Can Qwen 2.5 Coder 7B run on RX 590 8GB?

YES — Tight Fit

B70Good

Estimated from fit model

Qwen 2.5 Coder 7B needs ~6.8 GB VRAM. RX 590 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

38K

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 feelsQwen 2.5 Coder 7B on RX 590 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	38K
Coding	B	Tight fit	28.0 tok/s	6917 ms	38K
Agentic Coding	B	Runs with offload	28.0 tok/s	10061 ms	38K
Reasoning	B	Tight fit	28.0 tok/s	8174 ms	38K
RAG	B	Runs with offload	28.0 tok/s	12576 ms	38K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A73
Q3_K_S	3	3.4 GB	Low	A74
NVFP4	4	3.9 GB	Medium	A73
Q4_K_M	4	4.3 GB	Medium	A73
Q5_K_MBest for your GPU	5	5.0 GB	High	A73
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 Qwen 2.5 Coder 7B on your machine.

Run

ollama run qwen2.5-coder:7b

Upgrade options

Hardware that runs Qwen 2.5 Coder 7B well

RX 9060 XT 16GBBudget pick

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

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

Raises estimated decode speed by about 83%.

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

~$349 MSRP

RX 7700 XT 12GBBest value

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

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

Raises estimated decode speed by about 135%.

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

~$449 MSRP

RX 6700 XT 12GBAMD upgrade

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

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

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 590 8GB run Qwen 2.5 Coder 7B?

Yes, RX 590 8GB can run Qwen 2.5 Coder 7B with a B grade (Tight fit). Expected decode speed: 28.0 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 7B?

The recommended quantization for Qwen 2.5 Coder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 7B run at on RX 590 8GB?

On RX 590 8GB, Qwen 2.5 Coder 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 590 8GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on RX 590 8GB receives a B grade with 28.0 tok/s and 38K context.

What context window can Qwen 2.5 Coder 7B use on RX 590 8GB?

On RX 590 8GB, Qwen 2.5 Coder 7B can safely use up to 38K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

See all results for RX 590 8GB See all hardware for Qwen 2.5 Coder 7B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/qwen-2.5-coder-7b-on-rx-590-8gb" 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

28.0 tok/s

3773 ms

38K

Coding

Tight fit

28.0 tok/s

6917 ms

38K

Agentic Coding

Runs with offload

28.0 tok/s

10061 ms

38K

Reasoning

Tight fit

28.0 tok/s

8174 ms

38K

RAG

Runs with offload

28.0 tok/s

12576 ms

38K

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

A73

Q3_K_S

3.4 GB

Low

A74

NVFP4

3.9 GB

Medium

A73

Q4_K_M

4.3 GB

Medium

A73

Q5_K_MBest for your GPU

5.0 GB

High

A73

Q6_K

5.7 GB

High

Q8_0

7.5 GB

Very High

F16

14.3 GB

Maximum

Frequently asked questions

Can RX 590 8GB run Qwen 2.5 Coder 7B?

Yes, RX 590 8GB can run Qwen 2.5 Coder 7B with a B grade (Tight fit). Expected decode speed: 28.0 tok/s.

How much VRAM does Qwen 2.5 Coder 7B need?

Qwen 2.5 Coder 7B (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 7B?

The recommended quantization for Qwen 2.5 Coder 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 7B run at on RX 590 8GB?

On RX 590 8GB, Qwen 2.5 Coder 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 590 8GB run Qwen 2.5 Coder 7B for coding?

For coding workloads, Qwen 2.5 Coder 7B on RX 590 8GB receives a B grade with 28.0 tok/s and 38K context.

What context window can Qwen 2.5 Coder 7B use on RX 590 8GB?

On RX 590 8GB, Qwen 2.5 Coder 7B can safely use up to 38K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.