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
Qwen 3 8B	8B	A	38.6 tok/s
Nemotron Nano 8B	8B	A	40.9 tok/s
InternVL2 8B	8B	A	40.9 tok/s
Ministral 3 8B	8B	A	38.6 tok/s
MiniCPM-V 2.6 8B	8B	A	40.9 tok/s

Frequently asked questions

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

Yes, RTX 3060 Ti 8GB can run DeepSeek R1 Distill 7B with a A grade (Tight fit). Expected decode speed: 77.5 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 7B?

The recommended quantization for DeepSeek R1 Distill 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 7B run at on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, DeepSeek R1 Distill 7B achieves approximately 77.5 tokens per second decode speed with a time-to-first-token of 2499ms using Q4_K_M quantization.

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

For coding workloads, DeepSeek R1 Distill 7B on RTX 3060 Ti 8GB receives a A grade with 77.5 tok/s and 32K context.

What context window can DeepSeek R1 Distill 7B use on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, DeepSeek R1 Distill 7B can safely use up to 32K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

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

Embed this result▼

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<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-qwen-7b-on-rtx-3060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

77.5 tok/s

1363 ms

32K

Coding

Tight fit

77.5 tok/s

2499 ms

32K

Agentic Coding

Runs with offload

77.5 tok/s

3635 ms

32K

Reasoning

Tight fit

77.5 tok/s

2954 ms

32K

RAG

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

Model

Params

Grade

Decode

Capabilities

38.6 tok/s

40.9 tok/s

40.9 tok/s

38.6 tok/s

40.9 tok/s

Frequently asked questions

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

Yes, RTX 3060 Ti 8GB can run DeepSeek R1 Distill 7B with a A grade (Tight fit). Expected decode speed: 77.5 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 7.1 GB of memory with Q4_K_M quantization.

What is the best quantization for DeepSeek R1 Distill 7B?

The recommended quantization for DeepSeek R1 Distill 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will DeepSeek R1 Distill 7B run at on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, DeepSeek R1 Distill 7B achieves approximately 77.5 tokens per second decode speed with a time-to-first-token of 2499ms using Q4_K_M quantization.

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

For coding workloads, DeepSeek R1 Distill 7B on RTX 3060 Ti 8GB receives a A grade with 77.5 tok/s and 32K context.

What context window can DeepSeek R1 Distill 7B use on RTX 3060 Ti 8GB?

On RTX 3060 Ti 8GB, DeepSeek R1 Distill 7B can safely use up to 32K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.