Can Codestral 22B v0.1 run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

C47Usable

○Estimated from fit model

Codestral 22B v0.1 needs ~29.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~150 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) — 29.7 GB, 150.2 tok/s, Runs well

29.7 GB required128.0 GB available

23% VRAM used

Fit status

Runs well

Decode

150.2 tok/s

TTFT

1289 ms

Safe context

626K

Memory

29.7 GB / 128.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.6 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsCodestral 22B v0.1 on Intel Data Center GPU Max 1550 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: 150.2 tok/s decode · 1.3s TTFT (warm) · 376 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

Runtime ecosystem is narrower than CUDA

Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.

Best improvement path

Prefer CUDA if you want the path of least resistance

If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	150.2 tok/s	703 ms	626K
Coding	C	Runs well	150.2 tok/s	1289 ms	626K
Agentic Coding	C	Runs well	150.2 tok/s	1875 ms	626K
Reasoning	C	Runs well	150.2 tok/s	1523 ms	626K
RAG	C	Runs well	150.2 tok/s	2343 ms	626K

Quantization options

How Codestral 22B v0.1 (22B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	8.6 GB	Low	D38
Q3_K_S	3	10.8 GB	Low	D38
NVFP4	4	12.3 GB	Medium	D38
Q4_K_M	4	13.4 GB	Medium	D38
Q5_K_M	5	15.8 GB	High	D39
Q6_K	6	18.0 GB	High	D39
Q8_0	8	23.5 GB	Very High	D39
F16Best for your GPU	16	45.1 GB	Maximum	C43

Get started

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

Run

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

Frequently asked questions

See all results for Intel Data Center GPU Max 1550 128GB See all hardware for Codestral 22B v0.1

URL: https://willitrunai.com/can-run/hf-lmstudio-community--codestral-22b-v0-1-gguf-on-max-1550-128gb

⇱ Codestral 22B v0.1 on Intel Data Center GPU Max 1550 128GB?…