Can Devstral 2 123B Instruct run on NVIDIA GH200 96GB?

YES — Tight Fit

S95Excellent

Estimated from fit model

Devstral 2 123B Instruct needs ~90.9 GB VRAM. NVIDIA GH200 96GB has 96.0 GB. With Q4_K_M quantization, expect ~47 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: HighStack: 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) — 90.9 GB, 47.0 tok/s, Tight fit

90.9 GB required96.0 GB available

95% VRAM used

Fit status

Tight fit

Decode

47.0 tok/s

TTFT

4123 ms

Safe context

31K

Memory

90.9 GB / 96.0 GB

Memory breakdown

Weights75.0 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom9.6 GB

See how fast it feels

See how fast it feelsDevstral 2 123B Instruct on NVIDIA GH200 96GB
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: 47.0 tok/s decode · 4.1s TTFT (warm) · 117 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

Best improvement path

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Tight fit	47.0 tok/s	2249 ms	31K
Coding	S	Tight fit	47.0 tok/s	4123 ms	31K
Agentic Coding	S	Runs with offload (needs ~0.2 GB host RAM)	39.9 tok/s	7063 ms	31K
Reasoning	S	Tight fit	47.0 tok/s	4872 ms	31K
RAG	S	Runs with offload (needs ~0.2 GB host RAM)	39.9 tok/s	8829 ms	31K

Quantization options

How Devstral 2 123B Instruct (123B params) fits at each quantization level on NVIDIA GH200 96GB (96.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.0 GB	Low	S91
Q3_K_S	3	60.3 GB	Low	S91
NVFP4	4	68.9 GB	Medium	S91
Q4_K_MBest for your GPU	4	75.0 GB	Medium	S91
Q5_K_M	5	88.6 GB	High	F0
Q6_K	6	100.9 GB	High	F0
Q8_0	8	131.6 GB	Very High	F0
F16	16	252.2 GB	Maximum	F0

Get started

Copy-paste commands to run Devstral 2 123B Instruct on your machine.

Run

lms load Devstral-2-123B-Instruct-2512 && lms server start

Frequently asked questions

Can NVIDIA GH200 96GB run Devstral 2 123B Instruct?

Yes, NVIDIA GH200 96GB can run Devstral 2 123B Instruct with a S grade (Tight fit). Expected decode speed: 47.0 tok/s.

How much VRAM does Devstral 2 123B Instruct need?

Devstral 2 123B Instruct (123B parameters) requires approximately 90.9 GB of memory with Q4_K_M quantization.

What is the best quantization for Devstral 2 123B Instruct?

The recommended quantization for Devstral 2 123B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Devstral 2 123B Instruct run at on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Devstral 2 123B Instruct achieves approximately 47.0 tokens per second decode speed with a time-to-first-token of 4123ms using Q4_K_M quantization.

Can NVIDIA GH200 96GB run Devstral 2 123B Instruct for coding?

For coding workloads, Devstral 2 123B Instruct on NVIDIA GH200 96GB receives a S grade with 47.0 tok/s and 31K context.

What context window can Devstral 2 123B Instruct use on NVIDIA GH200 96GB?

On NVIDIA GH200 96GB, Devstral 2 123B Instruct can safely use up to 31K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

What should I upgrade first if Devstral 2 123B Instruct feels slow on NVIDIA GH200 96GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

See all results for NVIDIA GH200 96GB See all hardware for Devstral 2 123B Instruct

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/devstral-2-123b-on-gh200-96gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: