Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for internlm JanusCoder 14B?

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 internlm JanusCoder 14B run on NVIDIA DGX Spark 128GB?

YES — With F16

C43Usable

Estimated from fit model

internlm JanusCoder 14B needs ~44.6 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.

internlm JanusCoder 14B at Q4_K_M needs 11.4 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (44.6 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 24.4 GB, 19.2 tok/s, Runs well

24.4 GB required108.8 GB available

22% VRAM used

Fit status

Runs well

Decode

19.2 tok/s

TTFT

10094 ms

Safe context

839K

Memory

24.4 GB / 108.8 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B 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: 19.2 tok/s decode · 10.1s TTFT (warm) · 48 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	19.2 tok/s	5506 ms	839K
Coding	F	Too heavy	3.5 tok/s	56076 ms	4K
Agentic Coding	C	Runs well	19.2 tok/s	14682 ms	839K
Reasoning	C	Runs well	19.2 tok/s	11929 ms	839K
RAG	C	Runs well	19.2 tok/s	18352 ms	839K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	D39
Q3_K_S	3	6.9 GB	Low	D39
NVFP4	4

Get started

Copy-paste commands to run internlm JanusCoder 14B on your machine.

Run

lms load hf-bartowski--internlm-januscoder-14b-gguf && lms server start

Upgrade options

Hardware that runs internlm JanusCoder 14B well

NVIDIA H200 141GBBudget pick

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

Raises estimated decode speed by about 921%.196 tok/s decode

Raises estimated decode speed by about 921%.

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 921%.196 tok/s decode

Raises estimated decode speed by about 921%.

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 921%.196 tok/s decode

Raises estimated decode speed by about 921%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run internlm JanusCoder 14B?

Yes, NVIDIA DGX Spark 128GB can run internlm JanusCoder 14B at F16 quantization (Runs well). The recommended Q4_K_M requires 11.4 GB which exceeds available memory, but at F16 it needs only 44.6 GB. Expected decode speed: 8.0 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 11.4 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 44.6 GB.

What is the best quantization for internlm JanusCoder 14B?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 44.6 GB.

What speed will internlm JanusCoder 14B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, internlm JanusCoder 14B achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24230ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on NVIDIA DGX Spark 128GB receives a F grade with 3.5 tok/s and 4K context.

What context window can internlm JanusCoder 14B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, internlm JanusCoder 14B can safely use up to 642K tokens of context at F16 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for NVIDIA DGX Spark 128GB See all hardware for internlm JanusCoder 14B

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