What hardware do you need to run Qwen3.5 4B?

Qwen3.5 4B at Q4_K_M is 2.7 GB of weights, with a context ceiling of 256k tokens. Runs on almost anything and still scores respectably. On the cheapest machine that runs it, a Mac mini M6, 16GB, it generates 30 tok/s at 32k of context, worked out from memory bandwidth.

How good is Qwen3.5 4B, really?

On the Artificial Analysis Intelligence Index v4.3 it scores 13 (reasoning mode; 11 without), 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 MiniCPM5 2B above it, vs gpt-oss-120b below.

What Qwen3.5 4B costs either way

Nobody rents Qwen3.5 4B 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 Qwen3.5 4B

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. 3 of the 8 above reach it, and the slowest is 30 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 Qwen3.5 4B at 256k on the Strix Halo Framework Desktop, 32GB

How much memory does Qwen3.5 4B 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, Qwen3.5 4B needs 11.3 GB rather than 3.8 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 Qwen3.5 4B 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 33 tok/s estimated at 32k and 11 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

4.7B

Quantisation

Q4_K_M

Weights on disk

2.7 GB

KV cache

1.1 GB at 32k context — Hybrid: three linear-attention layers per full-attention layer. How weights and cache add up.

Maximum context

256k tokens

Licence

Apache 2.0

Sources

Qwen/Qwen3.5-4B