Can CodeLlama 7B Instruct run on NVIDIA DGX Spark 128GB?

YES — Runs Great

B68Good

Estimated from fit model

CodeLlama 7B Instruct needs ~26.3 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~38 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 26.3 GB, 38.4 tok/s, Runs well

26.3 GB required108.8 GB available

24% VRAM used

Fit status

Runs well

Decode

38.4 tok/s

TTFT

5047 ms

Safe context

16K

Memory

26.3 GB / 108.8 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsCodeLlama 7B 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: 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	B	Runs well	38.4 tok/s	2753 ms	16K
Coding	B	Runs well	38.4 tok/s	5047 ms	16K
Agentic Coding	B	Runs well	38.4 tok/s	7341 ms	16K
Reasoning	B	Runs well	38.4 tok/s	5964 ms	16K
RAG	B	Runs well	38.4 tok/s	9176 ms	16K

Quantization options

How CodeLlama 7B Instruct (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	3.9 GB	Medium	B63
Q4_K_M	4	4.3 GB	Medium	B63
Q5_K_M	5	5.0 GB	High	B63
Q6_K	6	5.7 GB	High	B63
Q8_0	8	7.5 GB	Very High	B63
F16Best for your GPU	16	14.3 GB	Maximum	B63

Get started

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

Run

lms load CodeLlama-7b-Instruct-hf && lms server start

Opções de upgrade

Hardware que roda bem CodeLlama 7B Instruct

NVIDIA H200 141GBOpção econômica

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

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

Raises estimated decode speed by about 155%.

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 155%.98 tok/s decodificação

Raises estimated decode speed by about 155%.

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 155%.98 tok/s decodificação

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 CodeLlama 7B Instruct?

Yes, NVIDIA DGX Spark 128GB can run CodeLlama 7B Instruct with a B grade (Runs well). Expected decode speed: 38.4 tok/s.

How much VRAM does CodeLlama 7B Instruct need?

CodeLlama 7B Instruct (7B parameters) requires approximately 26.3 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeLlama 7B Instruct?

The recommended quantization for CodeLlama 7B Instruct is Q4_K_M, which balances quality and memory efficiency.

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

On NVIDIA DGX Spark 128GB, CodeLlama 7B Instruct achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5047ms using Q4_K_M quantization.

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

For coding workloads, CodeLlama 7B Instruct on NVIDIA DGX Spark 128GB receives a B grade with 38.4 tok/s and 16K context.

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

On NVIDIA DGX Spark 128GB, CodeLlama 7B Instruct can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for CodeLlama 7B 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.

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

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