Can I Run IBM Granite 4.1 on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM)?

Superseded model. IBM Granite 4.1 has been superseded by IBM Granite 4.2. This page is kept for reference; the newer family is a better starting point. View IBM Granite 4.2 →

Written by Jakub Rusinowski · Last updated April 29, 2026

Yes, but it is tight

Yes, but it is tight — Granite 4.1 8B at Q5_K_M needs about 7.6 GB of the 8 GB usable on RTX 4060 Laptop (8 GB VRAM, 16 GB RAM), leaving only ~0.4 GB before the runtime starts swapping. Expect ~28.8 tok/s (estimated), with room for about 8,192 tokens of context.

Confidence: low · Recommended quantization: Q5_K_M · Estimated speed: ~28.8 tok/s

Get a personalized upgrade path →

Buy this hardware Intel Arc B570 10GB — 10 GB VRAM · 150 W board power Deploy in the cloud now RTX 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.

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)

IBM Granite 4.1 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	17.9 GB	✗ No	—	—	16 GB
Q8_0	10.4 GB	✗ No	—	—	8.5 GB
Q6_K	8.5 GB	✗ No	—	—	6.6 GB
Q5_K_M	7.6 GB	✓ Yes	8K	~28.8 tok/s	5.7 GB
Q4_K_M	6.8 GB	✓ Yes	16K	~32.9 tok/s	4.8 GB
Q3_K_M	5.4 GB	✓ Yes	16K	~43.5 tok/s	3.4 GB
Q2_K	4.6 GB	✓ Yes	32K	~52.9 tok/s	2.6 GB

Which IBM Granite 4.1 sizes fit

Variant	Needs at Q4_K_M	Fits?	Est. speed
Granite 4.1 30B	20.7 GB	✗ Too large	—
Granite 4.1 8B	6.8 GB	✓ Fits	~32.9 tok/s
Granite 4.1 3B	3.5 GB	✓ Fits	~72.1 tok/s

What to watch out for

Only ~0.4 GB of headroom at Q5_K_M: a longer context or a second application can push this into swapping.
1 larger variant of IBM Granite 4.1 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.

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 inferred from the parameter count because this model does not publish its full attention configuration.

FAQ