How much VRAM does DeepSeek-V2-Lite-Chat-Uncensored-Unbiased-Reasoner need?

Its published quantizations range from 5.99 GiB to 15.55 GiB in weights alone, before KV cache and runtime overhead.

DeepSeek-V2-Lite-Chat-Uncensored-Unbiased-Reasoner on GeForce RTX 3050

Can I run DeepSeek-V2-Lite-Chat-Uncensored-Unbiased-Reasoner on a GeForce RTX 3050?

Q: Can I run DeepSeek-V2-Lite-Chat-Uncensored-Unbiased-Reasoner on a GeForce RTX 3050?

No. No published quantization of DeepSeek-V2-Lite-Chat-Uncensored-Unbiased-Reasoner fits a GeForce RTX 3050 at any context with q4_0 KV cache.

Not at these settings. No indexed quantization of DeepSeek-V2-Lite-Chat-Uncensored-Unbiased-Reasoner fits GeForce RTX 3050 at any context we compute, with q4_0 KV. The smallest shipped quantization is 5.99 GiB in weights alone, against 5.58 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— 6 GB card, 5.58 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◐
Q8_0	15.55 GiB	16.4	16.4	16.5	16.6	16.9	17.4
Q6_K	13.10 GiB	13.9	14.0	14.0	14.2	14.4	15.0
Q5_K_M	11.03 GiB	11.9	11.9	12.0	12.1	12.4	12.9
Q5_K_S	10.37 GiB	11.2	11.2	11.3	11.4	11.7	12.3
Q4_K_M	9.65 GiB	10.5	10.5	10.6	10.7	11.0	11.5
Q4_K_S	8.88 GiB	9.7	9.8	9.8	10.0	10.2	10.8
IQ4_XS	8.05 GiB	8.9	8.9	9.0	9.1	9.4	9.9
Q3_K_L	7.88 GiB	8.7	8.8	8.8	9.0	9.2	9.8
Q3_K_M	7.57 GiB	8.4	8.4	8.5	8.6	8.9	9.4
Q3_K_S	6.97 GiB	7.8	7.8	7.9	8.0	8.3	8.8
Q2_K	5.99 GiB	6.8	6.9	6.9	7.1	7.3	7.9

●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.