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URL: https://willitrunai.com/can-run/qwen-2.5-coder-7b-on-arc-a380-6gb

⇱ Qwen 2.5 Coder 7B on Intel Arc A380 6GB? YES

Can Qwen 2.5 Coder 7B run on Intel Arc A380 6GB?

BARELY — Tight on Memory

B58Good
○Estimated from fit model

Qwen 2.5 Coder 7B needs ~6.6 GB VRAM. Intel Arc A380 6GB has 6.0 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload
Share:

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) — 6.6 GB, 14.1 tok/s, Very compromised (needs ~0.4 GB host RAM)
6.6 GB required6.0 GB available
110% VRAM needed

0.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

14.1 tok/s

TTFT

13720 ms

Safe context

4K

Memory

6.6 GB / 6.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.6 GB

See how fast it feels

See how fast it feelsQwen 2.5 Coder 7B on Intel Arc A380 6GB
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: 14.1 tok/s decode · 13.7s TTFT (warm) · 35 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.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

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.

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Buy headroom, not only minimum fit

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

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBRuns with offload (needs ~0.1 GB host RAM)16.2 tok/s6504 ms4K
CodingBVery compromised (needs ~0.4 GB host RAM)14.1 tok/s13720 ms4K
Agentic CodingFToo heavy10.9 tok/s25764 ms4K
ReasoningBVery compromised (needs ~0.4 GB host RAM)14.1 tok/s16215 ms4K
RAGFToo heavy10.9 tok/s32204 ms4K

Quantization options

How Qwen 2.5 Coder 7B (7B params) fits at each quantization level on Intel Arc A380 6GB (6.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowA74
Q3_K_SBest for your GPU
3
3.4 GB
LowA74
NVFP4
4
3.9 GB
MediumF0
Q4_K_M
4
4.3 GB
MediumF0
Q5_K_M
5
5.0 GB
HighF0
Q6_K
6
5.7 GB
HighF0
Q8_0
8
7.5 GB
Very HighF0
F16
16
14.3 GB
MaximumF0

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

👁 Intel
Intel Arc A580 8GBBudget pick
8 GB VRAM (+2)512 GB/s (+326)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.63.8 tok/s decode

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

Raises estimated decode speed by about 352%.

~$179 MSRP

👁 Intel
Intel Arc B570 10GBBest value
10 GB VRAM (+4)380 GB/s (+194)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.52.2 tok/s decode

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

Raises estimated decode speed by about 270%.

~$219 MSRP

👁 Intel
Intel Arc B580 12GBIntel upgrade
12 GB VRAM (+6)456 GB/s (+270)
A
Removes host-memory offload, which is usually the single biggest latency and throughput win.55.7 tok/s decode

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

Raises estimated decode speed by about 295%.

~$249 MSRP

Frequently asked questions

See all results for Intel Arc A380 6GBSee all hardware for Qwen 2.5 Coder 7B