Can Qwen 2.5 Coder 14B run on Radeon RX 7800M 12GB?

BARELY — Tight on Memory

C53Usable

Estimated from fit model

Qwen 2.5 Coder 14B needs ~13.6 GB VRAM. Radeon RX 7800M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~19 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 13.6 GB, 18.7 tok/s, Very compromised (needs ~1 GB host RAM)

13.6 GB required12.0 GB available

113% VRAM needed

1.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~1 GB host RAM)

Decode

18.7 tok/s

TTFT

10374 ms

Safe context

Memory

13.6 GB / 12.0 GB

Offload

10%

Memory breakdown

Weights8.5 GB

KV Cache2.9 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 14B on Radeon RX 7800M 12GB
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: 18.7 tok/s decode · 10.4s TTFT (warm) · 47 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 1.0 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs with offload (needs ~0.1 GB host RAM)	23.7 tok/s	4449 ms	7K
Coding	C	Very compromised (needs ~1 GB host RAM)	18.7 tok/s	10374 ms	7K
Agentic Coding	F	Too heavy	12.4 tok/s	22770 ms	7K
Reasoning	C	Very compromised (needs ~1 GB host RAM)	18.7 tok/s	12260 ms	7K
RAG	F	Too heavy	12.4 tok/s	28462 ms	7K

Quantization options

How Qwen 2.5 Coder 14B (14B params) fits at each quantization level on Radeon RX 7800M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	B67
Q3_K_S	3	6.9 GB	Low	B66
NVFP4	4	7.8 GB	Medium	B66
Q4_K_MBest for your GPU	4	8.5 GB	Medium	B66
Q5_K_M	5	10.1 GB	High	F0
Q6_K	6	11.5 GB	High	F0
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

Get started

Copy-paste commands to run Qwen 2.5 Coder 14B on your machine.

Run

ollama run qwen2.5-coder:14b

アップグレードオプション

Qwen 2.5 Coder 14Bを快適に動かすハードウェア

RX 7600 XT 16GBコスパ重視

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.21.1 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

〜$329 MSRP

RX 9060 XT 16GBコスパ最良

16 GB VRAM (+4)

Removes host-memory offload, which is usually the single biggest latency and throughput win.25.5 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 36%.

〜$349 MSRP

RX 9070 16GBAMDアップグレード

16 GB VRAM (+4)640 GB/s (+208)

Removes host-memory offload, which is usually the single biggest latency and throughput win.50.2 tok/sデコード

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 168%.

〜$479 MSRP

Frequently asked questions

Can Radeon RX 7800M 12GB run Qwen 2.5 Coder 14B?

Yes, Radeon RX 7800M 12GB can run Qwen 2.5 Coder 14B with a C grade (Very compromised (needs ~1 GB host RAM)). Expected decode speed: 18.7 tok/s.

How much VRAM does Qwen 2.5 Coder 14B need?

Qwen 2.5 Coder 14B (14B parameters) requires approximately 13.6 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 Radeon RX 7800M 12GB?

On Radeon RX 7800M 12GB, Qwen 2.5 Coder 14B achieves approximately 18.7 tokens per second decode speed with a time-to-first-token of 10374ms using Q4_K_M quantization.

Can Radeon RX 7800M 12GB run Qwen 2.5 Coder 14B for coding?

For coding workloads, Qwen 2.5 Coder 14B on Radeon RX 7800M 12GB receives a C grade with 18.7 tok/s and 7K context.

What context window can Qwen 2.5 Coder 14B use on Radeon RX 7800M 12GB?

On Radeon RX 7800M 12GB, Qwen 2.5 Coder 14B can safely use up to 7K 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 Radeon RX 7800M 12GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for Radeon RX 7800M 12GB See all hardware for Qwen 2.5 Coder 14B

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