Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for StarCoder 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.

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

YES — With F16

B67Good

Estimated from fit model

StarCoder 7B needs ~35.9 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.

StarCoder 7B at Q4_K_M needs 12.8 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (35.9 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 25.9 GB, 38.4 tok/s, Runs well

25.9 GB required108.8 GB available

24% VRAM used

Fit status

Runs well

Decode

38.4 tok/s

TTFT

5047 ms

Safe context

Memory

25.9 GB / 108.8 GB

Memory breakdown

Weights4.3 GB

KV Cache7.3 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsStarCoder 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 StarCoder 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	B63
Q3_K_S	3	3.4 GB	Low	B63
NVFP4	4

Get started

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

Run

lms load starcoder-7b && lms server start

Upgrade options

Hardware that runs StarCoder 7B well

NVIDIA H200 141GBBudget pick

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

Raises estimated decode speed by about 155%.98 tok/s decode

Raises estimated decode speed by about 155%.

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

~$30,000 MSRP

NVIDIA H200 PCIe 141GBBest value

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

Raises estimated decode speed by about 155%.98 tok/s decode

Raises estimated decode speed by about 155%.

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

~$30,000 MSRP

NVIDIA B200 180GBNVIDIA upgrade

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

Raises estimated decode speed by about 155%.98 tok/s decode

Raises estimated decode speed by about 155%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run StarCoder 7B?

Yes, NVIDIA DGX Spark 128GB can run StarCoder 7B at F16 quantization (Runs well). The recommended Q4_K_M requires 12.8 GB which exceeds available memory, but at F16 it needs only 35.9 GB. Expected decode speed: 16.0 tok/s.

How much VRAM does StarCoder 7B need?

StarCoder 7B (7B parameters) requires approximately 12.8 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 35.9 GB.

What is the best quantization for StarCoder 7B?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 35.9 GB.

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

On NVIDIA DGX Spark 128GB, StarCoder 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 StarCoder 7B for coding?

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

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

On NVIDIA DGX Spark 128GB, StarCoder 7B 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.

See all results for NVIDIA DGX Spark 128GB See all hardware for StarCoder 7B

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