Llama 3.3 70BonRTX 5090
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MHA model · 8 attention heads · 8k context · llama.cpp
estimated
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.
KV cache always stored in FP16 (2 bytes). GQA reduces KV cache size by 1.0× vs standard MHA.
Confidence
MediumStandard 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.
Need 9.0 GiB more. Don't compromise your model with heavier quantization.
What can I run instead?
Llama 3.3 70B Q4_K_M doesn't fit on RTX 5090 at 8k context. Here are viable alternatives:
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