What hardware do you need to run Qwen3-Coder Next?

Qwen3-Coder Next at Q4_K_M is 48 GB of weights, with a context ceiling of 256k tokens. Qwen's current coding model: 80B on disk but only 3B active per token. On the cheapest machine that runs it, a Framework Desktop, 128GB, it generates 54 tok/s at 32k of context, worked out from memory bandwidth.

How good is Qwen3-Coder Next, really?

On the Artificial Analysis Intelligence Index v4.3 it scores 10, 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 Qwen3-Coder 30B-A3B above it, vs gpt-oss-20b below. One more model needs much the same memory at 32k of context: vs DeepSeek-R1-Distill-Llama-70B.

What Qwen3-Coder Next costs either way

Renting the same model costs $0.12 per million input tokens and $0.8 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-Coder Next

Every machine here holds it to 256k, which is this model's own ceiling and the longest the calculator offers, so the choice between them is speed and price rather than how much you can keep in the window.

The table is the cheapest machine in each family, not the only one that runs it: 19 of the 56 machines on this site hold it at 32k. Within a family only memory decides, so that is every Mac Studio and NVIDIA card with 96GB or more, every Strix Halo box and MacBook Pro with 128GB or more and the DGX Spark, 128GB. 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. 1 of the 5 above reaches it, and the slowest is 54 tok/s.

The cheapest machine that runs it is sold with 128 GB. There is a page on what 128 GB runs, machine by machine: it names the 11 machines here sold at that size, and the 39 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 96 GB, on the RTX PRO 6000 Blackwell, 96GB at $18,000, card only, which hands a model 95 GB.

Run Qwen3-Coder Next at 256k on the Strix Halo Framework Desktop, 128GB

How much memory does Qwen3-Coder Next 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-Coder Next needs 54.9 GB rather than 49.2 GB. The weights are the same either way; the key-value cache goes from 0.8 GB to 6.4 GB.

The window costs nothing in hardware: the same 13 of the 37 machines here still sold at a published price hold Qwen3-Coder Next at both lengths, and the cheapest of them is the Framework Desktop, 128GB at $3,449.

On the Framework Desktop, 128GB it runs 54 tok/s estimated at 32k and 17 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

79.7B, of which 3B are active per token

Quantisation

Q4_K_M

Weights on disk

48 GB

KV cache

0.8 GB at 32k context — Hybrid: 12 of 48 layers hold a growing cache. How weights and cache add up.

Maximum context

256k tokens

Licence

Apache 2.0

Sources

Qwen/Qwen3-Coder-Next