Can I Run Qwen 3.5 on 8 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2026年2月24日

Yes, but it is tight

Yes, but it is tight — Qwen 3.5 4B at Q8_0 needs about 6 GB of the 6.4 GB usable on 8 GB system RAM, leaving only ~0.4 GB before the runtime starts swapping. Expect ~13.8 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: low · Recommended quantization: Q8_0 · Estimated speed: ~13.8 tok/s

Buy This HardwareIntel Arc B570 10GB — 10 GB VRAM · 150 W board power Deploy in the Cloud NowRTX 4090 on RunPod — from $0.34/hr · rate checked 2026-07

or compare on Vast.ai from $0.35/hr (typical low · varies)

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

8 GB system RAM — what it gives a model

Usable memory for models	6.4 GB
Memory bandwidth	90 GB/s

Qwen 3.5 on 8 GB system RAM: memory by quantization

Quant	Memory needed	Fits 6.4 GB?	Max context	Est. speed	Download
F16	9.7 GB	✗ No	—	—	8 GB
Q8_0	6 GB	✓ Yes	8K	~13.8 tok/s	4.3 GB
Q6_K	5 GB	✓ Yes	16K	~17.3 tok/s	3.3 GB
Q5_K_M	4.5 GB	✓ Yes	16K	~19.5 tok/s	2.8 GB
Q4_K_M	4.1 GB	✓ Yes	16K	~22.3 tok/s	2.4 GB
Q3_K_M	3.4 GB	✓ Yes	32K	~29.1 tok/s	1.7 GB
Q2_K	3 GB	✓ Yes	32K	~35 tok/s	1.3 GB

Which Qwen 3.5 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Qwen 3.5 397B-A17B	244.7 GB	✗ Too large	—
Qwen 3.5 122B-A10B	77.3 GB	✗ Too large	—
Qwen 3.5 35B-A3B	23.8 GB	✗ Too large	—
Qwen 3.5 27B	18.8 GB	✗ Too large	—
Qwen 3.5 9B	7.4 GB	✗ Too large	—
Qwen 3.5 4B	4.1 GB	✓ Fits	~22.3 tok/s
Qwen 3.5 2B	2.7 GB	✓ Fits	~38.9 tok/s
Qwen 3.5 0.8B	1.8 GB	✓ Fits	~74.5 tok/s

What to watch out for

Only ~0.4 GB of headroom at Q8_0: a longer context or a second application can push this into swapping.
5 larger variants of Qwen 3.5 do 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.
6.4 GB of the 8 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.

FAQ