Can DeepSeek R1 Distill 7B run on RTX 4060 Ti 16GB?

YES — Runs Great

B68Good

Estimated from fit model

DeepSeek R1 Distill 7B needs ~7.9 GB VRAM. RTX 4060 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~54 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) — 7.9 GB, 53.5 tok/s, Runs well

7.9 GB required16.0 GB available

49% VRAM used

Fit status

Runs well

Decode

53.5 tok/s

TTFT

3622 ms

Safe context

33K

Memory

7.9 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 7B on RTX 4060 Ti 16GB
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: 53.5 tok/s decode · 3.6s TTFT (warm) · 134 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	B	Runs well	53.5 tok/s	1975 ms	33K
Coding	B	Runs well	53.5 tok/s	3622 ms	33K
Agentic Coding	B	Runs well	53.5 tok/s	5268 ms	33K
Reasoning	B	Runs well	53.5 tok/s	4280 ms	33K
RAG	B	Runs well	53.5 tok/s	6585 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B64
Q3_K_S	3	3.4 GB	Low	B64
NVFP4	4	3.9 GB	Medium	B65
Q4_K_M	4	4.3 GB	Medium	B65
Q5_K_M	5	5.0 GB	High	B66
Q6_K	6	5.7 GB	High	B66
Q8_0Best for your GPU	8	7.5 GB	Very High	B68
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

Upgrade options

Hardware that runs DeepSeek R1 Distill 7B well

RX 7900 XT 20GBBest value

20 GB VRAM (+4)800 GB/s (+512)

Raises estimated decode speed by about 83%.98 tok/s decode

Raises estimated decode speed by about 83%.

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

~$899 MSRP

RTX A4500 20GBBudget pick

20 GB VRAM (+4)640 GB/s (+352)

Raises estimated decode speed by about 83%.98 tok/s decode

Raises estimated decode speed by about 83%.

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

~$2,000 MSRP

Frequently asked questions

Can RTX 4060 Ti 16GB run DeepSeek R1 Distill 7B?

Yes, RTX 4060 Ti 16GB can run DeepSeek R1 Distill 7B with a B grade (Runs well). Expected decode speed: 53.5 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 7.9 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 4060 Ti 16GB?

On RTX 4060 Ti 16GB, DeepSeek R1 Distill 7B achieves approximately 53.5 tokens per second decode speed with a time-to-first-token of 3622ms using Q4_K_M quantization.

Can RTX 4060 Ti 16GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on RTX 4060 Ti 16GB receives a B grade with 53.5 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, DeepSeek R1 Distill 7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for RTX 4060 Ti 16GB See all hardware for DeepSeek R1 Distill 7B

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<iframe src="https://willitrunai.com/embed/deepseek-r1-distill-qwen-7b-on-rtx-4060-ti-16gb" 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

Runs well

53.5 tok/s

1975 ms

33K

Coding

Runs well

53.5 tok/s

3622 ms

33K

Agentic Coding

Runs well

53.5 tok/s

5268 ms

33K

Reasoning

Runs well

53.5 tok/s

4280 ms

33K

RAG

Runs well

53.5 tok/s

6585 ms

33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX 4060 Ti 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B64
Q3_K_S	3	3.4 GB	Low	B64
NVFP4	4	3.9 GB	Medium	B65
Q4_K_M	4	4.3 GB	Medium	B65
Q5_K_M	5	5.0 GB	High	B66
Q6_K	6	5.7 GB	High	B66
Q8_0Best for your GPU	8	7.5 GB	Very High	B68
F16	16	14.3 GB	Maximum	F0

Frequently asked questions

Can RTX 4060 Ti 16GB run DeepSeek R1 Distill 7B?

Yes, RTX 4060 Ti 16GB can run DeepSeek R1 Distill 7B with a B grade (Runs well). Expected decode speed: 53.5 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 7.9 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 4060 Ti 16GB?

On RTX 4060 Ti 16GB, DeepSeek R1 Distill 7B achieves approximately 53.5 tokens per second decode speed with a time-to-first-token of 3622ms using Q4_K_M quantization.

Can RTX 4060 Ti 16GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on RTX 4060 Ti 16GB receives a B grade with 53.5 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on RTX 4060 Ti 16GB?

On RTX 4060 Ti 16GB, DeepSeek R1 Distill 7B can safely use up to 33K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.