Can StarCoder2 3B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

C41Usable

Estimated from fit model

StarCoder2 3B needs ~16.5 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~42 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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) — 16.5 GB, 42.0 tok/s, Runs well

16.5 GB required108.8 GB available

15% VRAM used

Fit status

Runs well

Decode

42.0 tok/s

TTFT

4610 ms

Safe context

16K

Memory

16.5 GB / 108.8 GB

Memory breakdown

Weights1.8 GB

KV Cache0.5 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsStarCoder2 3B on NVIDIA DGX Spark 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

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	42.0 tok/s	2514 ms	16K
Coding	C	Runs well	42.0 tok/s	4610 ms	16K
Agentic Coding	C	Runs well	42.0 tok/s	6705 ms	16K
Reasoning	C	Runs well	42.0 tok/s	5448 ms	16K
RAG	C	Runs well	42.0 tok/s	8381 ms	16K

Quantization options

How StarCoder2 3B (3B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 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

ollama run starcoder2:3b

Upgrade-Optionen

Hardware, die StarCoder2 3B gut ausführt

Mac Studio M3 Ultra 256GBGünstige Wahl

256 GB Unified (+128)819 GB/s (+546)

Adds memory headroom for longer context windows and future model growth.42 tok/s Dekodierung

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

ca. $6,999 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run StarCoder2 3B?

Yes, NVIDIA DGX Spark 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 16.5 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 NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 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 NVIDIA DGX Spark 128GB run StarCoder2 3B for coding?

For coding workloads, StarCoder2 3B on NVIDIA DGX Spark 128GB receives a C grade with 42.0 tok/s and 16K context.

What context window can StarCoder2 3B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, 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.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for StarCoder2 3B?

Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for NVIDIA DGX Spark 128GB See all hardware for StarCoder2 3B

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