Can StarCoder2 3B run on Gaudi 3 128GB?

YES — Runs Great

C42Usable

Estimated from fit model

StarCoder2 3B needs ~15.9 GB VRAM. Gaudi 3 128GB has 128.0 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 15.9 GB, 42.0 tok/s, Runs well

15.9 GB required128.0 GB available

12% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

5.1M

Memory

15.9 GB / 128.0 GB

Memory breakdown

Weights1.8 GB

KV Cache0.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsStarCoder2 3B on Gaudi 3 128GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 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	42.0 tok/s	2514 ms	5.1M
Coding	C	Runs well	42.0 tok/s	4610 ms	5.1M
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	5.1M
Reasoning	C	Runs well	42.0 tok/s	5448 ms	5.1M
RAG	C	Runs well	42.0 tok/s	8381 ms	5.1M

Quantization options

How StarCoder2 3B (3B params) fits at each quantization level on Gaudi 3 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	1.2 GB	Low	D38
Q3_K_S	3	1.5 GB	Low	D38
NVFP4	4	1.7 GB	Medium	D38
Q4_K_M	4	1.8 GB	Medium	D38
Q5_K_M	5	2.2 GB	High	D38
Q6_K	6	2.5 GB	High	D38
Q8_0	8	3.2 GB	Very High	D38
F16Best for your GPU	16	6.1 GB	Maximum	D38

Get started

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

Run

lms load hf-second-state--starcoder2-3b-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien StarCoder2 3B

Mac Studio M3 Ultra 256GBOpción económica

256 GB Unified (+128)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.42 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$6,999 MSRP

Frequently asked questions

Can Gaudi 3 128GB run StarCoder2 3B?

Yes, Gaudi 3 128GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.

How much VRAM does StarCoder2 3B need?

StarCoder2 3B (3B parameters) requires approximately 15.9 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 Gaudi 3 128GB?

On Gaudi 3 128GB, StarCoder2 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.

Can Gaudi 3 128GB run StarCoder2 3B for coding?

For coding workloads, StarCoder2 3B on Gaudi 3 128GB receives a C grade with 42.0 tok/s and 5.1M context.

What context window can StarCoder2 3B use on Gaudi 3 128GB?

On Gaudi 3 128GB, StarCoder2 3B can safely use up to 5.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder2 3B feels slow on Gaudi 3 128GB?

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 Gaudi 3 128GB 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 Gaudi 3 128GB See all hardware for StarCoder2 3B

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