Can StarCoder 15B run on NVIDIA DGX Spark 128GB?

YES — With F16

A71Great

Estimated from fit model

StarCoder 15B needs ~59.7 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~8 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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.

StarCoder 15B at Q5_K_M needs 26.6 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (59.7 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q5_K_M (High quality) — 39.7 GB, 15.5 tok/s, Runs well

39.7 GB required108.8 GB available

36% VRAM used

Fit status

Runs well

Decode

15.5 tok/s

TTFT

12515 ms

Safe context

Memory

39.7 GB / 108.8 GB

Memory breakdown

Weights10.8 GB

KV Cache14.6 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsStarCoder 15B 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: 15.5 tok/s decode · 12.5s TTFT (warm) · 39 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	15.5 tok/s	6826 ms	8K
Coding	F	Too heavy	2.8 tok/s	69525 ms	4K
Agentic Coding	A	Runs well	15.5 tok/s	18203 ms	8K
Reasoning	B	Runs well	15.5 tok/s	14790 ms	8K
RAG	A	Runs well	15.5 tok/s	22754 ms	8K

Quantization options

How StarCoder 15B (15B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.9 GB	Low	B64
Q3_K_S	3	7.4 GB	Low	B64
NVFP4	4	8.4 GB	Medium	B65
Q4_K_M	4	9.2 GB	Medium	B65
Q5_K_M	5	10.8 GB	High	B65
Q6_K	6	12.3 GB	High	B65
Q8_0	8	16.1 GB	Very High	B65
F16Best for your GPU	16	30.7 GB	Maximum	B68

Get started

Copy-paste commands to run StarCoder 15B on your machine.

Run

lms load starcoder && lms server start

Opções de upgrade

Hardware que roda bem StarCoder 15B

NVIDIA H200 141GBOpção econômica

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 1255%.210 tok/s decodificação

Raises estimated decode speed by about 1255%.

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

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMelhor custo-benefício

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 1255%.210 tok/s decodificação

Raises estimated decode speed by about 1255%.

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

~$30,000 MSRP

NVIDIA B200 180GBUpgrade NVIDIA

180 GB VRAM (+52)8000 GB/s (+7727)

Raises estimated decode speed by about 1255%.210 tok/s decodificação

Raises estimated decode speed by about 1255%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run StarCoder 15B?

Yes, NVIDIA DGX Spark 128GB can run StarCoder 15B at F16 quantization (Runs well). The recommended Q5_K_M requires 26.6 GB which exceeds available memory, but at F16 it needs only 59.7 GB. Expected decode speed: 7.5 tok/s.

How much VRAM does StarCoder 15B need?

StarCoder 15B (15B parameters) requires approximately 26.6 GB at Q5_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 59.7 GB.

What is the best quantization for StarCoder 15B?

The recommended quantization is Q5_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 59.7 GB.

What speed will StarCoder 15B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, StarCoder 15B achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25960ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run StarCoder 15B for coding?

For coding workloads, StarCoder 15B on NVIDIA DGX Spark 128GB receives a F grade with 2.8 tok/s and 4K context.

What context window can StarCoder 15B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, StarCoder 15B can safely use up to 8K tokens of context at F16 quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if StarCoder 15B feels slow on NVIDIA DGX Spark 128GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for StarCoder 15B?

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 StarCoder 15B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/starcoder-15b-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: