Can DeepSeek Coder V2 16B run on NVIDIA L20 48GB?

YES — Runs Great

A79Great

▲Estimated — low-sample bucket· few comparable runs

DeepSeek Coder V2 16B needs ~18.8 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~139 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: 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) — 18.8 GB, 138.5 tok/s, Runs well

18.8 GB required48.0 GB available

39% VRAM used

Fit status

Runs well

Decode

138.5 tok/s

TTFT

1398 ms

Safe context

131K

Memory

18.8 GB / 48.0 GB

Memory breakdown

Weights9.8 GB

KV Cache3.3 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsDeepSeek Coder V2 16B on NVIDIA L20 48GB
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: 138.5 tok/s decode · 1.4s TTFT (warm) · 346 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	138.5 tok/s	763 ms	131K
Coding	A	Runs well	138.5 tok/s	1398 ms	131K
Agentic Coding	A	Runs well	138.5 tok/s	2034 ms	131K
Reasoning	A	Runs well	138.5 tok/s	1652 ms	131K
RAG	A	Runs well	138.5 tok/s	2542 ms	131K

Quantization options

How DeepSeek Coder V2 16B (16B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	6.2 GB	Low	A71
Q3_K_S	3	7.8 GB	Low	A71
NVFP4	4	9.0 GB	Medium	A71
Q4_K_M	4	9.8 GB	Medium	A71
Q5_K_M	5	11.5 GB	High	A72
Q6_K	6	13.1 GB	High	A72
Q8_0	8	17.1 GB	Very High	A74
F16Best for your GPU	16	32.8 GB	Maximum	A76

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 L20 48GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen3-Coder 30B A3B Instruct	30.5B	S	68.7 tok/s
👁 Alibaba Qwen 3.5 27B	27B	S	28.6 tok/s
👁 Alibaba Qwen 3.6 27B	27B	S	18.8 tok/s
👁 Alibaba Qwen 3.6 35B A3B	35B	S	85.8 tok/s
👁 Alibaba Qwen3-VL 30B A3B Instruct	30B	S	98.6 tok/s

Frequently asked questions

See all results for NVIDIA L20 48GB See all hardware for DeepSeek Coder V2 16B

URL: https://willitrunai.com/can-run/deepseek-coder-v2-16b-on-l20-48gb

⇱ DeepSeek Coder V2 16B on NVIDIA L20 48GB? YES