Will It Run AI

Can internlm JanusCoder 14B run on Intel Data Center GPU Max 1550 128GB?

YES — Runs Great

C45Usable
Estimated from fit model

internlm JanusCoder 14B needs ~23.9 GB VRAM. Intel Data Center GPU Max 1550 128GB has 128.0 GB. With Q4_K_M quantization, expect ~196 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced
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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) — 23.9 GB, 196.0 tok/s, Runs well
23.9 GB required128.0 GB available
19% VRAM used

Fit status

Runs well

Decode

196.0 tok/s

TTFT

988 ms

Safe context

1.0M

Memory

23.9 GB / 128.0 GB

Memory breakdown

Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom12.8 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B 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: 196.0 tok/s decode · 988ms TTFT (warm) · 490 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
ChatCRuns well196.0 tok/s539 ms1.0M
CodingCRuns well196.0 tok/s988 ms1.0M
Agentic CodingCRuns well196.0 tok/s1437 ms1.0M
ReasoningCRuns well196.0 tok/s1167 ms1.0M
RAGCRuns well196.0 tok/s1796 ms1.0M

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on Intel Data Center GPU Max 1550 128GB (128.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
5.5 GB
LowD38
Q3_K_S
3
6.9 GB
LowD38
NVFP4
4
7.8 GB
MediumD38
Q4_K_M
4
8.5 GB
MediumD38
Q5_K_M
5
10.1 GB
HighD38
Q6_K
6
11.5 GB
HighD38
Q8_0
8
15.0 GB
Very HighD38
F16Best for your GPU
16
28.7 GB
MaximumD40

Get started

Copy-paste commands to run internlm JanusCoder 14B on your machine.

Run

lms load hf-bartowski--internlm-januscoder-14b-gguf && lms server start

Frequently asked questions

Can Intel Data Center GPU Max 1550 128GB run internlm JanusCoder 14B?

Yes, Intel Data Center GPU Max 1550 128GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 196.0 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 23.9 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm JanusCoder 14B?

The recommended quantization for internlm JanusCoder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm JanusCoder 14B run at on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, internlm JanusCoder 14B achieves approximately 196.0 tokens per second decode speed with a time-to-first-token of 988ms using Q4_K_M quantization.

Can Intel Data Center GPU Max 1550 128GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Intel Data Center GPU Max 1550 128GB receives a C grade with 196.0 tok/s and 1.0M context.

What context window can internlm JanusCoder 14B use on Intel Data Center GPU Max 1550 128GB?

On Intel Data Center GPU Max 1550 128GB, internlm JanusCoder 14B can safely use up to 1.0M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if internlm JanusCoder 14B 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 internlm JanusCoder 14B?

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 128GBSee all hardware for internlm JanusCoder 14B
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