Can Qwen 2.5 Coder 14B run on RX 7600 8GB?

Q: What should I upgrade first if Qwen 2.5 Coder 14B feels slow on RX 7600 8GB?

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

Qwen 2.5 Coder 14B needs ~13.2 GB but RX 7600 8GB only has 8.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: 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) — 13.2 GB, exceeds 8.0 GB available

13.2 GB required8.0 GB available

165% VRAM needed

5.2 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.5 tok/s

TTFT

34895 ms

Safe context

Memory

13.2 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsQwen 2.5 Coder 14B on RX 7600 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: 5.5 tok/s decode · 34.9s TTFT (warm) · 14 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.2 GB, but this setup only exposes 8.0 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	7.1 tok/s	14850 ms	4K
Coding	F	Too heavy	5.5 tok/s	34895 ms	4K
Agentic Coding	F	Too heavy	3.6 tok/s	77466 ms	4K
Reasoning	F	Too heavy	5.5 tok/s	41239 ms	4K
RAG	F	Too heavy	3.6 tok/s	96833 ms	4K

Inference speed

Qwen 2.5 Coder 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Qwen 2.5 Coder 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~152 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	151.8	Fits
RTX 4090 24GB	24 GB	Q4_K_M	96.9	Fits

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on RX 7600 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	F0
Q3_K_S	3	6.9 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs Qwen 2.5 Coder 14B well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.21.1 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.

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+8)320 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.25.5 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.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.19 tok/s decode

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

Raises estimated decode speed by about 245%.

~$449 MSRP

RTX A4500 20GBBiggest leap

20 GB VRAM (+12)640 GB/s (+352)

Makes the model fit on the accelerator instead of staying completely out of reach.63.1 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.

~$2,000 MSRP

Frequently asked questions

Can RX 7600 8GB run Qwen 2.5 Coder 14B?

No, Qwen 2.5 Coder 14B requires more memory than RX 7600 8GB provides.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 13.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Qwen 2.5 Coder 14B?

The recommended quantization for Qwen 2.5 Coder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will Qwen 2.5 Coder 14B run at on RX 7600 8GB?

On RX 7600 8GB, Qwen 2.5 Coder 14B achieves approximately 5.5 tokens per second decode speed with a time-to-first-token of 34895ms using Q4_K_M quantization.

Can RX 7600 8GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on RX 7600 8GB receives a F grade with 5.5 tok/s and 4K context.

What context window can Qwen 2.5 Coder 14B use on RX 7600 8GB?

On RX 7600 8GB, Qwen 2.5 Coder 14B can safely use up to 4K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Qwen 2.5 Coder 14B feels slow on RX 7600 8GB?

See all results for RX 7600 8GB See all hardware for Qwen 2.5 Coder 14B

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