Can DeepSeek R1 Distill 7B run on RTX 4060 8GB?

YES — Tight Fit

B69Good

Estimated from fit model

DeepSeek R1 Distill 7B needs ~7.1 GB VRAM. RTX 4060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~51 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, 50.5 tok/s, Tight fit

7.1 GB required8.0 GB available

89% VRAM used

Fit status

Tight fit

Decode

50.5 tok/s

TTFT

3835 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 4060 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: 50.5 tok/s decode · 3.8s TTFT (warm) · 126 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	Tight fit	50.5 tok/s	2092 ms	32K
Coding	B	Tight fit	50.5 tok/s	3835 ms	32K
Agentic Coding	B	Runs with offload	50.5 tok/s	5578 ms	32K
Reasoning	B	Tight fit	50.5 tok/s	4532 ms	32K
RAG	B	Runs with offload	50.5 tok/s	6972 ms	32K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RTX 4060 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

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

RTX 3060 12GBBudget pick

12 GB VRAM (+4)360 GB/s (+88)

Adds memory headroom for longer context windows and future model growth.60.4 tok/s decode

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

~$329 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+400)

Raises estimated decode speed by about 94%.98 tok/s decode

Raises estimated decode speed by about 94%.

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

~$549 MSRP

RTX 4070 12GBNVIDIA upgrade

12 GB VRAM (+4)504 GB/s (+232)

Raises estimated decode speed by about 90%.96.1 tok/s decode

Raises estimated decode speed by about 90%.

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

~$599 MSRP

Frequently asked questions

Can RTX 4060 8GB run DeepSeek R1 Distill 7B?

Yes, RTX 4060 8GB can run DeepSeek R1 Distill 7B with a B grade (Tight fit). Expected decode speed: 50.5 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 4060 8GB?

On RTX 4060 8GB, DeepSeek R1 Distill 7B achieves approximately 50.5 tokens per second decode speed with a time-to-first-token of 3835ms using Q4_K_M quantization.

Can RTX 4060 8GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on RTX 4060 8GB receives a B grade with 50.5 tok/s and 32K context.

What context window can DeepSeek R1 Distill 7B use on RTX 4060 8GB?

On RTX 4060 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 4060 8GB See all hardware for DeepSeek R1 Distill 7B

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