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URL: https://willitrunai.com/can-run/kimi-linear-48b-a3b-on-max-1550-128gb


Can Kimi Linear 48B A3B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

A81Great
Estimated from fit model

Kimi Linear 48B A3B needs ~44.8 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~69 tok/s.

Runtime: TransformersCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
Share:

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) — 44.8 GB, 68.9 tok/s, Runs well
44.8 GB required128.0 GB available
35% VRAM used

Fit status

Runs well

Decode

68.9 tok/s

TTFT

2812 ms

Safe context

1.0M

Memory

44.8 GB / 128.0 GB

Memory breakdown

Weights29.3 GB
KV Cache0.9 GB
Runtime1.8 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsKimi Linear 48B A3B 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: 68.9 tok/s decode · 2.8s TTFT (warm) · 172 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

WorkloadGradeFitDecodeTTFTContext
ChatARuns well68.9 tok/s1534 ms1.0M
CodingARuns well68.9 tok/s2812 ms1.0M
Agentic CodingARuns well68.9 tok/s4090 ms1.0M
ReasoningARuns well68.9 tok/s3323 ms1.0M
RAGARuns well68.9 tok/s5112 ms1.0M

Quantization options

How Kimi Linear 48B A3B (48B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
18.7 GB
LowA71
Q3_K_S
3
23.5 GB
LowA72
NVFP4
4

Get started

Copy-paste commands to run Kimi Linear 48B A3B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "moonshotai/Kimi-Linear-48B-A3B-Instruct" \ --hf-file "Kimi-Linear-48B-A3B-Instruct-Q4_K_M.gguf" \ -c 4096 -ngl 99

Your hardware

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

ModelParamsGradeDecodeCapabilities
👁 Mistral
Devstral 2 123B Instruct
123BS29.2 tok/s
👁 Alibaba
Qwen 3.5 122B A10B
122BS

Frequently asked questions

See all results for Intel Data Center GPU Max 1550 128GBSee all hardware for Kimi Linear 48B A3B
26.9 GB
Medium
A72
Q4_K_M
4
29.3 GB
MediumA73
Q5_K_M
5
34.6 GB
HighA74
Q6_K
6
39.4 GB
HighA75
Q8_0
8
51.4 GB
Very HighA77
F16Best for your GPU
16
98.4 GB
MaximumA80
81 tok/s
👁 Mistral
Mistral Small 4 119B
119BS87.9 tok/s
👁 OpenAI
GPT-OSS 120B
117BS30.7 tok/s
👁 Cohere
Command A 111B
111BS32.5 tok/s