Would CUDA be a better path than Intel Arc Pro A40 6GB for CodeNinja 1.0 OpenChat 7B i1?

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 CodeNinja 1.0 OpenChat 7B i1 run on Intel Arc Pro A40 6GB?

Q: What should I upgrade first if CodeNinja 1.0 OpenChat 7B i1 feels slow on Intel Arc Pro A40 6GB?

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.

BARELY — Tight on Memory

D37Poor

Estimated from fit model

CodeNinja 1.0 OpenChat 7B i1 needs ~6.6 GB VRAM. Intel Arc Pro A40 6GB has 6.0 GB. With Q4_K_M quantization, expect ~14 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 6.6 GB, 13.6 tok/s, Very compromised (needs ~0.4 GB host RAM)

6.6 GB required6.0 GB available

110% VRAM needed

0.6 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

13.6 tok/s

TTFT

14275 ms

Safe context

Memory

6.6 GB / 6.0 GB

Offload

10%

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

See how fast it feelsCodeNinja 1.0 OpenChat 7B i1 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: 13.6 tok/s decode · 14.3s TTFT (warm) · 34 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	C	Runs with offload (needs ~0.1 GB host RAM)	15.5 tok/s	6801 ms	4K
Coding	D	Very compromised (needs ~0.4 GB host RAM)	13.6 tok/s	14275 ms	4K
Agentic Coding	F	Too heavy	10.6 tok/s	26579 ms	4K
Reasoning	D	Very compromised (needs ~0.4 GB host RAM)	13.6 tok/s	16870 ms	4K
RAG	F	Too heavy	10.6 tok/s	33224 ms

Quantization options

How CodeNinja 1.0 OpenChat 7B i1 (7B params) fits at each quantization level on Intel Arc Pro A40 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C54
Q3_K_SBest for your GPU	3	3.4 GB	Low	C53

Get started

Copy-paste commands to run CodeNinja 1.0 OpenChat 7B i1 on your machine.

Run

lms load hf-mradermacher--codeninja-1-0-openchat-7b-i1-gguf && lms server start

Upgrade options

Hardware that runs CodeNinja 1.0 OpenChat 7B i1 well

Intel Arc A580 8GBBudget pick

8 GB VRAM (+2)512 GB/s (+320)

Removes host-memory offload, which is usually the single biggest latency and throughput win.58.8 tok/s decode

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

Raises estimated decode speed by about 332%.

~$179 MSRP

Intel Arc B570 10GBBest value

10 GB VRAM (+4)380 GB/s (+188)

Removes host-memory offload, which is usually the single biggest latency and throughput win.48.1 tok/s decode

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

Raises estimated decode speed by about 254%.

~$219 MSRP

Intel Arc B580 12GBIntel upgrade

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

Removes host-memory offload, which is usually the single biggest latency and throughput win.51.3 tok/s decode

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

Raises estimated decode speed by about 277%.

~$249 MSRP

Frequently asked questions

Can Intel Arc Pro A40 6GB run CodeNinja 1.0 OpenChat 7B i1?

Yes, Intel Arc Pro A40 6GB can run CodeNinja 1.0 OpenChat 7B i1 with a D grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 13.6 tok/s.

How much VRAM does CodeNinja 1.0 OpenChat 7B i1 need?

CodeNinja 1.0 OpenChat 7B i1 (7B parameters) requires approximately 6.6 GB of memory with Q4_K_M quantization.

What is the best quantization for CodeNinja 1.0 OpenChat 7B i1?

The recommended quantization for CodeNinja 1.0 OpenChat 7B i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will CodeNinja 1.0 OpenChat 7B i1 run at on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, CodeNinja 1.0 OpenChat 7B i1 achieves approximately 13.6 tokens per second decode speed with a time-to-first-token of 14275ms using Q4_K_M quantization.

Can Intel Arc Pro A40 6GB run CodeNinja 1.0 OpenChat 7B i1 for coding?

For coding workloads, CodeNinja 1.0 OpenChat 7B i1 on Intel Arc Pro A40 6GB receives a D grade with 13.6 tok/s and 4K context.

What context window can CodeNinja 1.0 OpenChat 7B i1 use on Intel Arc Pro A40 6GB?

On Intel Arc Pro A40 6GB, CodeNinja 1.0 OpenChat 7B i1 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 CodeNinja 1.0 OpenChat 7B i1

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