Can Laguna XS 2.1 run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A79Great

Estimated from fit model

Laguna XS 2.1 needs ~34.7 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~288 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) — 34.7 GB, 288.3 tok/s, Runs well

34.7 GB required128.0 GB available

27% VRAM used

Fit status

Runs well

Decode

288.3 tok/s

TTFT

672 ms

Safe context

262K

Memory

34.7 GB / 128.0 GB

Memory breakdown

Weights20.4 GB

KV Cache0.6 GB

Runtime0.9 GB

Headroom12.8 GB

See how fast it feels

See how fast it feelsLaguna XS 2.1 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: 288.3 tok/s decode · 672ms TTFT (warm) · 721 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	288.3 tok/s	366 ms	262K
Coding	A	Runs well	288.3 tok/s	672 ms	262K
Agentic Coding	A	Runs well	288.3 tok/s	977 ms	262K
Reasoning	A	Runs well	288.3 tok/s	794 ms	262K
RAG	A	Runs well	288.3 tok/s	1221 ms	262K

Inference speed

Laguna XS 2.1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Laguna XS 2.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~144 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	144.2	Fits
RTX 4090 24GB	24 GB	Q4_K_M	85.8	Offloads
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	79.6	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	72.3	Offloads
RTX 3090 24GB	24 GB	Q4_K_M	68.5	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	66.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	62.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	49.2	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	49.2	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	37.7	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	34.6	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	33.1	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	31.3	Too big
RTX 4070 12GB	12 GB	Q4_K_M	10.9	Too big
RTX 3060 12GB	12 GB	Q4_K_M	6.4	Too big
RTX 4060 8GB	8 GB	Q4_K_M	4.6	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 Laguna XS 2.1 (33.400001525878906B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q1_0_G128	1.125	4.8 GB	Very Low	B70
Q2_0_G128	1.71	8.9 GB	Low	A70
Q2_K	2	13.0 GB	Low	A70
Q3_K_S	3	16.4 GB	Low	A71
NVFP4	4	18.7 GB	Medium	A71
Q4_K_M	4	20.4 GB	Medium	A71
Q5_K_M	5	24.0 GB	High	A71
Q6_K	6	27.4 GB	High	A72
Q8_0	8	35.7 GB	Very High	A73
F16Best for your GPU	16	68.5 GB	Maximum	A79

Get started

Copy-paste commands to run Laguna XS 2.1 on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "poolside/Laguna-XS-2.1" \
  --hf-file "Laguna-XS-2.1-Q4_K_M.gguf" \
  -c 4096 -ngl 99

Your hardware

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

Model	Params	Grade	Decode
Devstral 2 123B Instruct	123B	S	29.2 tok/s
Qwen 3.5 122B A10B	122B	S	81 tok/s
Qwen 3.6 35B A3B	35B	S	256.2 tok/s
Command A 111B	111B	S	32.5 tok/s
Qwen 2.5 VL 72B	72B	S	49.9 tok/s

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run Laguna XS 2.1?

Yes, Intel Data Center GPU Max 1550 128GB can run Laguna XS 2.1 with a A grade (Runs well). Expected decode speed: 288.3 tok/s.

How much VRAM does Laguna XS 2.1 need?

Laguna XS 2.1 (33.400001525878906B parameters) requires approximately 34.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Laguna XS 2.1?

The recommended quantization for Laguna XS 2.1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Laguna XS 2.1 run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Laguna XS 2.1 achieves approximately 288.3 tokens per second decode speed with a time-to-first-token of 672ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run Laguna XS 2.1 for coding?

For coding workloads, Laguna XS 2.1 on Intel Data Center GPU Max 1550 128GB receives a A grade with 288.3 tok/s and 262K context.

What context window can Laguna XS 2.1 use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, Laguna XS 2.1 can safely use up to 262K tokens of context. The model's official context limit is 262K, but available memory constrains the safe maximum.

What should I upgrade first if Laguna XS 2.1 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.

Would CUDA be a better path than Intel Data Center GPU Max 1550 128GB for Laguna XS 2.1?

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.

See all results for Intel Data Center GPU Max 1550 128GB See all hardware for Laguna XS 2.1

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