How much VRAM does deepseek-llm-7b-chat need?

Q4_K_M is exactly 4,223,768,032 bytes (3.93 GiB) in weights. Add the KV cache, which depends on context length, plus roughly half a gigabyte of runtime overhead.

How large is deepseek-llm-7b-chat's KV cache?

15.00 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-llm-7b-chat

deepseek-ai/deepseek-llm-7b-chat

deepseek-llm-7b-chat at Q4_K_M is exactly 4,223,768,032 bytes (3.93 GiB / 4.22 GB) — an effective 4.890 bits per weight, not the nominal 4. Its KV cache at 32K is 15.00 GiB.

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

Parameters

6.9B

Architecture

llama

30 layers

Context

4,096

native (config.json)

License

other

Shipped quantizations

● exact bytes, summed from published files

Quant	Size●	Exact bytes●	Effective bpw●	Tensors●	Publisher
Q2_K	2.53 GiB	2,718,832,096	3.147	—	second-state
Q2_K	2.79 GiB	2,993,594,144	3.466	—	TheBloke
Q3_K_S	2.92 GiB	3,138,426,336	3.633	—	second-state
Q3_K_S	2.92 GiB	3,138,428,704	3.633	—	TheBloke
Q3_K_M	3.22 GiB	3,461,600,736	4.007	—	second-state
Q3_K_M	3.23 GiB	3,467,239,200	4.014	—	TheBloke
Q3_K_L	3.49 GiB	3,746,682,336	4.338	—	second-state
Q3_K_L	3.49 GiB	3,746,684,704	4.338	—	TheBloke
Q4_0	3.73 GiB	4,000,470,496	4.631	—	second-state
Q4_0	3.73 GiB	4,000,472,864	4.631	—	TheBloke
Q4_K_S	3.75 GiB	4,025,767,392	4.661	—	second-state
Q4_K_S	3.75 GiB	4,031,405,856	4.667	—	TheBloke
Q4_K_M	3.93 GiB	4,223,768,032	4.890	—	second-state
Q4_K_M	3.93 GiB	4,223,770,400	4.890	—	TheBloke
Q5_K_S	4.48 GiB	4,811,806,176	5.571	—	second-state
Q5_0	4.48 GiB	4,811,806,176	5.571	—	second-state
Q5_0	4.48 GiB	4,811,808,544	5.571	—	TheBloke
Q5_K_S	4.48 GiB	4,811,808,544	5.571	—	TheBloke
Q5_K_M	4.59 GiB	4,926,838,240	5.704	—	second-state
Q5_K_M	4.59 GiB	4,926,840,608	5.704	—	TheBloke
Q6_K	5.28 GiB	5,673,850,336	6.569	—	second-state
Q6_K	5.28 GiB	5,673,852,704	6.569	—	TheBloke
Q8_0	6.84 GiB	7,347,394,016	8.506	—	second-state
Q8_0	6.84 GiB	7,347,396,384	8.506	—	TheBloke

KV cache by context

computed per layer

Context	KV cache (f16)●	Flat formula	Overstated by	Full / windowed / recurrent
4,096	1.88 GiB	1.88 GiB	—	30 / 0 / 0
8,192	3.75 GiB	3.75 GiB	—	30 / 0 / 0
16,384	7.50 GiB	7.50 GiB	—	30 / 0 / 0
32,768	15.00 GiB	15.00 GiB	—	30 / 0 / 0
65,536	30.00 GiB	30.00 GiB	—	30 / 0 / 0
131,072	60.00 GiB	60.00 GiB	—	30 / 0 / 0

Compare with

same modality, comparable size

vs gemma-4-E4B-it vs Qwen3-4B vs Qwen3-8B vs HyperCLOVAX-SEED-Text-Instruct-1.5B vs Llama-3.2-1B-Instruct vs llama-3-youko-8b 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 3.62 GiB. The real file is 3.93 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

4096

Vocab

102,400

Sliding window

none

SWA period

—

MLA

Experts

—

Experts per token

—

use_sliding_window

—

Questions people ask

How much VRAM does deepseek-llm-7b-chat need?: Q4_K_M is exactly 4,223,768,032 bytes (3.93 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-llm-7b-chat's KV cache?: 15.00 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-llm-7b-chat 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.