Unverified50% confidenceFactExact time
When a quantized 8B model (such as llama3:8b) runs inference on GPU, VRAM usage is approximately 4.5–5.5 GiB.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/18/2026
First Seen
Valid until: 1/16/2027
Sources
Related Entities
Related Claims
UnverifiedFull-parameter fine-tuning typically requires GPU memory 3–5 times the model's parameter count.74% similarUnverified7B–9B parameter models typically occupy 5–7 GB of VRAM after Q4 or Q5 quantization.70% similarUnverifiedGPTQ (2022) achieved the first usable performance of large models at INT4 by quantizing layer by layer and minimizing quantization error using Hessian information.68% similarUnverifiedIn practice, 4-bit to 8-bit quantized versions perform nearly identically to the original model on most tasks.65% similarUnverifiedOn the math reasoning task GSM8K, OQ4E answered 954 questions correctly (95.4%), while 8-bit only answered 924 correctly (92.4%), with the 4-bit version coming out 3 percentage points higher.64% similar
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