Would CUDA be a better path than Intel Arc A730M 12GB 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.

Can internlm JanusCoder 14B run on Intel Arc A730M 12GB?

Q: What should I upgrade first if internlm JanusCoder 14B feels slow on Intel Arc A730M 12GB?

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 — With Offload

C47Usable

Estimated from fit model

internlm JanusCoder 14B needs ~12.3 GB VRAM. Intel Arc A730M 12GB has 12.0 GB. With Q4_K_M quantization, expect ~14 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 12.3 GB, 13.8 tok/s, Runs with offload (needs ~0.2 GB host RAM)

12.3 GB required12.0 GB available

103% VRAM needed

0.3 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

13.8 tok/s

TTFT

14057 ms

Safe context

13K

Memory

12.3 GB / 12.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.2 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on Intel Arc A730M 12GB
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: 13.8 tok/s decode · 14.1s TTFT (warm) · 34 tok/s prefill

What limits this setup

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

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload	19.3 tok/s	5478 ms	13K
Coding	C	Runs with offload	13.8 tok/s	14057 ms	13K
Agentic Coding	D	Very compromised	10.6 tok/s	26623 ms	13K
Reasoning	C	Runs with offload	13.8 tok/s	16613 ms	13K
RAG	D	Very compromised	10.6 tok/s	33279 ms	13K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on Intel Arc A730M 12GB (12.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C52
Q3_K_S	3	6.9 GB	Low	C52
NVFP4	4

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

Upgrade options

Hardware that runs internlm JanusCoder 14B well

Intel Arc A770 16GBBudget pick

16 GB VRAM (+4)560 GB/s (+224)

Raises estimated decode speed by about 114%.29.5 tok/s decode

Raises estimated decode speed by about 114%.

Adds memory headroom for longer context windows and future model growth.

~$349 MSRP

Intel Arc Pro B50 16GBBest value

16 GB VRAM (+4)

Adds memory headroom for longer context windows and future model growth.14.2 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$399 MSRP

Intel Arc Pro B60 24GBIntel upgrade

24 GB VRAM (+12)456 GB/s (+120)

Raises estimated decode speed by about 109%.28.8 tok/s decode

Raises estimated decode speed by about 109%.

Adds memory headroom for longer context windows and future model growth.

~$599 MSRP

Frequently asked questions

Can Intel Arc A730M 12GB run internlm JanusCoder 14B?

Yes, Intel Arc A730M 12GB can run internlm JanusCoder 14B with a C grade (Runs with offload). Expected decode speed: 13.8 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 12.3 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 Arc A730M 12GB?

On Intel Arc A730M 12GB, internlm JanusCoder 14B achieves approximately 13.8 tokens per second decode speed with a time-to-first-token of 14057ms using Q4_K_M quantization.

Can Intel Arc A730M 12GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Intel Arc A730M 12GB receives a C grade with 13.8 tok/s and 13K context.

What context window can internlm JanusCoder 14B use on Intel Arc A730M 12GB?

On Intel Arc A730M 12GB, internlm JanusCoder 14B can safely use up to 13K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc A730M 12GB See all hardware for internlm JanusCoder 14B

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