Would CUDA be a better path than Intel Arc A750 8GB for Yi Coder 9B Chat?

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 Yi Coder 9B Chat run on Intel Arc A750 8GB?

Q: What should I upgrade first if Yi Coder 9B Chat feels slow on Intel Arc A750 8GB?

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

C50Usable

Estimated from fit model

Yi Coder 9B Chat needs ~8.2 GB VRAM. Intel Arc A750 8GB has 8.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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) — 8.2 GB, 28.2 tok/s, Runs with offload (needs ~0.2 GB host RAM)

8.2 GB required8.0 GB available

102% VRAM needed

0.2 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.2 GB host RAM)

Decode

28.2 tok/s

TTFT

6858 ms

Safe context

12K

Memory

8.2 GB / 8.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on Intel Arc A750 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: 28.2 tok/s decode · 6.9s TTFT (warm) · 71 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	40.1 tok/s	2633 ms	12K
Coding	C	Runs with offload	28.2 tok/s	6858 ms	12K
Agentic Coding	D	Very compromised (needs ~0.8 GB host RAM)	21.9 tok/s	12853 ms	12K
Reasoning	C	Runs with offload (needs ~0.2 GB host RAM)	28.2 tok/s	8105 ms	12K
RAG	D	Very compromised (needs ~0.8 GB host RAM)	21.9 tok/s	16066 ms

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on Intel Arc A750 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C54
Q3_K_S	3	4.4 GB	Low	C53
NVFP4Best for your GPU

Get started

Copy-paste commands to run Yi Coder 9B Chat on your machine.

Run

lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server start

Upgrade options

Hardware that runs Yi Coder 9B Chat well

Intel Arc B570 10GBBudget pick

10 GB VRAM (+2)

Raises estimated decode speed by about 33%.37.4 tok/s decode

Raises estimated decode speed by about 33%.

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

~$219 MSRP

Intel Arc B580 12GBBest value

12 GB VRAM (+4)

Raises estimated decode speed by about 41%.39.9 tok/s decode

Raises estimated decode speed by about 41%.

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

~$249 MSRP

Intel Arc A770 16GBIntel upgrade

16 GB VRAM (+8)560 GB/s (+48)

Raises estimated decode speed by about 63%.45.9 tok/s decode

Raises estimated decode speed by about 63%.

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

~$349 MSRP

Frequently asked questions

Can Intel Arc A750 8GB run Yi Coder 9B Chat?

Yes, Intel Arc A750 8GB can run Yi Coder 9B Chat with a C grade (Runs with offload). Expected decode speed: 28.2 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 8.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B Chat?

The recommended quantization for Yi Coder 9B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 9B Chat run at on Intel Arc A750 8GB?

On Intel Arc A750 8GB, Yi Coder 9B Chat achieves approximately 28.2 tokens per second decode speed with a time-to-first-token of 6858ms using Q4_K_M quantization.

Can Intel Arc A750 8GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on Intel Arc A750 8GB receives a C grade with 28.2 tok/s and 12K context.

What context window can Yi Coder 9B Chat use on Intel Arc A750 8GB?

On Intel Arc A750 8GB, Yi Coder 9B Chat can safely use up to 12K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Yi Coder 9B Chat feels slow on Intel Arc A750 8GB?

See all results for Intel Arc A750 8GB See all hardware for Yi Coder 9B Chat

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