Can speechless zephyr code functionary 7b run on NVIDIA DGX Spark 128GB?

YES — Runs Great

C43Usable

Estimated from fit model

speechless zephyr code functionary 7b needs ~19.3 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) — 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 feelsspeechless zephyr code functionary 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	C	Runs well	38.4 tok/s	2753 ms	1.8M
Coding	C	Runs well	38.4 tok/s	5047 ms	1.8M
Agentic Coding	C	Runs well	38.4 tok/s	7341 ms	1.8M
Reasoning	C	Runs well	38.4 tok/s	5964 ms	1.8M
RAG	C	Runs well	38.4 tok/s	9176 ms	1.8M

Quantization options

How speechless zephyr code functionary 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 speechless zephyr code functionary 7b on your machine.

Run

lms load hf-uukuguy--speechless-zephyr-code-functionary-7b && lms server start

Upgrade options

Hardware that runs speechless zephyr code functionary 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 speechless zephyr code functionary 7b?

Yes, NVIDIA DGX Spark 128GB can run speechless zephyr code functionary 7b with a C grade (Runs well). Expected decode speed: 38.4 tok/s.

How much VRAM does speechless zephyr code functionary 7b need?

speechless zephyr code functionary 7b (7B parameters) requires approximately 19.3 GB of memory with Q4_K_M quantization.

What is the best quantization for speechless zephyr code functionary 7b?

The recommended quantization for speechless zephyr code functionary 7b is Q4_K_M, which balances quality and memory efficiency.

What speed will speechless zephyr code functionary 7b run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, speechless zephyr code functionary 7b 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 speechless zephyr code functionary 7b for coding?

For coding workloads, speechless zephyr code functionary 7b on NVIDIA DGX Spark 128GB receives a C grade with 38.4 tok/s and 1.8M context.

What context window can speechless zephyr code functionary 7b use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, speechless zephyr code functionary 7b can safely use up to 1.8M tokens of context. 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 speechless zephyr code functionary 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 speechless zephyr code functionary 7b

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<iframe src="https://willitrunai.com/embed/hf-uukuguy--speechless-zephyr-code-functionary-7b-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:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

38.4 tok/s

2753 ms

1.8M

Coding

Runs well

38.4 tok/s

5047 ms

1.8M

Agentic Coding

Runs well

38.4 tok/s

7341 ms

1.8M

Reasoning

Runs well

38.4 tok/s

5964 ms

1.8M

RAG

Runs well

38.4 tok/s

9176 ms

1.8M

Quantization options

How speechless zephyr code functionary 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