What hardware do you need to run Ornith 1.5 9B?

Ornith 1.5 9B at Q4_K_M is 5.8 GB of weights, with a context ceiling of 256k tokens. DeepReinforce's MIT-licensed coding model, post-trained on Qwen3.5. On the cheapest machine that runs it, a Mac mini M6, 16GB, it generates 17 tok/s at 32k of context, worked out from memory bandwidth.

How good is Ornith 1.5 9B, really?

It has not been placed on the intelligence index yet. See the whole table.

What Ornith 1.5 9B costs either way

Nobody rents Ornith 1.5 9B by the token. The closest hosted match, Qwen3.5 9B, costs $0.08 per million input tokens and $0.13 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 Ornith 1.5 9B

Every machine here runs it, but not to the same length. The Mac mini M6, 16GB stops at 128k; the Strix Halo Framework Desktop, 32GB takes it to 256k, which is this model's own ceiling and the longest the calculator offers. The weights are the same size on every machine; what differs is the memory left for the key-value cache, which grows with every token you keep.

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 256k 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. 2 of the 8 above reach it, and the slowest is 17 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 Ornith 1.5 9B at 256k on the Strix Halo Framework Desktop, 32GB

How much memory does Ornith 1.5 9B need at 256k?

Every figure above is priced at 32k of context. At 256k, which is this model's own ceiling and the longest the calculator offers, Ornith 1.5 9B needs 14.4 GB rather than 6.9 GB. The weights are the same either way; the key-value cache goes from 1.1 GB to 8.6 GB.

All 37 machines here still sold at a published price hold Ornith 1.5 9B at 32k and 33 hold it at 256k. The cheapest still sold goes from the Mac mini M6, 16GB at $899 to the Mac mini M6, 24GB at $1,099.

On the Mac mini M6, 24GB it runs 19 tok/s estimated at 32k and 8.9 tok/s estimated at 256k. Every token generated reads the whole cache back, so a longer window is slower as well as bigger.

The specifics

Parameters

9.7B

Quantisation

Q4_K_M

Weights on disk

5.8 GB

KV cache

1.1 GB at 32k context — Hybrid: three linear-attention layers per full-attention layer. Multimodal; the vision projector is a separate 0.92 GB file, not counted here. How weights and cache add up.

Maximum context

256k tokens

Licence

MIT

Sources

ornith-ai/Ornith-1.5-9B, ornith-ai/Ornith-1.5-9B-GGUF