Would CUDA be a better path than Intel Arc A550M 8GB 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 A550M 8GB?

Q: What should I upgrade first if internlm JanusCoder 14B feels slow on Intel Arc A550M 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

YES — With Q2_K

D37Poor

Estimated from fit model

internlm JanusCoder 14B needs ~8.8 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q2_K quantization, expect ~11 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very 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.

internlm JanusCoder 14B at Q4_K_M needs 11.9 GB — too much for Intel Arc A550M 8GB (8.0 GB). Runs at Q2_K (8.8 GB) with low quality.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.9 GB, exceeds 8.0 GB available

11.9 GB required8.0 GB available

149% VRAM needed

3.9 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

4.2 tok/s

TTFT

46173 ms

Safe context

Memory

11.9 GB / 8.0 GB

Offload

30%

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsinternlm JanusCoder 14B on Intel Arc A550M 8GB
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: 4.2 tok/s decode · 46.2s TTFT (warm) · 11 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

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	F	Too heavy	4.9 tok/s	21664 ms	4K
Coding	F	Too heavy	4.2 tok/s	46173 ms	4K
Agentic Coding	F	Too heavy	3.2 tok/s	88180 ms	4K
Reasoning	F	Too heavy	4.2 tok/s	54568 ms	4K
RAG	F	Too heavy	3.2 tok/s	110225 ms	4K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on Intel Arc A550M 8GB (8.0 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

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 B580 12GBBudget pick

12 GB VRAM (+4)456 GB/s (+232)

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 (+8)560 GB/s (+336)

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 A730M 12GBIntel upgrade

12 GB VRAM (+4)336 GB/s (+112)

Makes the model fit on the accelerator instead of staying completely out of reach.13.8 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.

Frequently asked questions

Can Intel Arc A550M 8GB run internlm JanusCoder 14B?

Yes, Intel Arc A550M 8GB can run internlm JanusCoder 14B at Q2_K quantization (Very compromised (needs ~0.5 GB host RAM)). The recommended Q4_K_M requires 11.9 GB which exceeds available memory, but at Q2_K it needs only 8.8 GB. Expected decode speed: 10.5 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 11.9 GB at Q4_K_M quantization. On Intel Arc A550M 8GB, it fits at Q2_K using 8.8 GB.

What is the best quantization for internlm JanusCoder 14B?

The recommended quantization is Q4_K_M, but on Intel Arc A550M 8GB the best fitting quantization is Q2_K, which uses 8.8 GB.

What speed will internlm JanusCoder 14B run at on Intel Arc A550M 8GB?

On Intel Arc A550M 8GB, internlm JanusCoder 14B achieves approximately 10.5 tokens per second decode speed with a time-to-first-token of 18466ms using Q2_K quantization.

Can Intel Arc A550M 8GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Intel Arc A550M 8GB receives a F grade with 4.2 tok/s and 4K context.

What context window can internlm JanusCoder 14B use on Intel Arc A550M 8GB?

On Intel Arc A550M 8GB, internlm JanusCoder 14B can safely use up to 8K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Intel Arc A550M 8GB See all hardware for internlm JanusCoder 14B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm-januscoder-14b-gguf-on-arc-a550m-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: