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

YES — Runs Great

B68Good

Estimated from fit model

Codestral Mamba 7B needs ~18.7 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) — 18.7 GB, 44.1 tok/s, Runs well

18.7 GB required108.8 GB available

17% VRAM used

Fit status

Runs well

Decode

44.1 tok/s

TTFT

4389 ms

Safe context

262K

Memory

18.7 GB / 108.8 GB

Memory breakdown

Weights4.3 GB

KV Cache0.5 GB

Runtime0.9 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsCodestral Mamba 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: 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	B	Runs well	44.1 tok/s	2394 ms	262K
Coding	B	Runs well	44.1 tok/s	4389 ms	262K
Agentic Coding	B	Runs well	44.1 tok/s	6383 ms	262K
Reasoning	B	Runs well	44.1 tok/s	5186 ms	262K
RAG	B	Runs well	44.1 tok/s	7979 ms	262K

Quantization options

How Codestral Mamba 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	B64
Q3_K_S	3	3.4 GB	Low	B64
NVFP4	4	3.9 GB	Medium	B64
Q4_K_M	4	4.3 GB	Medium	B64
Q5_K_M	5	5.0 GB	High	B64
Q6_K	6	5.7 GB	High	B64
Q8_0	8	7.5 GB	Very High	B64
F16Best for your GPU	16	14.3 GB	Maximum	B65

Get started

Copy-paste commands to run Codestral Mamba 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "mistralai/Mamba-Codestral-7B-v0.1" \
  --hf-file "Mamba-Codestral-7B-v0.1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Upgrade-Optionen

Hardware, die Codestral Mamba 7B gut ausführt

NVIDIA H200 141GBGünstige Wahl

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

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

Raises estimated decode speed by about 122%.

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

ca. $30,000 MSRP

NVIDIA H200 PCIe 141GBBestes Preis-Leistungs-Verhältnis

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

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

Raises estimated decode speed by about 122%.

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

ca. $30,000 MSRP

NVIDIA B200 180GBNVIDIA-Upgrade

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

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

Raises estimated decode speed by about 122%.

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

ca. $30,000 MSRP

Frequently asked questions

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

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

How much VRAM does Codestral Mamba 7B need?

Codestral Mamba 7B (7B parameters) requires approximately 18.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral Mamba 7B?

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

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

On NVIDIA DGX Spark 128GB, Codestral Mamba 7B 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 Codestral Mamba 7B for coding?

For coding workloads, Codestral Mamba 7B on NVIDIA DGX Spark 128GB receives a B grade with 44.1 tok/s and 262K context.

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

On NVIDIA DGX Spark 128GB, Codestral Mamba 7B can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Codestral Mamba 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 Codestral Mamba 7B

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