Can StarCoder2 7B run on Intel Arc Pro A40 6GB?

YES — With Offload

C48Usable

Estimated from fit model

StarCoder2 7B needs ~6.3 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q4_K_M quantization, expect ~17 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 6.3 GB, 16.5 tok/s, Runs with offload (needs ~0.2 GB host RAM)

6.3 GB required6.0 GB available

105% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

16.5 tok/s

TTFT

11728 ms

Safe context

Memory

6.3 GB / 6.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsStarCoder2 7B on Intel Arc Pro A40 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: 16.5 tok/s decode · 11.7s TTFT (warm) · 41 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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	C	Runs with offload (needs ~0 GB host RAM)	18.0 tok/s	5883 ms	8K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	16.5 tok/s	11728 ms	8K
Agentic Coding	D	Very compromised (needs ~0.5 GB host RAM)	14.1 tok/s	19981 ms	8K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	16.5 tok/s	13860 ms	8K
RAG	D	Very compromised (needs ~0.5 GB host RAM)	14.1 tok/s	24977 ms	8K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C53
Q3_K_SBest for your GPU	3	3.4 GB	Low	C53
NVFP4	4	3.9 GB	Medium	F0
Q4_K_M	4	4.3 GB	Medium	F0
Q5_K_M	5	5.0 GB	High	F0
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 StarCoder2 7B on your machine.

Run

lms load starcoder2-7b && lms server start

Upgrade options

Hardware that runs StarCoder2 7B well

Intel Arc A580 8GBBudget pick

8 GB VRAM (+2)512 GB/s (+320)

Raises estimated decode speed by about 288%.64.1 tok/s decode

Raises estimated decode speed by about 288%.

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

~$179 MSRP

Intel Arc B570 10GBBest value

10 GB VRAM (+4)380 GB/s (+188)

Raises estimated decode speed by about 218%.52.5 tok/s decode

Raises estimated decode speed by about 218%.

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

~$219 MSRP

Intel Arc B580 12GBIntel upgrade

12 GB VRAM (+6)456 GB/s (+264)

Raises estimated decode speed by about 239%.56 tok/s decode

Raises estimated decode speed by about 239%.

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

~$249 MSRP

Frequently asked questions

Can Intel Arc Pro A40 6GB run StarCoder2 7B?

Yes, Intel Arc Pro A40 6GB can run StarCoder2 7B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 16.5 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 6.3 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, StarCoder2 7B achieves approximately 16.5 tokens per second decode speed with a time-to-first-token of 11728ms using Q4_K_M quantization.

Can Intel Arc Pro A40 6GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on Intel Arc Pro A40 6GB receives a C grade with 16.5 tok/s and 8K context.

What context window can StarCoder2 7B use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, StarCoder2 7B can safely use up to 8K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 7B feels slow on Intel Arc Pro A40 6GB?

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.

Would CUDA be a better path than Intel Arc Pro A40 6GB for StarCoder2 7B?

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 Pro A40 6GB See all hardware for StarCoder2 7B

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Preview:

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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

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	C	Runs with offload (needs ~0 GB host RAM)	18.0 tok/s	5883 ms	8K
Coding	C	Runs with offload (needs ~0.2 GB host RAM)	16.5 tok/s	11728 ms	8K
Agentic Coding	D	Very compromised (needs ~0.5 GB host RAM)	14.1 tok/s	19981 ms	8K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	16.5 tok/s	13860 ms	8K
RAG	D	Very compromised (needs ~0.5 GB host RAM)	14.1 tok/s	24977 ms	8K

Quant

Bits

VRAM

Quality

Fit

Q2_K

2.7 GB

Low

C53

Q3_K_SBest for your GPU

3.4 GB

Low

C53

NVFP4

3.9 GB

Medium

Q4_K_M

4.3 GB

Medium

Q5_K_M

5.0 GB

High

Q6_K

5.7 GB

High

Q8_0

7.5 GB

Very High

F16

14.3 GB

Maximum

Frequently asked questions

Can Intel Arc Pro A40 6GB run StarCoder2 7B?

Yes, Intel Arc Pro A40 6GB can run StarCoder2 7B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 16.5 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 6.3 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 7B?

The recommended quantization for StarCoder2 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 7B run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, StarCoder2 7B achieves approximately 16.5 tokens per second decode speed with a time-to-first-token of 11728ms using Q4_K_M quantization.

Can Intel Arc Pro A40 6GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on Intel Arc Pro A40 6GB receives a C grade with 16.5 tok/s and 8K context.

What context window can StarCoder2 7B use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, StarCoder2 7B can safely use up to 8K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 7B feels slow on Intel Arc Pro A40 6GB?

Would CUDA be a better path than Intel Arc Pro A40 6GB for StarCoder2 7B?