How much VRAM does Kimi-K3 need?

Its published quantizations range from 528.03 GiB to 864.81 GiB in weights alone, before KV cache and runtime overhead.

Can I run Kimi-K3 on a GeForce RTX 3080 Ti?

Q: Can I run Kimi-K3 on a GeForce RTX 3080 Ti?

No. No published quantization of Kimi-K3 fits a GeForce RTX 3080 Ti at any context with q8_0 KV cache.

Not at these settings. No indexed quantization of Kimi-K3 fits GeForce RTX 3080 Ti at any context we compute, with q8_0 KV. The smallest shipped quantization is 528.03 GiB in weights alone, against 18.60 GiB usable. CPU offload can still run it, slowly.

From the file· weights summed from filesFrom the file· KV computed per layerPredicted· speed and compute buffer

KV cache dtypef16 q8_0 q4_0— 20 GB card, 18.60 GiB usable after overhead

Every quantization at every context

total memory required; green fits, red does not

Quant	Weights●	4K◐	8K◐	16K◐	32K◐	64K◐	128K◐
Q2_K	864.81 GiB	866.0	866.2	866.6	867.5	869.2	872.7
IQ1_S	528.03 GiB	529.2	529.4	529.8	530.7	532.4	535.9

●From the file◐Predictedwhat these mean

Figures are GiB of total memory: weights plus KV cache plus compute buffer and backend overhead. Weights and KV are near-exact; the overhead term is modeled. Hover any cell for the breakdown.

Why other calculators disagree

A parameters × bits ÷ 8 estimate ignores two things that dominate at long context. First, the weights themselves are not the nominal rate — quantizations are mixtures, so the real file is consistently larger than the label implies. Second, this model uses latent attention and allocates no V cache at all, so any formula reading num_key_value_heads overstates its cache by more than an order of magnitude.