Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for Codestral 2 25.08?

Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.

Can Codestral 2 25.08 run on Intel Data Center GPU Max 1550 128GB?

Q: What should I upgrade first if Codestral 2 25.08 feels slow on Intel Data Center GPU Max 1550 128GB?

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.

YES — Runs Great

A81Great

Estimated from fit model

Codestral 2 25.08 needs ~29.6 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~152 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) — 29.6 GB, 151.8 tok/s, Runs well

29.6 GB required128.0 GB available

23% VRAM used

Fit status

Runs well

Decode

151.8 tok/s

TTFT

1275 ms

Safe context

256K

Memory

29.6 GB / 128.0 GB

Memory breakdown

Weights13.4 GB

KV Cache2.4 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsCodestral 2 25.08 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: 151.8 tok/s decode · 1.3s TTFT (warm) · 380 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	A	Runs well	151.8 tok/s	696 ms	256K
Coding	A	Runs well	151.8 tok/s	1275 ms	256K
Agentic Coding	A	Runs well	151.8 tok/s	1855 ms	256K
Reasoning	A	Runs well	151.8 tok/s	1507 ms	256K
RAG	A	Runs well	151.8 tok/s	2319 ms	256K

Inference speed

Codestral 2 25.08 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Codestral 2 25.08 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~96 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?
RTX 5090 32GB	32 GB	Q4_K_M	96.2	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	52.0	Fits

Quantization options

How Codestral 2 25.08 (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	A73
Q3_K_S	3	10.8 GB	Low	A73
NVFP4	4

Get started

Copy-paste commands to run Codestral 2 25.08 on your machine.

Run

lms load codestral-2508 && lms server start

Your hardware

More models your Intel Data Center GPU Max 1550 128GB can run

Model	Params	Grade	Decode	Capabilities
Devstral 2 123B Instruct	123B	S	29.2 tok/s
Qwen3-Coder 30B A3B Instruct	30.5B	S

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Codestral 2 25.08?

Yes, Intel Data Center GPU Max 1550 128GB can run Codestral 2 25.08 with a A grade (Runs well). Expected decode speed: 151.8 tok/s.

How much VRAM does Codestral 2 25.08 need?

Codestral 2 25.08 (22B parameters) requires approximately 29.6 GB of memory with Q4_K_M quantization.

What is the best quantization for Codestral 2 25.08?

The recommended quantization for Codestral 2 25.08 is Q4_K_M, which balances quality and memory efficiency.

What speed will Codestral 2 25.08 run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Codestral 2 25.08 achieves approximately 151.8 tokens per second decode speed with a time-to-first-token of 1275ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Codestral 2 25.08 for coding?

For coding workloads, Codestral 2 25.08 on Intel Data Center GPU Max 1550 128GB receives a A grade with 151.8 tok/s and 256K context.

What context window can Codestral 2 25.08 use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Codestral 2 25.08 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 Intel Data Center GPU Max 1550 128GB See all hardware for Codestral 2 25.08

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