Can Mamba Codestral 7B v0.1 run on NVIDIA DGX Spark 128GB?

YES — Runs Great

C43Usable

Estimated from fit model

Mamba Codestral 7B v0.1 needs ~19.0 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~44 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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) — 19.0 GB, 44.1 tok/s, Runs well

19.0 GB required108.8 GB available

17% VRAM used

Fit status

Runs well

Decode

44.1 tok/s

TTFT

4389 ms

Safe context

1.8M

Memory

19.0 GB / 108.8 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsMamba Codestral 7B v0.1 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: 44.1 tok/s decode · 4.4s TTFT (warm) · 110 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	44.1 tok/s	2394 ms	1.8M
Coding	C	Runs well	44.1 tok/s	4389 ms	1.8M
Agentic Coding	C	Runs well	44.1 tok/s	6383 ms	1.8M
Reasoning	C	Runs well	44.1 tok/s	5186 ms	1.8M
RAG	C	Runs well	44.1 tok/s	7979 ms	1.8M

Quantization options

How Mamba Codestral 7B v0.1 (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 Mamba Codestral 7B v0.1 on your machine.

Run

lms load hf-gabriellarson--mamba-codestral-7b-v0-1-gguf && lms server start

升级选项

能流畅运行 Mamba Codestral 7B v0.1 的硬件

NVIDIA H200 141GB经济之选

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

Raises estimated decode speed by about 122%.98 tok/s 解码

Raises estimated decode speed by about 122%.

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

~$30,000 MSRP

NVIDIA H200 PCIe 141GB最佳性价比

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

Raises estimated decode speed by about 122%.98 tok/s 解码

Raises estimated decode speed by about 122%.

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

~$30,000 MSRP

NVIDIA B200 180GBNVIDIA 升级

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

Raises estimated decode speed by about 122%.98 tok/s 解码

Raises estimated decode speed by about 122%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Mamba Codestral 7B v0.1?

Yes, NVIDIA DGX Spark 128GB can run Mamba Codestral 7B v0.1 with a C grade (Runs well). Expected decode speed: 44.1 tok/s.

How much VRAM does Mamba Codestral 7B v0.1 need?

Mamba Codestral 7B v0.1 (7B parameters) requires approximately 19.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Mamba Codestral 7B v0.1?

The recommended quantization for Mamba Codestral 7B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Mamba Codestral 7B v0.1 run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Mamba Codestral 7B v0.1 achieves approximately 44.1 tokens per second decode speed with a time-to-first-token of 4389ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Mamba Codestral 7B v0.1 for coding?

For coding workloads, Mamba Codestral 7B v0.1 on NVIDIA DGX Spark 128GB receives a C grade with 44.1 tok/s and 1.8M context.

What context window can Mamba Codestral 7B v0.1 use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Mamba Codestral 7B v0.1 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 Mamba Codestral 7B v0.1?

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 Mamba Codestral 7B v0.1

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-gabriellarson--mamba-codestral-7b-v0-1-gguf-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: