What hardware do you need to run Qwen3 8B?

Qwen3 8B at Q4_K_M is 5.0 GB of weights, with a context ceiling of 40k tokens. Quick and competent on short tasks. Reasoning mode helps a little and costs a lot of tokens.

Cheapest machine that runs itMac mini M6, 16GB at $899

Fastest of the ones listedMac Studio M5 Ultra, 96GB — 91 tok/s at 32k context

Honest answer on costPays back in 40 years at 500k tokens a day.

How good is it, really?

On the Artificial Analysis Intelligence Index v4.3 it scores 5 (reasoning mode; 6 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. Score source. See the whole table.

Summarising — good
Translation — usable
Everyday coding — usable
Reasoning & maths — don’t
Agentic work — don’t

What it costs either way

Renting the same model costs $0.117 per million input tokens and $0.455 per million output (OpenRouter, cheapest active endpoint, checked 2026-09-03). 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.

Machines that run it

Machine	Price	Speed at 32k	Pay-back
Mac mini M6, 16GB	$899	12 tok/s estimated	Pays back in 40 years	Run the numbers
Strix Halo Framework Desktop, 32GB	$1,269	19 tok/s estimated	Pays back in 59 years	Run the numbers
MacBook Air M5 (13-inch), 16GB	$1,299	12 tok/s estimated	Pays back in 58 years	Run the numbers
MacBook Pro M5 (14-inch), 16GB	$1,999	12 tok/s estimated	Pays back in 90 years	Run the numbers
Mac Studio M5 Max, 36GB	$2,499	35 tok/s estimated	Pays back in 109 years	Run the numbers
DGX Spark GB10 Grace Blackwell, 128GB	$4,699	23 tok/s measured	Pays back in 217 years	Run the numbers

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 specifics

Parameters: 8.2B
Quantisation: Q4_K_M
Weights on disk: 5.0 GB
KV cache: 4.8 GB at 32k context
Maximum context: 40k tokens (32k natively; 128k with YaRN, which llama.cpp can enable)
Licence: Apache 2.0
Sources: source 1, source 2