Can Codestral 22B v0.1 run on NVIDIA DGX Spark 128GB?

YES — Runs Great

C41Usable

Estimated from fit model

Codestral 22B v0.1 needs ~30.3 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~12 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) — 30.3 GB, 12.2 tok/s, Runs well

30.3 GB required108.8 GB available

28% VRAM used

Fit status

Runs well

Decode

12.2 tok/s

TTFT

15861 ms

Safe context

503K

Memory

30.3 GB / 108.8 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsCodestral 22B 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: 12.2 tok/s decode · 15.9s TTFT (warm) · 31 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	12.2 tok/s	8652 ms	503K
Coding	C	Runs well	12.2 tok/s	15861 ms	503K
Agentic Coding	C	Runs well	12.2 tok/s	23071 ms	503K
Reasoning	C	Runs well	12.2 tok/s	18745 ms	503K
RAG	C	Runs well	12.2 tok/s	28839 ms	503K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	D39
Q3_K_S	3	10.8 GB	Low	D39
NVFP4	4	12.3 GB	Medium	D40
Q4_K_M	4	13.4 GB	Medium	D40
Q5_K_M	5	15.8 GB	High	C40
Q6_K	6	18.0 GB	High	C40
Q8_0	8	23.5 GB	Very High	C41
F16Best for your GPU	16	45.1 GB	Maximum	C46

Get started

Copy-paste commands to run Codestral 22B v0.1 on your machine.

Run

lms load hf-sanctumai--codestral-22b-v0-1-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Codestral 22B v0.1

NVIDIA H200 141GBOpción económica

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

Sube la velocidad estimada de decodificación alrededor de un 2362%.300.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 2362%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMejor relación calidad-precio

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

Sube la velocidad estimada de decodificación alrededor de un 2362%.300.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 2362%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

NVIDIA B200 180GBMejora NVIDIA

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

Sube la velocidad estimada de decodificación alrededor de un 2425%.308 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 2425%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Codestral 22B v0.1?

Yes, NVIDIA DGX Spark 128GB can run Codestral 22B v0.1 with a C grade (Runs well). Expected decode speed: 12.2 tok/s.

How much VRAM does Codestral 22B v0.1 need?

Codestral 22B v0.1 (22B parameters) requires approximately 30.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 22B v0.1?

The recommended quantization for Codestral 22B v0.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 22B v0.1 run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Codestral 22B v0.1 achieves approximately 12.2 tokens per second decode speed with a time-to-first-token of 15861ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Codestral 22B v0.1 for coding?

For coding workloads, Codestral 22B v0.1 on NVIDIA DGX Spark 128GB receives a C grade with 12.2 tok/s and 503K context.

What context window can Codestral 22B v0.1 use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Codestral 22B v0.1 can safely use up to 503K 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 Codestral 22B 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 Codestral 22B v0.1

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