How much VRAM does DeepSeek Coder V2 16B need?

DeepSeek Coder V2 16B (16B parameters) requires approximately 13.4 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek Coder V2 16B?

The recommended quantization for DeepSeek Coder V2 16B is Q4_K_M, which balances quality and memory efficiency.

Can it run?

Yes, RTX 4070 Ti Super 16GB can run DeepSeek Coder V2 16B with a C grade (Tight fit). Expected decode speed: 118.0 tok/s.

CUsable

Tight fit

Using Q4_K_M in Ollama

Capabilities:

Fit status

Tight fit

Decode

118.0 tok/s

TTFT

1640 ms

Safe context

19K

Memory

13.4 GB / 16.0 GB

Weights9.8 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom1.6 GB

Workload	Grade	Fit	Decode	TTFT	Context
Agentic Coding	C	Tight fit	118.0 tok/s	2386 ms	38K
Chat	C	Tight fit	118.0 tok/s	895 ms	10K
Coding	C	Tight fit	118.0 tok/s	1640 ms	19K
RAG	C	Tight fit	118.0 tok/s	2982 ms	38K
Reasoning	C	Tight fit	118.0 tok/s	1938 ms	19K

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on RTX 4070 Ti Super 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	D37
Q3_K_S	3	7.8 GB	Low	D39
NVFP4	4

Ollama

ollama run deepseek-coder-v2-16b

HuggingFace

huggingface-cli download deepseek-coder-v2-16b

Upgrade options

RX 7900 XT 20GBBudget pick

B117.1 tok/s decode

~$899 MSRP

RX 7900 XTX 24GBBest value

C168.6 tok/s decode

~$999 MSRP

RTX A4500 20GBBiggest leap