Qwen 2.5 7BonRTX 3060

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MHA model · 8 attention heads · 8k context · llama.cpp

6.3 GiB

+5.7 GiB headroom

estimated

Model

Qwen 2.5 7B

7B params · Dense · 28 layers

GPU

RTX 3060

12GB VRAM · NVIDIA

Entry-level GPU

VRAM by Quantization

Calculated estimate using: Weights + KV Cache (GQA-aware) + Activations + Engine Overhead. These are theoretical calculations — actual VRAM usage varies by runtime and batch size.

FP16 (Full precision)2 bpw

15.7 GiB— OOM

Q8_0 (High quality)1 bpw

9.2 GiB— fits

Q5_K_M (Balanced)0.6875 bpw

7.2 GiB— fits

Q4_K_M (Efficient)0.5625 bpw

6.3 GiB— fits

IQ4_XS (Ultra efficient)0.53 bpw

6.1 GiB— fits

KV cache always stored in FP16 (2 bytes). GQA reduces KV cache size by 1.0× vs standard MHA.

Confidence

Medium

Standard inference workload estimated with llama.cpp engine overhead at 0.8GB. Variance depends on batch size, exact context used, and runtime implementation. This is a calculated estimate, not a measured runtime value.

Deploy a GPU API

Your GPU fits the model. Going cloud? Use managed GPU to avoid infra headaches.

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Other combinations:

Llama 3.3 70B · RTX 3090 Llama 3.3 70B · RTX 4090 Llama 3.3 70B · RTX 5090 Qwen 2.5 72B · RTX 3090 VRAM Calculator