Can DeepSeek R1 Distill 7B run on RTX 3050 Ti Laptop 4GB?

Q: What should I upgrade first if DeepSeek R1 Distill 7B feels slow on RTX 3050 Ti Laptop 4GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

DeepSeek R1 Distill 7B needs ~6.7 GB but RTX 3050 Ti Laptop 4GB only has 4.0 GB. Try a smaller quantization or lighter model.

Runtime: OllamaCapacity: No fitBandwidth: Very lowStack: BasicBottleneck: Memory capacity

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) — 6.7 GB, exceeds 4.0 GB available

6.7 GB required4.0 GB available

168% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

9.6 tok/s

TTFT

20232 ms

Safe context

Memory

6.7 GB / 4.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime1.2 GB

Headroom0.4 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsDeepSeek R1 Distill 7B on RTX 3050 Ti Laptop 4GB
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: 9.6 tok/s decode · 20.2s TTFT (warm) · 24 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 6.7 GB, but this setup only exposes 4.0 GB of usable VRAM.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	11.0 tok/s	9612 ms	4K
Coding	F	Too heavy	9.6 tok/s	20232 ms	4K
Agentic Coding	F	Too heavy	7.4 tok/s	37856 ms	4K
Reasoning	F	Too heavy	9.6 tok/s	23911 ms	4K
RAG	F	Too heavy	7.4 tok/s	47320 ms	4K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	F0
Q3_K_S	3	3.4 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs DeepSeek R1 Distill 7B well

GTX 1660 Super 6GBBest value

6 GB VRAM (+2)336 GB/s (+144)

Makes the model fit on the accelerator instead of staying completely out of reach.25 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 160%.

~$229 MSRP

RTX 3050 8GBBudget pick

8 GB VRAM (+4)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.37.6 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

GTX 1060 6GBNVIDIA upgrade

6 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.15.3 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 59%.

~$249 MSRP

RTX 3080 10GBBiggest leap

10 GB VRAM (+6)760 GB/s (+568)

Makes the model fit on the accelerator instead of staying completely out of reach.98 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$699 MSRP

Frequently asked questions

Can RTX 3050 Ti Laptop 4GB run DeepSeek R1 Distill 7B?

No, DeepSeek R1 Distill 7B requires more memory than RTX 3050 Ti Laptop 4GB provides.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 6.7 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 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, DeepSeek R1 Distill 7B achieves approximately 9.6 tokens per second decode speed with a time-to-first-token of 20232ms using Q4_K_M quantization.

Can RTX 3050 Ti Laptop 4GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on RTX 3050 Ti Laptop 4GB receives a F grade with 9.6 tok/s and 4K context.

What context window can DeepSeek R1 Distill 7B use on RTX 3050 Ti Laptop 4GB?

On RTX 3050 Ti Laptop 4GB, DeepSeek R1 Distill 7B can safely use up to 4K tokens of context. The model's official context limit is 33K, but available memory constrains the safe maximum.

See all results for RTX 3050 Ti Laptop 4GB See all hardware for DeepSeek R1 Distill 7B

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