Can I Run StarCoder 2 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

Written by Jakub Rusinowski · Last updated February 28, 2024

Yes

Yes — StarCoder 2 7B at Q6_K needs about 7.2 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) (~0.8 GB spare), at ~29.1 tok/s (estimated), with room for about 16,384 tokens of context.

Confidence: high · Recommended quantization: Q6_K · Estimated speed: ~29.1 tok/s

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RTX 4060 Laptop (8 GB VRAM, 16 GB RAM) — what it gives a model

Usable memory for models	8 GB
Memory bandwidth	256 GB/s
Form factor	Laptop
Operating system	Windows or Linux
Memory upgradeable	Yes
Price	$1,099 (lib/data/laptops.ts (street price), checked 2026-07-06)

StarCoder 2 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM): memory by quantization

Quant	Memory needed	Fits 8 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	✓ Yes	16K	~29.1 tok/s	5.9 GB
Q5_K_M	6.4 GB	✓ Yes	16K	~33.1 tok/s	5.1 GB
Q4_K_M	5.7 GB	✓ Yes	16K	~38 tok/s	4.3 GB
Q3_K_M	4.4 GB	✓ Yes	16K	~50.9 tok/s	3.1 GB
Q2_K	3.7 GB	✓ Yes	16K	~62.6 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	~38 tok/s
StarCoder 2 3B	2.9 GB	✓ Fits	~81.1 tok/s

What to watch out for

Only ~0.8 GB of headroom at Q6_K: 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.

RTX 4060 laptop limitations

8 GB VRAM limits you to 7–8B models at Q4 with a short context.
System RAM is usually upgradeable on this class of laptop even though VRAM is not.

Recommended setup

Ollama or llama.cpp (CUDA/ROCm) — vLLM if you need concurrent serving

How these numbers are calculated

Memory need = quantized weights + KV cache at 8,192 tokens (f16) + 0.8 GB runtime overhead.
8 GB of VRAM on the NVIDIA GeForce RTX 4060 Laptop GPU at 256 GB/s.
16 GB of system RAM available for CPU offload when a model exceeds VRAM.
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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