Can DeepSeek R1 Distill 7B run on RX 580 8GB?

YES — Tight Fit

B67Good

Estimated from fit model

DeepSeek R1 Distill 7B needs ~6.8 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 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, 28.0 tok/s, Tight fit

6.8 GB required8.0 GB available

85% VRAM used

Fit status

Tight fit

Decode

28.0 tok/s

TTFT

6917 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 580 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: 28.0 tok/s decode · 6.9s TTFT (warm) · 70 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	28.0 tok/s	3773 ms	33K
Coding	B	Tight fit	28.0 tok/s	6917 ms	33K
Agentic Coding	B	Runs with offload	28.0 tok/s	10061 ms	33K
Reasoning	B	Tight fit	28.0 tok/s	8174 ms	33K
RAG	B	Runs with offload	28.0 tok/s	12576 ms	33K

Quantization options

How DeepSeek R1 Distill 7B (7B params) fits at each quantization level on RX 580 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

Opções de upgrade

Hardware que roda bem DeepSeek R1 Distill 7B

RX 9060 XT 16GBOpção econômica

16 GB VRAM (+8)320 GB/s (+64)

Raises estimated decode speed by about 83%.51.3 tok/s decodificação

Raises estimated decode speed by about 83%.

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

~$349 MSRP

RX 7700 XT 12GBMelhor custo-benefício

12 GB VRAM (+4)432 GB/s (+176)

Raises estimated decode speed by about 135%.65.9 tok/s decodificação

Raises estimated decode speed by about 135%.

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

~$449 MSRP

RX 6700 XT 12GBUpgrade AMD

12 GB VRAM (+4)384 GB/s (+128)

Raises estimated decode speed by about 81%.50.8 tok/s decodificação

Raises estimated decode speed by about 81%.

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

~$479 MSRP

Frequently asked questions

Can RX 580 8GB run DeepSeek R1 Distill 7B?

Yes, RX 580 8GB can run DeepSeek R1 Distill 7B with a B grade (Tight fit). Expected decode speed: 28.0 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 580 8GB?

On RX 580 8GB, DeepSeek R1 Distill 7B achieves approximately 28.0 tokens per second decode speed with a time-to-first-token of 6917ms using Q4_K_M quantization.

Can RX 580 8GB run DeepSeek R1 Distill 7B for coding?

For coding workloads, DeepSeek R1 Distill 7B on RX 580 8GB receives a B grade with 28.0 tok/s and 33K context.

What context window can DeepSeek R1 Distill 7B use on RX 580 8GB?

On RX 580 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 580 8GB See all hardware for DeepSeek R1 Distill 7B

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