Can I Run Qwen3-Coder on 96 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2026年7月8日

Yes

Yes — Qwen3-Coder 80B-A3B (MoE) at Q6_K needs about 68.9 GB of the 76.8 GB usable on 96 GB system RAM (~7.9 GB spare), at ~17.4 tok/s (estimated), with room for about 32,768 tokens of context.

Confidence: medium · Recommended quantization: Q6_K · Estimated speed: ~17.4 tok/s

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96 GB system RAM — what it gives a model

Usable memory for models	76.8 GB
Memory bandwidth	90 GB/s

Qwen3-Coder on 96 GB system RAM: memory by quantization

Quant	Memory needed	Fits 76.8 GB?	Max context	Est. speed	Download
F16	163.3 GB	✗ No	—	—	160 GB
Q8_0	88.3 GB	✗ No	—	—	85 GB
Q6_K	68.9 GB	✓ Yes	32K	~17.4 tok/s	65.6 GB
Q5_K_M	60 GB	✓ Yes	32K	~19.1 tok/s	56.7 GB
Q4_K_M	51.6 GB	✓ Yes	64K	~21 tok/s	48.3 GB
Q3_K_M	37.4 GB	✓ Yes	64K	~25.2 tok/s	34.1 GB
Q2_K	29.6 GB	✓ Yes	64K	~28.3 tok/s	26.3 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 80B-A3B (MoE)	51.6 GB	✓ Fits	~21 tok/s
Qwen3-Coder 8B	6.8 GB	✓ Fits	~12.1 tok/s

What to watch out for

1 larger variant of Qwen3-Coder does not fit and would need CPU offload or different hardware.
This model does not publish its full attention configuration, so the KV-cache share of these figures is inferred from its parameter count rather than computed exactly.
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.
76.8 GB of the 96 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 inferred from the parameter count because this model does not publish its full attention configuration.

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