What hardware do you need to run Ling 3.0 tiny?

Ling 3.0 tiny at Q4_K_M is 4.8 GB of weights, with a context ceiling of 128k tokens. 1.3B active in a 7.9B mixture, so it runs quickly on very little memory. On the cheapest machine that runs it, a Mac mini M6, 16GB, it generates 45 tok/s at 32k of context, worked out from memory bandwidth.

How good is Ling 3.0 tiny, really?

On the Artificial Analysis Intelligence Index v4.3 it scores 12, which puts it in the Below every hosted tier band. Fine for simple, well-specified tasks. Noticeably less capable than anything the big labs sell today. See the whole table.

Head to head with its neighbours on the leaderboard: vs gpt-oss-120b above it, vs Granite 4.2 8B below.

What Ling 3.0 tiny costs either way

Nobody rents Ling 3.0 tiny by the token. The closest hosted match, Granite 4.2 8B, costs $0.06 per million input tokens and $0.15 per million output (OpenRouter, cheapest active endpoint, checked 2026-10-29). 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. What a million tokens costs each way puts the two prices side by side.

Machines that run Ling 3.0 tiny

Every machine here holds it to 128k, this model's own ceiling, so the choice between them is speed and price rather than how much you can keep in the window.

All 56 machines on this site run it at 32k, not just the eight in the table. One table sets every machine against the others: price, the memory its GPU can use, what it holds and how long it takes to pay for itself.

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 longest context is the longest setting the calculator offers that the machine still holds this model at, cache included, and no machine is shown taking it past its own 128k limit. Each one opens the calculator on that machine at that length.

For scale, the calculator starts from 80 tok/s for a hosted API and times a local machine against it. 5 of the 8 above reach it, and the slowest is 45 tok/s.

The cheapest machine that runs it is sold with 16 GB. There is a page on what 16 GB runs, machine by machine: it names the 6 machines here sold at that size, and the 15 current models the roomiest of them holds at 32k, this one among them.

It fits in less memory than that: the smallest size here that runs it at 32k is 12 GB, on the GeForce RTX 3060, 12GB at $329, card only, which hands a model 11 GB and is no longer sold.

Run Ling 3.0 tiny at 128k on the Mac mini M6, 16GB

How much memory does Ling 3.0 tiny need at 128k?

Every figure above is priced at 32k of context. At 128k, this model's own ceiling, Ling 3.0 tiny needs 5.7 GB rather than 5 GB. The weights are the same either way; the key-value cache goes from 0.2 GB to 0.9 GB.

The window costs nothing in hardware: all 37 machines here still sold at a published price hold Ling 3.0 tiny at both lengths, and the cheapest of them is the Mac mini M6, 16GB at $899.

On the Mac mini M6, 16GB it runs 45 tok/s estimated at 32k and 27 tok/s estimated at 128k. Every token generated reads the whole cache back, so a longer window is slower as well as bigger.

The specifics

Parameters

7.9B, of which 1.3B are active per token

Quantisation

Q4_K_M

Weights on disk

4.8 GB

KV cache

0.2 GB at 32k context — 18 of the 24 layers are linear attention and keep a state of a fixed size, which does not grow with the context. The other 6 use latent attention: each caches one 512-number compressed vector plus a 64-number rotary key per token, 1,152 bytes a layer at 16 bits, so 6,912 bytes a token for the whole model. How weights and cache add up.

Maximum context

128k tokens

Licence

MIT

Sources

inclusionAI/Ling-3.0-tiny, inclusionAI/Ling-3.0-tiny-GGUF