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URL: https://willitrunai.com/can-run/hf-second-state--starcoder2-7b-gguf-on-arc-pro-a40-6gb

⇱ StarCoder2 7B on Intel Arc Pro A40 6GB? YES

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

BARELY — Tight on Memory

D38Poor
○Estimated from fit model

StarCoder2 7B needs ~6.6 GB VRAM. Intel Arc Pro A40 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, 13.6 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

13.6 tok/s

TTFT

14275 ms

Safe context

4K

Memory

6.6 GB / 6.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB
KV Cache0.8 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: 13.6 tok/s decode · 14.3s TTFT (warm) · 34 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
ChatCRuns with offload (needs ~0.1 GB host RAM)15.5 tok/s6801 ms4K
CodingDVery compromised (needs ~0.4 GB host RAM)13.6 tok/s14275 ms4K
Agentic CodingFToo heavy10.6 tok/s26579 ms4K
ReasoningDVery compromised (needs ~0.4 GB host RAM)13.6 tok/s16870 ms4K
RAGFToo heavy10.6 tok/s33224 ms4K

Quantization options

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

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowC54
Q3_K_SBest for your GPU
3
3.4 GB
LowC54
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 StarCoder2 7B on your machine.

Run

lms load hf-second-state--starcoder2-7b-gguf && lms server start

Upgrade options

Hardware that runs StarCoder2 7B well

👁 Intel
Intel Arc A580 8GBBudget pick
8 GB VRAM (+2)512 GB/s (+320)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.58.8 tok/s decode

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

Raises estimated decode speed by about 332%.

~$179 MSRP

👁 Intel
Intel Arc B570 10GBBest value
10 GB VRAM (+4)380 GB/s (+188)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.48.1 tok/s decode

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

Raises estimated decode speed by about 254%.

~$219 MSRP

👁 Intel
Intel Arc B580 12GBIntel upgrade
12 GB VRAM (+6)456 GB/s (+264)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.51.3 tok/s decode

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

Raises estimated decode speed by about 277%.

~$249 MSRP

Frequently asked questions

See all results for Intel Arc Pro A40 6GBSee all hardware for StarCoder2 7B