisation
- Q4_K_M
- Weights on disk
- 78 GB
- KV cache
- 3.8 GB at 32k context — Hybrid: five linear-attention layers per full-attention layer. Those seven layers use compressed latent attention, which this figure does NOT model, so the KV cache shown is an over-estimate.
- Maximum context
- 256k tokens (256k)
- Licence
- MIT
- Sources
- source 1, source 2