Can starcoder2 15b instruct v0.1 run on Intel Arc A770 16GB?

YES — Tight Fit

C50Usable

Estimated from fit model

starcoder2 15b instruct v0.1 needs ~13.4 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 13.4 GB, 27.5 tok/s, Tight fit

13.4 GB required16.0 GB available

84% VRAM used

Fit status

Tight fit

Decode

27.5 tok/s

TTFT

7030 ms

Safe context

40K

Memory

13.4 GB / 16.0 GB

Memory breakdown

Weights9.2 GB

KV Cache1.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsstarcoder2 15b instruct v0.1 on Intel Arc A770 16GB
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: 27.5 tok/s decode · 7.0s TTFT (warm) · 69 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	Runs well	27.5 tok/s	3834 ms	40K
Coding	C	Tight fit	27.5 tok/s	7030 ms	40K
Agentic Coding	C	Tight fit	27.5 tok/s	10225 ms	40K
Reasoning	C	Tight fit	27.5 tok/s	8308 ms	40K
RAG	C	Tight fit	27.5 tok/s	12781 ms	40K

Quantization options

How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on Intel Arc A770 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	C49
Q3_K_S	3	7.4 GB	Low	C51
NVFP4	4	8.4 GB	Medium	C51
Q4_K_M	4	9.2 GB	Medium	C51
Q5_K_M	5	10.8 GB	High	C50
Q6_KBest for your GPU	6	12.3 GB	High	C50
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 instruct v0.1 on your machine.

Run

lms load hf-lmstudio-community--starcoder2-15b-instruct-v0-1-gguf && lms server start

Opções de upgrade

Hardware que roda bem starcoder2 15b instruct v0.1

Intel Arc Pro B60 24GBOpção econômica

24 GB VRAM (+8)

Adds memory headroom for longer context windows and future model growth.26.9 tok/s decodificação

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

~$599 MSRP

RX 7900 XT 20GBMelhor custo-benefício

20 GB VRAM (+4)800 GB/s (+240)

Raises estimated decode speed by about 91%.52.5 tok/s decodificação

Raises estimated decode speed by about 91%.

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

~$899 MSRP

Frequently asked questions

Can Intel Arc A770 16GB run starcoder2 15b instruct v0.1?

Yes, Intel Arc A770 16GB can run starcoder2 15b instruct v0.1 with a C grade (Tight fit). Expected decode speed: 27.5 tok/s.

How much VRAM does starcoder2 15b instruct v0.1 need?

starcoder2 15b instruct v0.1 (15B parameters) requires approximately 13.4 GB of memory with Q4_K_M quantization.

What is the best quantization for starcoder2 15b instruct v0.1?

The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will starcoder2 15b instruct v0.1 run at on Intel Arc A770 16GB?

On Intel Arc A770 16GB, starcoder2 15b instruct v0.1 achieves approximately 27.5 tokens per second decode speed with a time-to-first-token of 7030ms using Q4_K_M quantization.

Can Intel Arc A770 16GB run starcoder2 15b instruct v0.1 for coding?

For coding workloads, starcoder2 15b instruct v0.1 on Intel Arc A770 16GB receives a C grade with 27.5 tok/s and 40K context.

What context window can starcoder2 15b instruct v0.1 use on Intel Arc A770 16GB?

On Intel Arc A770 16GB, starcoder2 15b instruct v0.1 can safely use up to 40K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if starcoder2 15b instruct v0.1 feels slow on Intel Arc A770 16GB?

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 A770 16GB for starcoder2 15b instruct v0.1?

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.

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