How much VRAM does DeepSeek-R1-Distill-Qwen-1.5B need?

Q4_K_M is exactly 1,117,320,512 bytes (1.04 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is DeepSeek-R1-Distill-Qwen-1.5B's KV cache?

0.88 GiB at 32K context with an f16 cache, computed per layer from the model's architecture. Quantizing the cache to q8_0 roughly halves it.

deepseek-ai · text

DeepSeek-R1-Distill-Qwen-1.5B

deepseek-ai/DeepSeek-R1-Distill-Qwen-1.5B

DeepSeek-R1-Distill-Qwen-1.5B at Q4_K_M is exactly 1,117,320,512 bytes (1.04 GiB / 1.12 GB) — an effective 5.030 bits per weight, not the nominal 4. Its KV cache at 32K is 0.88 GiB.

From the file· summed from 1 file(s)From the file· KV per layer

Parameters

1.8B

Architecture

qwen2

28 layers

Context

131,072

native (config.json)

License

apache-2.0

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
UD-IQ1_S	0.65 GiB	700,140,640	3.152	—	unsloth
IQ2_M	0.65 GiB	701,332,064	3.157	—	bartowski
UD-IQ1_M	0.67 GiB	716,268,640	3.224	—	unsloth
Q2_K	0.70 GiB	752,880,224	3.389	—	bartowski
Q2_K	0.70 GiB	752,880,736	3.389	—	unsloth
UD-IQ2_XXS	0.72 GiB	774,946,912	3.489	—	unsloth
UD-IQ2_M	0.74 GiB	792,150,112	3.566	—	unsloth
Q2_K_L	0.75 GiB	807,577,696	3.635	—	unsloth
IQ3_XS	0.77 GiB	831,976,544	3.745	—	bartowski
Q3_K_S	0.80 GiB	861,221,984	3.877	—	bartowski
IQ3_M	0.82 GiB	876,941,408	3.948	—	bartowski
UD-IQ3_XXS	0.82 GiB	882,082,912	3.971	—	unsloth
Q3_K_M	0.86 GiB	924,456,032	4.162	339	bartowski
Q3_K_M	0.86 GiB	924,456,544	4.162	339	unsloth
Q3_K_L	0.91 GiB	980,439,872	4.414	—	lmstudio-community
Q3_K_L	0.91 GiB	980,440,160	4.414	—	bartowski
Q2_K_L	0.91 GiB	980,784,224	4.415	—	bartowski
IQ4_XS	0.95 GiB	1,019,711,072	4.590	339	bartowski
IQ4_NL	0.99 GiB	1,067,603,552	4.806	—	bartowski
Q4_0	1.00 GiB	1,068,807,776	4.811	339	bartowski
Q4_K_S	1.00 GiB	1,071,584,864	4.824	—	bartowski
UD-IQ4_XS	1.02 GiB	1,098,800,224	4.947	—	unsloth
Q4_K_M	1.04 GiB	1,117,320,512	5.030	339	lmstudio-community
Q4_K_M	1.04 GiB	1,117,320,800	5.030	339	bartowski
Q4_K_M	1.04 GiB	1,117,321,312	5.030	—	unsloth
Q4_1	1.08 GiB	1,162,700,384	5.234	—	bartowski
Q5_K_S	1.17 GiB	1,259,173,472	5.668	—	bartowski
Q5_K_M	1.20 GiB	1,285,494,368	5.787	339	bartowski
Q5_K_M	1.20 GiB	1,285,494,880	5.787	—	unsloth
Q4_K_L	1.20 GiB	1,290,527,840	5.810	—	bartowski
Q5_K_L	1.33 GiB	1,429,529,696	6.435	—	bartowski
Q6_K	1.36 GiB	1,464,178,496	6.591	339	lmstudio-community
Q6_K	1.36 GiB	1,464,178,784	6.591	339	bartowski
Q6_K	1.36 GiB	1,464,179,296	6.591	—	unsloth
Q6_K_L	1.47 GiB	1,577,219,168	7.100	—	bartowski
Q8_0	1.76 GiB	1,894,531,904	8.529	—	lmstudio-community
Q8_0	1.76 GiB	1,894,532,192	8.529	339	bartowski
Q8_0	1.76 GiB	1,894,532,416	8.529	339	unsloth
F16	3.32 GiB	3,560,416,352	16.028	339	bartowski
BF16	3.32 GiB	3,560,416,576	16.028	—	unsloth

KV cache by context

computed per layer

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	0.11 GiB	0.11 GiB	—	28 / 0 / 0
8,192	0.22 GiB	0.22 GiB	—	28 / 0 / 0
16,384	0.44 GiB	0.44 GiB	—	28 / 0 / 0
32,768	0.88 GiB	0.88 GiB	—	28 / 0 / 0
65,536	1.75 GiB	1.75 GiB	—	28 / 0 / 0
131,072	3.50 GiB	3.50 GiB	—	28 / 0 / 0

Compare with

same modality, comparable size

vs Qwen3-4B vs HyperCLOVAX-SEED-Text-Instruct-1.5B vs Llama-3.2-1B-Instruct vs ced-base vs gemma-3-1b-it vs Qwen3-1.7B All alternatives →Will it fit on my card? →

Why other calculators give a different number

A parameters × bits ÷ 8 estimate puts Q4_K_M at roughly 0.93 GiB. The real file is 1.04 GiB, because a quantization is a mixture and some tensors are always kept at higher precision.

Architecture

from config.json

Layers

Attention heads

KV heads

Head dim

128

Hidden size

1536

Vocab

151,936

Sliding window

4096

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

false

This model declares a sliding window but sets use_sliding_window: false, so the window is not applied. Honouring the field without the flag understates KV for the whole family.

Questions people ask

How much VRAM does DeepSeek-R1-Distill-Qwen-1.5B need?: Q4_K_M is exactly 1,117,320,512 bytes (1.04 GiB) in weights. Add the KV cache, which depends on your context length, plus roughly half a gigabyte of runtime overhead.
How large is DeepSeek-R1-Distill-Qwen-1.5B's KV cache?: 0.88 GiB at 32K context with an f16 cache, computed per layer. Quantizing the cache to q8_0 roughly halves it, which is often the difference between a context length fitting and not.
Which quantization of DeepSeek-R1-Distill-Qwen-1.5B should I use?: Q4_K_M is the usual default. Pick the largest quantization that fits your card at the context you actually need — the table above gives exact sizes for every one published.