Can DeepSeek R1 Distill Qwen 1.5B run on RTX 5090 32GB?

YES — Runs Great

C42Usable

○Estimated from fit model

DeepSeek R1 Distill Qwen 1.5B needs ~5.2 GB VRAM. RTX 5090 32GB has 32.0 GB. With Q4_K_M quantization, expect ~29 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) — 5.2 GB, 28.5 tok/s, Runs well

5.2 GB required32.0 GB available

16% VRAM used

Fit status

Runs well

Decode

28.5 tok/s

TTFT

6793 ms

Safe context

2.5M

Memory

5.2 GB / 32.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom3.2 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill Qwen 1.5B on RTX 5090 32GB
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: 28.5 tok/s decode · 6.8s TTFT (warm) · 71 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	C	Runs well	28.5 tok/s	3705 ms	2.2M
Coding	C	Runs well	28.5 tok/s	6793 ms	2.5M
Agentic Coding	C	Runs well	28.5 tok/s	9881 ms	2.5M
Reasoning	C	Runs well	28.5 tok/s	8028 ms	2.5M
RAG	C	Runs well	28.5 tok/s	12351 ms	2.5M

Quantization options

How DeepSeek R1 Distill Qwen 1.5B (1.5B params) fits at each quantization level on RTX 5090 32GB (32.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C43
Q3_K_S	3	0.7 GB	Low	C43
NVFP4	4	0.8 GB	Medium	C43
Q4_K_M	4	0.9 GB	Medium	C43
Q5_K_M	5	1.1 GB	High	C43
Q6_K	6	1.2 GB	High	C43
Q8_0	8	1.6 GB	Very High	C43
F16Best for your GPU	16	3.1 GB	Maximum	C43