Can DeepSeek R1 Distill 14B run on RTX 5000 Ada Laptop 16GB?

YES — Tight Fit

A76Great

Estimated from fit model

DeepSeek R1 Distill 14B needs ~14.0 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: 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) — 14.0 GB, 55.8 tok/s, Tight fit

14.0 GB required16.0 GB available

88% VRAM used

Fit status

Tight fit

Decode

55.8 tok/s

TTFT

3467 ms

Safe context

27K

Memory

14.0 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 14B on RTX 5000 Ada Laptop 16GB
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: 55.8 tok/s decode · 3.5s TTFT (warm) · 140 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	55.8 tok/s	1891 ms	27K
Coding	A	Tight fit	49.2 tok/s	3932 ms	27K
Agentic Coding	B	Runs with offload (needs ~0.5 GB host RAM)	37.3 tok/s	7545 ms	27K
Reasoning	A	Tight fit	55.8 tok/s	4098 ms	27K
RAG	B	Runs with offload (needs ~0.5 GB host RAM)	37.3 tok/s	9431 ms

Quantization options

How DeepSeek R1 Distill 14B (14B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	A74
Q3_K_S	3	6.9 GB	Low	A75
NVFP4	4

Get started

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

Run

ollama run deepseek-r1

Your hardware

More models your RTX 5000 Ada Laptop 16GB can run

Model	Params	Grade	Decode	Capabilities
Phi-4-reasoning-plus 14B	14.7B	S	52 tok/s
GPT-OSS 20B	21B	A	43.8 tok/s

Frequently asked questions

Can RTX 5000 Ada Laptop 16GB run DeepSeek R1 Distill 14B?

Yes, RTX 5000 Ada Laptop 16GB can run DeepSeek R1 Distill 14B with a A grade (Tight fit). Expected decode speed: 49.2 tok/s.

How much VRAM does DeepSeek R1 Distill 14B need?

DeepSeek R1 Distill 14B (14B parameters) requires approximately 14.0 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 14B?

The recommended quantization for DeepSeek R1 Distill 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 14B run at on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, DeepSeek R1 Distill 14B achieves approximately 49.2 tokens per second decode speed with a time-to-first-token of 3932ms using Q4_K_M quantization.

Can RTX 5000 Ada Laptop 16GB run DeepSeek R1 Distill 14B for coding?

For coding workloads, DeepSeek R1 Distill 14B on RTX 5000 Ada Laptop 16GB receives a A grade with 49.2 tok/s and 27K context.

What context window can DeepSeek R1 Distill 14B use on RTX 5000 Ada Laptop 16GB?

On RTX 5000 Ada Laptop 16GB, DeepSeek R1 Distill 14B can safely use up to 27K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for RTX 5000 Ada Laptop 16GB See all hardware for DeepSeek R1 Distill 14B

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