Can StarCoder2 15B run on Intel Arc A550M 8GB?

YES — With Q2_K

D38Poor

Estimated from fit model

StarCoder2 15B needs ~8.8 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q2_K quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very 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.

StarCoder2 15B at Q5_K_M needs 13.7 GB — too much for Intel Arc A550M 8GB (8.0 GB). Runs at Q2_K (8.8 GB) with low quality.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 13.7 GB, exceeds 8.0 GB available

13.7 GB required8.0 GB available

171% VRAM needed

5.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.7 tok/s

TTFT

71008 ms

Safe context

Memory

13.7 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights10.8 GB

KV Cache1.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStarCoder2 15B on Intel Arc A550M 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: 2.7 tok/s decode · 71.0s TTFT (warm) · 7 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

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.0 tok/s	35194 ms	4K
Coding	F	Too heavy	2.7 tok/s	71008 ms	4K
Agentic Coding	F	Too heavy	2.3 tok/s	123581 ms	4K
Reasoning	F	Too heavy	2.7 tok/s	83918 ms	4K
RAG	F	Too heavy	2.3 tok/s	154476 ms	4K

Quantization options

How StarCoder2 15B (15B params) fits at each quantization level on Intel Arc A550M 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	F0
Q3_K_S	3	7.4 GB	Low	F0
NVFP4	4	8.4 GB	Medium	F0
Q4_K_M	4	9.2 GB	Medium	F0
Q5_K_M	5	10.8 GB	High	F0
Q6_K	6	12.3 GB	High	F0
Q8_0	8	16.1 GB	Very High	F0
F16	16	30.7 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 15B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "bigcode/starcoder2-15b" \
  --hf-file "starcoder2-15b-Q5_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade options

Hardware that runs StarCoder2 15B well

Intel Arc B580 12GBBest value

12 GB VRAM (+4)456 GB/s (+232)

Makes the model fit on the accelerator instead of staying completely out of reach.12.4 tok/s decode

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

Raises estimated decode speed by about 359%.

~$249 MSRP

Intel Arc A770 16GBBudget pick

16 GB VRAM (+8)560 GB/s (+336)

Makes the model fit on the accelerator instead of staying completely out of reach.26 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

RTX 4090 24GBBiggest leap

24 GB VRAM (+16)1008 GB/s (+784)

Makes the model fit on the accelerator instead of staying completely out of reach.80.8 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.

~$1,599 MSRP

Intel Arc A730M 12GBIntel upgrade

12 GB VRAM (+4)336 GB/s (+112)

Makes the model fit on the accelerator instead of staying completely out of reach.9 tok/s decode

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

Raises estimated decode speed by about 233%.

Frequently asked questions

Can Intel Arc A550M 8GB run StarCoder2 15B?

Yes, Intel Arc A550M 8GB can run StarCoder2 15B at Q2_K quantization (Very compromised (needs ~0.5 GB host RAM)). The recommended Q5_K_M requires 13.7 GB which exceeds available memory, but at Q2_K it needs only 8.8 GB. Expected decode speed: 10.8 tok/s.

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 13.7 GB at Q5_K_M quantization. On Intel Arc A550M 8GB, it fits at Q2_K using 8.8 GB.

What is the best quantization for StarCoder2 15B?

The recommended quantization is Q5_K_M, but on Intel Arc A550M 8GB the best fitting quantization is Q2_K, which uses 8.8 GB.

What speed will StarCoder2 15B run at on Intel Arc A550M 8GB?

On Intel Arc A550M 8GB, StarCoder2 15B achieves approximately 10.8 tokens per second decode speed with a time-to-first-token of 17994ms using Q2_K quantization.

Can Intel Arc A550M 8GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on Intel Arc A550M 8GB receives a F grade with 2.7 tok/s and 4K context.

What context window can StarCoder2 15B use on Intel Arc A550M 8GB?

On Intel Arc A550M 8GB, StarCoder2 15B can safely use up to 6K tokens of context at Q2_K quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 15B feels slow on Intel Arc A550M 8GB?

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.

Would CUDA be a better path than Intel Arc A550M 8GB for StarCoder2 15B?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

See all results for Intel Arc A550M 8GB See all hardware for StarCoder2 15B

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Preview:

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.

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Too heavy

3.0 tok/s

35194 ms

Coding

Too heavy

2.7 tok/s

71008 ms

Agentic Coding

Too heavy

2.3 tok/s

123581 ms

Reasoning

Too heavy

2.7 tok/s

83918 ms

RAG

Too heavy

2.3 tok/s

154476 ms

Quant

Bits

VRAM

Quality

Fit

Q2_K

5.9 GB

Low

Q3_K_S

7.4 GB

Low

NVFP4

8.4 GB

Medium

Q4_K_M

9.2 GB

Medium

Q5_K_M

10.8 GB

High

Q6_K

12.3 GB

High

Q8_0

16.1 GB

Very High

F16

30.7 GB

Maximum

Frequently asked questions

Can Intel Arc A550M 8GB run StarCoder2 15B?

How much VRAM does StarCoder2 15B need?

StarCoder2 15B (15B parameters) requires approximately 13.7 GB at Q5_K_M quantization. On Intel Arc A550M 8GB, it fits at Q2_K using 8.8 GB.

What is the best quantization for StarCoder2 15B?

The recommended quantization is Q5_K_M, but on Intel Arc A550M 8GB the best fitting quantization is Q2_K, which uses 8.8 GB.

What speed will StarCoder2 15B run at on Intel Arc A550M 8GB?

On Intel Arc A550M 8GB, StarCoder2 15B achieves approximately 10.8 tokens per second decode speed with a time-to-first-token of 17994ms using Q2_K quantization.

Can Intel Arc A550M 8GB run StarCoder2 15B for coding?

For coding workloads, StarCoder2 15B on Intel Arc A550M 8GB receives a F grade with 2.7 tok/s and 4K context.

What context window can StarCoder2 15B use on Intel Arc A550M 8GB?

On Intel Arc A550M 8GB, StarCoder2 15B can safely use up to 6K tokens of context at Q2_K quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 15B feels slow on Intel Arc A550M 8GB?

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.

Would CUDA be a better path than Intel Arc A550M 8GB for StarCoder2 15B?