Can I Run StarCoder 2 on 8 GB system RAM?

作者： Jakub Rusinowski · 最后更新： 2024年2月28日

Yes

Yes — StarCoder 2 7B at Q4_K_M needs about 5.7 GB of the 6.4 GB usable on 8 GB system RAM (~0.7 GB spare), at ~14.1 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q4_K_M · Estimated speed: ~14.1 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

StarCoder 2 on 8 GB system RAM: memory by quantization

Quant	Memory needed	Fits 6.4 GB?	Max context	Est. speed	Download
F16	15.7 GB	✗ No	—	—	14.4 GB
Q8_0	9 GB	✗ No	—	—	7.7 GB
Q6_K	7.2 GB	✗ No	—	—	5.9 GB
Q5_K_M	6.4 GB	✗ No	—	—	5.1 GB
Q4_K_M	5.7 GB	✓ Yes	16K	~14.1 tok/s	4.3 GB
Q3_K_M	4.4 GB	✓ Yes	16K	~19.3 tok/s	3.1 GB
Q2_K	3.7 GB	✓ Yes	16K	~24.1 tok/s	2.4 GB

Which StarCoder 2 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
StarCoder 2 15B	10.8 GB	✗ Too large	—
StarCoder 2 7B	5.7 GB	✓ Fits	~14.1 tok/s
StarCoder 2 3B	2.9 GB	✓ Fits	~32.2 tok/s

What to watch out for

Only ~0.7 GB of headroom at Q4_K_M: a longer context or a second application can push this into swapping.
1 larger variant of StarCoder 2 does 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.
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 computed from this model's published attention configuration.

FAQ