Can StarCoder2 3B run on Intel Arc A370M 4GB?

YES — Tight Fit

C49Usable

Estimated from fit model

StarCoder2 3B needs ~3.6 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With Q4_K_M quantization, expect ~33 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 3.6 GB, 32.6 tok/s, Tight fit

3.6 GB required4.0 GB available

90% VRAM used

Fit status

Tight fit

Decode

32.6 tok/s

TTFT

5936 ms

Safe context

16K

Memory

3.6 GB / 4.0 GB

Memory breakdown

Weights1.8 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom0.4 GB

See how fast it feels

See how fast it feelsStarCoder2 3B on Intel Arc A370M 4GB
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: 32.6 tok/s decode · 5.9s TTFT (warm) · 82 tok/s prefill

What limits this setup

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

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	32.6 tok/s	3238 ms	16K
Coding	C	Tight fit	32.6 tok/s	5936 ms	16K
Agentic Coding	C	Runs with offload (needs ~0 GB host RAM)	23.9 tok/s	11790 ms	16K
Reasoning	C	Tight fit	32.6 tok/s	7016 ms	16K
RAG	C	Runs with offload (needs ~0 GB host RAM)	23.9 tok/s	14738 ms	16K

Quantization options

How StarCoder2 3B (3B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	C54
Q3_K_S	3	1.5 GB	Low	C54
NVFP4	4	1.7 GB	Medium	C54
Q4_K_MBest for your GPU	4	1.8 GB	Medium	C53
Q5_K_M	5	2.2 GB	High	F0
Q6_K	6	2.5 GB	High	F0
Q8_0	8	3.2 GB	Very High	F0
F16	16	6.1 GB	Maximum	F0

Get started

Copy-paste commands to run StarCoder2 3B on your machine.

Run

ollama run starcoder2:3b

Upgrade options

Hardware that runs StarCoder2 3B well

Intel Arc A380 6GBBudget pick

6 GB VRAM (+2)186 GB/s (+74)

Raises estimated decode speed by about 29%.42 tok/s decode

Raises estimated decode speed by about 29%.

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

~$139 MSRP

Intel Arc Pro A40 6GBBest value

6 GB VRAM (+2)192 GB/s (+80)

Raises estimated decode speed by about 29%.42 tok/s decode

Raises estimated decode speed by about 29%.

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

~$249 MSRP

Frequently asked questions

Can Intel Arc A370M 4GB run StarCoder2 3B?

Yes, Intel Arc A370M 4GB can run StarCoder2 3B with a C grade (Tight fit). Expected decode speed: 32.6 tok/s.

How much VRAM does StarCoder2 3B need?

StarCoder2 3B (3B parameters) requires approximately 3.6 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 3B?

The recommended quantization for StarCoder2 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 3B run at on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, StarCoder2 3B achieves approximately 32.6 tokens per second decode speed with a time-to-first-token of 5936ms using Q4_K_M quantization.

Can Intel Arc A370M 4GB run StarCoder2 3B for coding?

For coding workloads, StarCoder2 3B on Intel Arc A370M 4GB receives a C grade with 32.6 tok/s and 16K context.

What context window can StarCoder2 3B use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, StarCoder2 3B can safely use up to 16K 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 3B feels slow on Intel Arc A370M 4GB?

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 A370M 4GB for StarCoder2 3B?

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 A370M 4GB See all hardware for StarCoder2 3B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/starcoder2-3b-on-arc-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Tight fit

32.6 tok/s

3238 ms

16K

Coding

Tight fit

32.6 tok/s

5936 ms

16K

Agentic Coding

Runs with offload (needs ~0 GB host RAM)

23.9 tok/s

11790 ms

16K

Reasoning

Tight fit

32.6 tok/s

7016 ms

16K

RAG

Runs with offload (needs ~0 GB host RAM)

23.9 tok/s

14738 ms

16K

Quant

Bits

VRAM

Quality

Fit

Q2_K

1.2 GB

Low

C54

Q3_K_S

1.5 GB

Low

C54

NVFP4

1.7 GB

Medium

C54

Q4_K_MBest for your GPU

1.8 GB

Medium

C53

Q5_K_M

2.2 GB

High

Q6_K

2.5 GB

High

Q8_0

3.2 GB

Very High

F16

6.1 GB

Maximum

Frequently asked questions

Can Intel Arc A370M 4GB run StarCoder2 3B?

Yes, Intel Arc A370M 4GB can run StarCoder2 3B with a C grade (Tight fit). Expected decode speed: 32.6 tok/s.

How much VRAM does StarCoder2 3B need?

StarCoder2 3B (3B parameters) requires approximately 3.6 GB of memory with Q4_K_M quantization.

What is the best quantization for StarCoder2 3B?

The recommended quantization for StarCoder2 3B is Q4_K_M, which balances quality and memory efficiency.

What speed will StarCoder2 3B run at on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, StarCoder2 3B achieves approximately 32.6 tokens per second decode speed with a time-to-first-token of 5936ms using Q4_K_M quantization.

Can Intel Arc A370M 4GB run StarCoder2 3B for coding?

For coding workloads, StarCoder2 3B on Intel Arc A370M 4GB receives a C grade with 32.6 tok/s and 16K context.

What context window can StarCoder2 3B use on Intel Arc A370M 4GB?

On Intel Arc A370M 4GB, StarCoder2 3B can safely use up to 16K 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 3B feels slow on Intel Arc A370M 4GB?

Would CUDA be a better path than Intel Arc A370M 4GB for StarCoder2 3B?