Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for CodeLlama 13B Instruct?

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 CodeLlama 13B Instruct run on NVIDIA DGX Spark 128GB?

YES — With F16

B70Good

Estimated from fit model

CodeLlama 13B Instruct needs ~53.1 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~9 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.

CodeLlama 13B Instruct at Q4_K_M needs 21.3 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (53.1 GB) with maximum quality. 8 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 34.4 GB, 20.7 tok/s, Runs well

34.4 GB required108.8 GB available

32% VRAM used

Fit status

Runs well

Decode

20.7 tok/s

TTFT

9373 ms

Safe context

16K

Memory

34.4 GB / 108.8 GB

Memory breakdown

Weights7.9 GB

KV Cache12.2 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsCodeLlama 13B Instruct 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: 20.7 tok/s decode · 9.4s TTFT (warm) · 52 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	3.7 tok/s	28402 ms	4K
Coding	F	Too heavy	3.7 tok/s	52071 ms	4K
Agentic Coding	F	Too heavy	3.7 tok/s	75739 ms	4K
Reasoning	F	Too heavy	3.7 tok/s	61538 ms	4K
RAG	F	Too heavy	3.7 tok/s	94674 ms	4K

Quantization options

How CodeLlama 13B Instruct (13B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B64
Q3_K_S	3	6.4 GB	Low	B64
NVFP4	4

Get started

Copy-paste commands to run CodeLlama 13B Instruct on your machine.

Run

lms load CodeLlama-13b-Instruct-hf && lms server start

Upgrade options

Hardware that runs CodeLlama 13B Instruct well

NVIDIA H200 141GBBudget pick

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

Raises estimated decode speed by about 779%.182 tok/s decode

Raises estimated decode speed by about 779%.

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 779%.182 tok/s decode

Raises estimated decode speed by about 779%.

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 779%.182 tok/s decode

Raises estimated decode speed by about 779%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run CodeLlama 13B Instruct?

Yes, NVIDIA DGX Spark 128GB can run CodeLlama 13B Instruct at F16 quantization (Runs well). The recommended Q4_K_M requires 21.3 GB which exceeds available memory, but at F16 it needs only 53.1 GB. Expected decode speed: 8.6 tok/s.

How much VRAM does CodeLlama 13B Instruct need?

CodeLlama 13B Instruct (13B parameters) requires approximately 21.3 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 53.1 GB.

What is the best quantization for CodeLlama 13B Instruct?

The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 53.1 GB.

What speed will CodeLlama 13B Instruct run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, CodeLlama 13B Instruct achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22499ms using F16 quantization.

Can NVIDIA DGX Spark 128GB run CodeLlama 13B Instruct for coding?

For coding workloads, CodeLlama 13B Instruct on NVIDIA DGX Spark 128GB receives a F grade with 3.7 tok/s and 4K context.

What context window can CodeLlama 13B Instruct use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, CodeLlama 13B Instruct can safely use up to 16K tokens of context at F16 quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

See all results for NVIDIA DGX Spark 128GB See all hardware for CodeLlama 13B Instruct

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/codellama-13b-instruct-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: