Can I Run Qwen3-Coder on 24 GB system RAM?

Autor: Jakub Rusinowski · Ostatnia aktualizacja: 29 września 2026

Yes

Yes — Qwen3-Coder 30B-A3B (MoE) at Q3_K_M needs about 14.6 GB of the 19.2 GB usable on 24 GB system RAM (~4.6 GB spare), at ~34.3 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: high · Recommended quantization: Q3_K_M · Estimated speed: ~34.3 tok/s

See what else this hardware can run →

Kup ten sprzęt AMD Radeon RX 7900 XT 20GB — 20 GB VRAM · 315 W board power Uruchom w chmurze RTX 4090 w RunPod — od $0.34/godz. · stawka sprawdzona 2026-07

lub porównaj w Vast.ai od $0.35/godz. (typical low · varies)

Jako partner Amazon zarabiamy na kwalifikujących się zakupach. Linki do chmurowych GPU są linkami polecającymi — możemy otrzymać prowizję bez dodatkowych kosztów dla Ciebie.

24 GB system RAM — what it gives a model

Usable memory for models	19.2 GB
Memory bandwidth	90 GB/s

Qwen3-Coder on 24 GB system RAM: memory by quantization

Quant	Memory needed	Fits 19.2 GB?	Max context	Est. speed	Download
F16	62.6 GB	✗ No	—	—	61 GB
Q8_0	34 GB	✗ No	—	—	32.4 GB
Q6_K	26.6 GB	✗ No	—	—	25 GB
Q5_K_M	23.2 GB	✗ No	—	—	21.6 GB
Q4_K_M	20 GB	✗ No	—	—	18.4 GB
Q3_K_M	14.6 GB	✓ Yes	32K	~34.3 tok/s	13 GB
Q2_K	11.6 GB	✓ Yes	64K	~41 tok/s	10 GB

Which Qwen3-Coder sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen3-Coder 480B-A35B (MoE)	291.6 GB	✗ Too large	—
Qwen3-Coder-Next (80B-A3B MoE)	49.4 GB	✗ Too large	—
Qwen3-Coder 30B-A3B (MoE)	20 GB	✗ Too large	—

What to watch out for

Q3_K_M is the only quantization that fits, and it is a heavily degraded one — expect noticeably worse output than the same model at Q4_K_M. A smaller model at Q4 is usually the better trade.
3 larger variants of Qwen3-Coder do not fit and would need CPU offload or different hardware.
These figures assume CPU-only inference. Any discrete GPU, even an 8 GB one, will be several times faster for models that fit in its VRAM.

Recommended setup

llama.cpp (CPU build) or Ollama — both run without a GPU

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
19.2 GB of the 24 GB is treated as usable for model weights (80% — the rest is the OS and running applications).
DDR5-5600 dual channel at 89.6 GB/s peak. CPU decode is assumed to sustain 35% of that peak, because CPU inference is not purely bandwidth-bound — it also spends real time in compute and thread synchronisation. This figure is an assumption, not a fitted constant: no CPU measurement is in the calibration set.
CPU-only inference: no GPU is assumed. A GPU of any size will beat these figures substantially.
Throughput is a memory-bandwidth roofline estimate, not a measurement. It is labelled "estimated" everywhere it appears.
KV cache is computed from this model's published attention configuration.

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