Would CUDA be a better path than Intel Arc Pro A40 6GB 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 Pro A40 6GB?

Q: What should I upgrade first if internlm JanusCoder 14B feels slow on Intel Arc Pro A40 6GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

NO — Won't Fit

F0Won't run

Estimated from fit model

internlm JanusCoder 14B needs ~11.7 GB but Intel Arc Pro A40 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: Very lowStack: StandardBottleneck: Memory capacity

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) — 11.7 GB, exceeds 6.0 GB available

11.7 GB required6.0 GB available

195% VRAM needed

5.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

2.0 tok/s

TTFT

95238 ms

Safe context

Memory

11.7 GB / 6.0 GB

Offload

50%

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsinternlm JanusCoder 14B on Intel Arc Pro A40 6GB
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: 2.0 tok/s decode · 95.2s TTFT (warm) · 5 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 11.7 GB, but this setup only exposes 6.0 GB of usable VRAM.

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

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

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	F	Too heavy	2.4 tok/s	44566 ms	4K
Coding	F	Too heavy	2.0 tok/s	95238 ms	4K
Agentic Coding	F	Too heavy	2.0 tok/s	140800 ms	4K
Reasoning	F	Too heavy	2.0 tok/s	112555 ms	4K
RAG	F	Too heavy	2.0 tok/s	176000 ms	4K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	F0
Q3_K_S	3	6.9 GB	Low	F0
NVFP4	4

Upgrade options

Hardware that runs internlm JanusCoder 14B well

Intel Arc B580 12GBBudget pick

12 GB VRAM (+6)456 GB/s (+264)

Makes the model fit on the accelerator instead of staying completely out of reach.18.7 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$249 MSRP

Intel Arc A770 16GBBest value

16 GB VRAM (+10)560 GB/s (+368)

Makes the model fit on the accelerator instead of staying completely out of reach.29.5 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$349 MSRP

Intel Arc Pro B50 16GBIntel upgrade

16 GB VRAM (+10)224 GB/s (+32)

Makes the model fit on the accelerator instead of staying completely out of reach.14.2 tok/s decode

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

~$399 MSRP

Frequently asked questions

Can Intel Arc Pro A40 6GB run internlm JanusCoder 14B?

No, internlm JanusCoder 14B requires more memory than Intel Arc Pro A40 6GB provides.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 11.7 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 Pro A40 6GB?

On Intel Arc Pro A40 6GB, internlm JanusCoder 14B achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 95238ms using Q4_K_M quantization.

Can Intel Arc Pro A40 6GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Intel Arc Pro A40 6GB receives a F grade with 2.0 tok/s and 4K context.

What context window can internlm JanusCoder 14B use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, internlm JanusCoder 14B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc Pro A40 6GB See all hardware for internlm JanusCoder 14B

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