Can DeepSeek R1 Distill 8B run on NVIDIA T4 16GB?

YES — Runs Great

B69Good

○Estimated from fit model

DeepSeek R1 Distill 8B needs ~9.6 GB VRAM. NVIDIA T4 16GB has 16.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: 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) — 9.6 GB, 45.8 tok/s, Runs well

9.6 GB required16.0 GB available

60% VRAM used

Fit status

Runs well

Decode

45.8 tok/s

TTFT

4225 ms

Safe context

33K

Memory

9.6 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 8B on NVIDIA T4 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: 45.8 tok/s decode · 4.2s TTFT (warm) · 115 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	45.8 tok/s	2305 ms	33K
Coding	B	Runs well	42.6 tok/s	4542 ms	33K
Agentic Coding	A	Runs well	45.8 tok/s	6146 ms	33K
Reasoning	B	Runs well	45.8 tok/s	4993 ms	33K
RAG	A	Runs well	45.8 tok/s	7682 ms	33K

Quantization options

How DeepSeek R1 Distill 8B (8B params) fits at each quantization level on NVIDIA T4 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B64
Q3_K_S	3	3.9 GB	Low	B64
NVFP4	4

Get started

Copy-paste commands to run DeepSeek R1 Distill 8B on your machine.

Run

ollama run deepseek-r1:8b

Upgrade options

Hardware that runs DeepSeek R1 Distill 8B well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+480)

Raises estimated decode speed by about 131%.105.7 tok/s decode

Raises estimated decode speed by about 131%.

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

👁 NVIDIA
RTX A4500 20GBBudget pick

20 GB VRAM (+4)640 GB/s (+320)

Raises estimated decode speed by about 140%.110 tok/s decode

Raises estimated decode speed by about 140%.