Sunk Cost sunkcost.ai Data checked 2026-09-03

What hardware do you need to run Qwen3-Coder 30B-A3B?

Qwen3-Coder 30B-A3B at Q4_K_M is 19 GB of weights, with a context ceiling of 256k tokens. Built for coding and agentic coding loops. The best local option at this size for Claude Code-style tooling. On the cheapest machine that runs it, a Framework Desktop, 32GB, it generates 27 tok/s at 32k of context, worked out from memory bandwidth.

Cheapest machine that runs itStrix Halo Framework Desktop, 32GB at $1,269
Fastest machine that runs itNVIDIA GeForce RTX 5090, 32GB at $1,999card only — 113 tok/s estimated at 32k context
Honest answer on costPays back in 101 years at 500k tokens a day.

How good is Qwen3-Coder 30B-A3B, 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: vs Qwen3-Coder Next, the next model down the leaderboard. Also head to head: vs Qwen3 30B-A3B Instruct 2507, the last-generation Qwen nearest it in size. One more model needs much the same memory at 32k of context: vs Nemotron 3.5 Bolt 74B-A9B.

What Qwen3-Coder 30B-A3B costs either way

Renting the same model costs $0.07 per million input tokens and $0.28 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 30B-A3B

Every machine here runs it, but not to the same length. The Strix Halo Framework Desktop, 32GB stops at 32k; the DGX Spark GB10 Grace Blackwell, 128GB 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.

MachinePriceSpeed at 32kLongest contextPay-back
Strix Halo Framework Desktop, 32GB $1,269 27 tok/s estimated 32k Pays back in 101 years Run the numbers
AMD Radeon AI PRO R9700, 32GB $1,299card only 64 tok/s estimated 64k Pays back in 103 years Run the numbers
NVIDIA GeForce RTX 5090, 32GB $1,999card only 113 tok/s estimated 64k Pays back in 161 years Run the numbers
Mac mini M5 Pro, 48GB $2,299 18 tok/s estimated 128k Pays back in 211 years Run the numbers
Mac Studio M5 Max, 36GB $2,499 26 tok/s estimated 64k Pays back in 204 years Run the numbers
MacBook Pro M5 Pro (16-inch), 48GB $3,599 18 tok/s estimated 128k Pays back in 330 years Run the numbers
DGX Spark GB10 Grace Blackwell, 128GB $4,699 34 tok/s measured 256k Pays back in 366 years Run the numbers

The table is the cheapest machine in each family, not the only one that runs it: 41 of the 56 machines on this site hold it at 32k. Within a family only memory decides, so that is every Mac Studio and Strix Halo box at any size, every NVIDIA card with 24GB or more, every Mac mini and MacBook Pro with 48GB or more, the Radeon AI PRO R9700, 32GB 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 7 above reaches it, and the slowest is 18 tok/s.

The cheapest machine that runs it is sold with 32 GB. There is a page on what 32 GB runs, machine by machine: it names the 6 machines here sold at that size, and the 32 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 24 GB, on the GeForce RTX 3090, 24GB at $1,499, card only, which hands a model 23 GB and is no longer sold.

Run Qwen3-Coder 30B-A3B at 256k on the DGX Spark GB10 Grace Blackwell, 128GB

How much memory does Qwen3-Coder 30B-A3B 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 30B-A3B needs 44.3 GB rather than 21.8 GB. The weights are the same either way; the key-value cache goes from 3.2 GB to 25.8 GB.

28 of the 37 machines here still sold at a published price hold Qwen3-Coder 30B-A3B at 32k and 20 hold it at 256k. The cheapest still sold goes from the Framework Desktop, 32GB at $1,269 to the Corsair AI Workstation 300, 64GB at $1,700.

On the Corsair AI Workstation 300, 64GB it runs 27 tok/s estimated at 32k and 5.1 tok/s estimated at 256k. Every token generated reads the whole cache back, so a longer window is slower as well as bigger.

What has been measured running Qwen3-Coder 30B-A3B

One of the speeds in the table above was measured, and there are more measurements than that table shows. Ten machines here have a published speed for Qwen3-Coder 30B-A3B. The table above has one of them; of the other nine, four are no longer sold and five are still sold but not the cheapest of their family. They are below, as published and again at 32k of context, which is what every other speed on this page is taken at.

MachinePriceAt 32kAs published
Strix Halo Beelink GTR9 Pro, 128GB $4,349 37 tok/s measured 97 tok/smeasured at an empty window
Mac Studio M3 Ultra, 96GBdiscontinued $3,999 36 tok/s measured 95 tok/smeasured at an empty window
DGX Spark GB10 Grace Blackwell, 128GB $4,699 34 tok/s measured 89 tok/smeasured at an empty window
Strix Halo GMKtec EVO-X2, 128GB $3,500 33 tok/s measured 86 tok/smeasured at an empty window
Strix Halo GMKtec EVO-X2, 64GB $2,200 33 tok/s measured 86 tok/smeasured at an empty window
Mac Studio M4 Max, 128GBdiscontinued $3,499 31 tok/s measured 82 tok/smeasured at an empty window
Mac Studio M4 Max, 48GBdiscontinued $2,499 31 tok/s measured 82 tok/smeasured at an empty window
Mac Studio M4 Max, 64GBdiscontinued $2,699 31 tok/s measured 82 tok/smeasured at an empty window
Strix Halo Framework Desktop, 128GB $3,449 25 tok/s measured 66 tok/smeasured at an empty window
Strix Halo Framework Desktop, 64GB $1,959 25 tok/s measured 66 tok/smeasured at an empty window

A published figure is what its source reports, at the window the model was run at. The 32k figure is that measurement read at 32k of context: every token generated reads the weights and the whole key-value cache, so the longer the window the slower it runs. A discontinued machine's price is the one it launched at. Where two memory tiers of one machine carry the same figure, the speed is set by the bandwidth of the chip they share rather than by the amount of memory. Each published figure links the source that took it.

Two more machines, at a shorter window

The table above lists the machines that run it at 32k, the context the calculator starts on, where Qwen3-Coder 30B-A3B needs 22 GB. Two more machines hold it at a shorter window, because the weights are the same size either way and the key-value cache is not. None of them is cheaper than the table above, which starts at $1,269.

MachinePriceLongest window it fitsNeeds there
Mac mini M6, 32GB $1,299 16k 20 GB
MacBook Pro M5 (14-inch), 32GB $2,399 16k 20 GB

One machine per family, cheapest first, from the same list as the table above. Each window is the longest setting the calculator offers that the machine still holds this model at, and it opens the calculator on that machine at that length. What it needs there is the weights plus the key-value cache at that window, measured against the memory each machine's GPU can address, which that machine's own page gives.

The specifics

Parameters
30.5B, of which 3.3B are active per token
Quantisation
Q4_K_M
Weights on disk
19 GB
KV cache
3.2 GB at 32k context. How weights and cache add up.
Maximum context
256k tokens (up to 1M with YaRN)
Licence
Apache 2.0
Sources
unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF, Qwen/Qwen3-Coder-30B-A3B-Instruct