Can DeepSeek R1 Distill 7B run on RTX 3060 Ti 8GB?

YES — Tight Fit

A70Great

○Estimated from fit model

DeepSeek R1 Distill 7B needs ~7.1 GB VRAM. RTX 3060 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~78 tok/s.

Runtime: OllamaCapacity: TightBandwidth: 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) — 7.1 GB, 77.5 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

77.5 tok/s

TTFT

2499 ms

Safe context

32K

Memory

7.1 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 7B on RTX 3060 Ti 8GB
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: 77.5 tok/s decode · 2.5s TTFT (warm) · 194 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	Tight fit	77.5 tok/s	1363 ms	32K
Coding	A	Tight fit	77.5 tok/s	2499 ms	32K
Agentic Coding	A	Runs with offload	77.5 tok/s	3635 ms	32K
Reasoning	A	Tight fit	77.5 tok/s	2954 ms	32K
RAG	A	Runs with offload	77.5 tok/s	4544 ms	32K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX 3060 Ti 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A70
Q3_K_S	3	3.4 GB	Low	A71
NVFP4	4	3.9 GB	Medium	A70
Q4_K_M	4	4.3 GB	Medium	A70
Q5_K_MBest for your GPU	5	5.0 GB	High	B70
Q6_K	6	5.7 GB	High	F0
Q8_0	8	7.5 GB	Very High	F0
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run DeepSeek R1 Distill 7B on your machine.

Run

ollama run deepseek-r1:7b

Your hardware

More models your RTX 3060 Ti 8GB can run

Model	Params	Grade	Decode
👁 Alibaba Qwen 3 8B	8B	A	38.6 tok/s
👁 NVIDIA Nemotron Nano 8B	8B	A	40.9 tok/s
👁 InternLM InternVL2 8B	8B	A	40.9 tok/s
👁 Mistral Ministral 3 8B	8B	A	38.6 tok/s
👁 OpenBMB MiniCPM-V 2.6 8B	8B	A	40.9 tok/s

Frequently asked questions

See all results for RTX 3060 Ti 8GB See all hardware for DeepSeek R1 Distill 7B

URL: https://willitrunai.com/can-run/deepseek-r1-distill-qwen-7b-on-rtx-3060-ti-8gb

⇱ DeepSeek R1 Distill 7B on RTX 3060 Ti 8GB? TIGHT FIT