Can DeepSeek R1 Distill 7B run on RX 9060 8GB?

YES — Tight Fit

B68Good

Estimated from fit model

DeepSeek R1 Distill 7B needs ~6.8 GB VRAM. RX 9060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~43 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: 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) — 6.8 GB, 46.1 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

46.1 tok/s

TTFT

4196 ms

Safe context

33K

Memory

6.8 GB / 8.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsDeepSeek R1 Distill 7B on RX 9060 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: 46.1 tok/s decode · 4.2s TTFT (warm) · 115 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	Runs well	42.5 tok/s	2485 ms	33K
Coding	B	Tight fit	42.5 tok/s	4556 ms	33K
Agentic Coding	B	Runs with offload	42.5 tok/s	6627 ms	33K
Reasoning	B	Tight fit	42.5 tok/s	5385 ms	33K
RAG	B	Runs with offload	42.5 tok/s	8284 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RX 9060 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

RX 7700 XT 12GBBudget pick

12 GB VRAM (+4)432 GB/s (+144)

Raises estimated decode speed by about 43%.65.9 tok/s decode

Raises estimated decode speed by about 43%.

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

~$449 MSRP

RX 6700 XT 12GBBest value

12 GB VRAM (+4)384 GB/s (+96)

Adds memory headroom for longer context windows and future model growth.50.8 tok/s decode

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

~$479 MSRP

RX 9070 16GBAMD upgrade

16 GB VRAM (+8)640 GB/s (+352)

Raises estimated decode speed by about 113%.98 tok/s decode

Raises estimated decode speed by about 113%.

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

~$479 MSRP

Frequently asked questions

Can RX 9060 8GB run DeepSeek R1 Distill 7B?

Yes, RX 9060 8GB can run DeepSeek R1 Distill 7B with a B grade (Tight fit). Expected decode speed: 42.5 tok/s.

How much VRAM does DeepSeek R1 Distill 7B need?

DeepSeek R1 Distill 7B (7B parameters) requires approximately 6.8 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 RX 9060 8GB?

On RX 9060 8GB, DeepSeek R1 Distill 7B achieves approximately 42.5 tokens per second decode speed with a time-to-first-token of 4556ms using Q4_K_M quantization.

Can RX 9060 8GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on RX 9060 8GB receives a B grade with 42.5 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on RX 9060 8GB?

On RX 9060 8GB, 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 RX 9060 8GB See all hardware for DeepSeek R1 Distill 7B

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