Can DeepSeek R1 Distill 14B run on NVIDIA L20 48GB?

YES — Runs Great

A74Great

○Estimated from fit model

DeepSeek R1 Distill 14B needs ~17.2 GB VRAM. NVIDIA L20 48GB has 48.0 GB. With Q4_K_M quantization, expect ~84 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) — 17.2 GB, 83.8 tok/s, Runs well

17.2 GB required48.0 GB available

36% VRAM used

Fit status

Runs well

Decode

83.8 tok/s

TTFT

2312 ms

Safe context

33K

Memory

17.2 GB / 48.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom4.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 14B on NVIDIA L20 48GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 83.8 tok/s decode · 2.3s TTFT (warm) · 209 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	83.8 tok/s	1261 ms	33K
Coding	A	Runs well	83.8 tok/s	2312 ms	33K
Agentic Coding	A	Runs well	83.8 tok/s	3362 ms	33K
Reasoning	A	Runs well	83.8 tok/s	2732 ms	33K
RAG	A	Runs well	83.8 tok/s	4203 ms	33K

Quantization options

How DeepSeek R1 Distill 14B (14B params) fits at each quantization level on NVIDIA L20 48GB (48.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B66
Q3_K_S	3	6.9 GB	Low	B67
NVFP4	4	7.8 GB	Medium	B67
Q4_K_M	4	8.5 GB	Medium	B67
Q5_K_M	5	10.1 GB	High	B67
Q6_K	6	11.5 GB	High	B68
Q8_0	8	15.0 GB	Very High	B69
F16Best for your GPU	16	28.7 GB	Maximum	A73

Get started

Copy-paste commands to run DeepSeek R1 Distill 14B on your machine.

Run

ollama run deepseek-r1

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 R1 Distill 14B

URL: https://willitrunai.com/can-run/deepseek-r1-distill-14b-on-l20-48gb

⇱ DeepSeek R1 Distill 14B on NVIDIA L20 48GB? YES