Can Devstral 2 123B Instruct run on AMD Instinct MI300X 192GB?

YES — Runs Great

S95Excellent

Estimated from fit model

Devstral 2 123B Instruct needs ~100.5 GB VRAM. AMD Instinct MI300X 192GB has 192.0 GB. With Q4_K_M quantization, expect ~60 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 100.5 GB, 59.9 tok/s, Runs well

100.5 GB required192.0 GB available

52% VRAM used

Fit status

Runs well

Decode

59.9 tok/s

TTFT

3231 ms

Safe context

256K

Memory

100.5 GB / 192.0 GB

Memory breakdown

Weights75.0 GB

KV Cache5.4 GB

Runtime0.9 GB

Headroom19.2 GB

See how fast it feels

See how fast it feelsDevstral 2 123B Instruct on AMD Instinct MI300X 192GB
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: 59.9 tok/s decode · 3.2s TTFT (warm) · 150 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	S	Runs well	59.9 tok/s	1762 ms	256K
Coding	S	Runs well	59.9 tok/s	3231 ms	256K
Agentic Coding	S	Runs well	59.9 tok/s	4700 ms	256K
Reasoning	S	Runs well	59.9 tok/s	3818 ms	256K
RAG	S	Runs well	59.9 tok/s	5875 ms	256K

Inference speed

Devstral 2 123B Instruct inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Devstral 2 123B Instruct at Q4_K_M across popular GPUs and Apple Silicon, including multi-GPU rigs, using the fastest local runtime per device. Fastest is MacBook Pro M4 Max 128GB at ~8 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	8.2	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	8.1	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	6.3	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	6.0	Offloads
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	3.9	Too big
2× RX 7900 XTX 24GB	48 GB	Q4_K_M	3.7	Too big
2× RTX 4090 24GB	48 GB	Q4_K_M	2.6	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	2.5	Too big
2× RTX 3090 24GB	48 GB	Q4_K_M	2.2	Too big
RTX 5090 32GB	32 GB	Q4_K_M	2.0	Too big
RTX 4090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4080 Super 16GB	16 GB	Q4_K_M	2.0	Too big
RTX 3090 24GB	24 GB	Q4_K_M	2.0	Too big
RTX 4070 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 3060 12GB	12 GB	Q4_K_M	2.0	Too big
RTX 4060 8GB	8 GB	Q4_K_M	2.0	Too big
RX 7900 XTX 24GB	24 GB	Q4_K_M	2.0	Too big
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	2.0	Too big
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	2.0	Too big
4× RTX 3060 12GB	48 GB	Q4_K_M	2.0	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Devstral 2 123B Instruct (123B params) fits at each quantization level on AMD Instinct MI300X 192GB (192.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	48.0 GB	Low	A84
Q3_K_S	3	60.3 GB	Low	S86
NVFP4	4	68.9 GB	Medium	S87
Q4_K_M	4	75.0 GB	Medium	S87
Q5_K_M	5	88.6 GB	High	S89
Q6_K	6	100.9 GB	High	S90
Q8_0Best for your GPU	8	131.6 GB	Very High	S91
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 AMD Instinct MI300X 192GB run Devstral 2 123B Instruct?

Yes, AMD Instinct MI300X 192GB can run Devstral 2 123B Instruct with a S grade (Runs well). Expected decode speed: 59.9 tok/s.

How much VRAM does Devstral 2 123B Instruct need?

Devstral 2 123B Instruct (123B parameters) requires approximately 100.5 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 AMD Instinct MI300X 192GB?

On AMD Instinct MI300X 192GB, Devstral 2 123B Instruct achieves approximately 59.9 tokens per second decode speed with a time-to-first-token of 3231ms using Q4_K_M quantization.

Can AMD Instinct MI300X 192GB run Devstral 2 123B Instruct for coding?

For coding workloads, Devstral 2 123B Instruct on AMD Instinct MI300X 192GB receives a S grade with 59.9 tok/s and 256K context.

What context window can Devstral 2 123B Instruct use on AMD Instinct MI300X 192GB?

On AMD Instinct MI300X 192GB, Devstral 2 123B Instruct can safely use up to 256K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.

See all results for AMD Instinct MI300X 192GB See all hardware for Devstral 2 123B Instruct

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