Can StarCoder2 7B run on NVIDIA DGX Spark 128GB?

YES — With F16

C42Usable

Estimated from fit model

StarCoder2 7B needs ~29.4 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~16 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.

StarCoder2 7B at Q4_K_M needs 6.3 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (29.4 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 19.3 GB, 38.4 tok/s, Runs well

19.3 GB required108.8 GB available

18% VRAM used

Fit status

Runs well

Decode

38.4 tok/s

TTFT

5047 ms

Safe context

1.8M

Memory

19.3 GB / 108.8 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsStarCoder2 7B 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: 38.4 tok/s decode · 5.0s TTFT (warm) · 96 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	F	Too heavy	6.9 tok/s	15293 ms	4K
Coding	F	Too heavy	6.9 tok/s	28038 ms	4K
Agentic Coding	F	Too heavy	6.9 tok/s	40783 ms	4K
Reasoning	F	Too heavy	6.9 tok/s	33136 ms	4K
RAG	F	Too heavy	6.9 tok/s	50978 ms	4K

Quantization options

How StarCoder2 7B (7B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	D39
Q3_K_S	3	3.4 GB	Low	D39
NVFP4	4	3.9 GB	Medium	D39
Q4_K_M	4	4.3 GB	Medium	D39
Q5_K_M	5	5.0 GB	High	D39
Q6_K	6	5.7 GB	High	D39
Q8_0	8	7.5 GB	Very High	D39
F16Best for your GPU	16	14.3 GB	Maximum	D40

Get started

Copy-paste commands to run StarCoder2 7B on your machine.

Run

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

Opciones de mejora

Hardware que ejecuta bien StarCoder2 7B

NVIDIA H200 141GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 155%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 155%.

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

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 155%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 155%.

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

~$30,000 MSRP

NVIDIA B200 180GBMejora NVIDIA

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

Sube la velocidad estimada de decodificación alrededor de un 155%.98 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 155%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run StarCoder2 7B?

Yes, NVIDIA DGX Spark 128GB can run StarCoder2 7B at F16 quantization (Runs well). The recommended Q4_K_M requires 6.3 GB which exceeds available memory, but at F16 it needs only 29.4 GB. Expected decode speed: 16.0 tok/s.

How much VRAM does StarCoder2 7B need?

StarCoder2 7B (7B parameters) requires approximately 6.3 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 29.4 GB.

What is the best quantization for StarCoder2 7B?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 29.4 GB.

What speed will StarCoder2 7B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, StarCoder2 7B achieves approximately 16.0 tokens per second decode speed with a time-to-first-token of 12115ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run StarCoder2 7B for coding?

For coding workloads, StarCoder2 7B on NVIDIA DGX Spark 128GB receives a F grade with 6.9 tok/s and 4K context.

What context window can StarCoder2 7B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, StarCoder2 7B can safely use up to 1.6M tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for StarCoder2 7B?

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 7B

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