Can Devstral 2 123B Instruct run on NVIDIA H100 80GB?

Q: What should I upgrade first if Devstral 2 123B Instruct feels slow on NVIDIA H100 80GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

A83Great

Estimated from fit model

Devstral 2 123B Instruct needs ~89.3 GB VRAM. NVIDIA H100 80GB has 80.0 GB. With Q4_K_M quantization, expect ~29 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: Host offload

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) — 89.3 GB, 29.0 tok/s, Very compromised (needs ~7.8 GB host RAM)

89.3 GB required80.0 GB available

112% VRAM needed

9.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~7.8 GB host RAM)

Decode

29.0 tok/s

TTFT

6672 ms

Safe context

Memory

89.3 GB / 80.0 GB

Offload

10%

Memory breakdown

Weights75.0 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsDevstral 2 123B Instruct on NVIDIA H100 80GB
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: 29.0 tok/s decode · 6.7s TTFT (warm) · 73 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly 7.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Very compromised (needs ~5.7 GB host RAM)	30.5 tok/s	3461 ms	4K
Coding	A	Very compromised (needs ~7.8 GB host RAM)	29.0 tok/s	6672 ms	4K
Agentic Coding	A	Very compromised (needs ~11.6 GB host RAM)	26.3 tok/s	10687 ms	4K
Reasoning	A	Very compromised (needs ~7.8 GB host RAM)	29.0 tok/s	7886 ms	4K
RAG	A	Very compromised (needs ~11.6 GB host RAM)	26.3 tok/s

Quantization options

How Devstral 2 123B Instruct (123B params) fits at each quantization level on NVIDIA H100 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.0 GB	Low	S91
Q3_K_SBest for your GPU	3	60.3 GB	Low	S91

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 H100 80GB run Devstral 2 123B Instruct?

Yes, NVIDIA H100 80GB can run Devstral 2 123B Instruct with a A grade (Very compromised (needs ~7.8 GB host RAM)). Expected decode speed: 29.0 tok/s.

How much VRAM does Devstral 2 123B Instruct need?

Devstral 2 123B Instruct (123B parameters) requires approximately 89.3 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 H100 80GB?

On NVIDIA H100 80GB, Devstral 2 123B Instruct achieves approximately 29.0 tokens per second decode speed with a time-to-first-token of 6672ms using Q4_K_M quantization.

Can NVIDIA H100 80GB run Devstral 2 123B Instruct for coding?

For coding workloads, Devstral 2 123B Instruct on NVIDIA H100 80GB receives a A grade with 29.0 tok/s and 4K context.

What context window can Devstral 2 123B Instruct use on NVIDIA H100 80GB?

On NVIDIA H100 80GB, Devstral 2 123B Instruct can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for NVIDIA H100 80GB See all hardware for Devstral 2 123B Instruct

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