Can DeepSeek Coder V2 16B run on NVIDIA T4 16GB?

YES — With Offload

A81Great

○Estimated from fit model

DeepSeek Coder V2 16B needs ~15.9 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~51 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: Balanced

Operating mode

Choose the run profile you care about

Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.

Current mode

Balanced

Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 15.9 GB, 50.7 tok/s, Runs with offload

15.9 GB required16.0 GB available

99% VRAM used

Fit status

Runs with offload

Decode

50.7 tok/s

TTFT

3815 ms

Safe context

17K

Memory

15.9 GB / 16.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on NVIDIA T4 16GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:

def quicksort(arr):
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]

The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
 """Sort a list using the quicksort algorithm.

 Args:
 arr: List of integers to sort.

 Returns:
 A new sorted list.
 """
 if len(arr) <= 1:
 return arr
 pivot = arr[len(arr) // 2]
 left = [x for x in arr if x < pivot]
 middle = [x for x in arr if x == pivot]
 right = [x for x in arr if x > pivot]
 return quicksort(left) + middle + quicksort(right)

Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 50.7 tok/s decode · 3.8s TTFT (warm) · 127 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Tight fit	50.7 tok/s	2081 ms	17K
Coding	A	Runs with offload	50.7 tok/s	3815 ms	17K
Agentic Coding	B	Very compromised (needs ~1.6 GB host RAM)	24.9 tok/s	11292 ms	17K
Reasoning	A	Runs with offload	50.7 tok/s	4509 ms	17K
RAG	B	Very compromised (needs ~1.6 GB host RAM)	24.9 tok/s	14115 ms

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A79
Q3_K_S	3	7.8 GB	Low	A80
NVFP4	4

Get started

Copy-paste commands to run DeepSeek Coder V2 16B on your machine.

Run

lms load DeepSeek-Coder-V2-Lite-Instruct && lms server start

Your hardware

More models your NVIDIA T4 16GB can run

Model	Params	Grade	Decode	Capabilities
👁 OpenAI GPT-OSS 20B	21B	A	22.3 tok/s
👁 Mistral Codestral 2 25.08	22B	A

Frequently asked questions

See all results for NVIDIA T4 16GB See all hardware for DeepSeek Coder V2 16B

URL: https://willitrunai.com/can-run/deepseek-coder-v2-16b-on-t4-16gb

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