Can Yi Coder 9B run on Intel Arc A580 8GB?

BARELY — Tight on Memory

C53Usable

Estimated from fit model

Yi Coder 9B needs ~8.7 GB VRAM. Intel Arc A580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~32 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) — 8.7 GB, 31.6 tok/s, Very compromised (needs ~0.4 GB host RAM)

8.7 GB required8.0 GB available

109% VRAM needed

0.7 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

31.6 tok/s

TTFT

6130 ms

Safe context

Memory

8.7 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsYi Coder 9B on Intel Arc A580 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: 31.6 tok/s decode · 6.1s TTFT (warm) · 79 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	B	Runs with offload	49.7 tok/s	2125 ms	9K
Coding	C	Very compromised (needs ~0.4 GB host RAM)	31.6 tok/s	6130 ms	9K
Agentic Coding	F	Too heavy	22.7 tok/s	12391 ms	9K
Reasoning	C	Very compromised (needs ~0.4 GB host RAM)	31.6 tok/s	7244 ms	9K
RAG	F	Too heavy	22.7 tok/s	15489 ms	9K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on Intel Arc A580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B66
Q3_K_S	3	4.4 GB	Low	B65
NVFP4Best for your GPU	4	5.0 GB	Medium	B65
Q4_K_M	4	5.5 GB	Medium	F0
Q5_K_M	5	6.5 GB	High	F0
Q6_K	6	7.4 GB	High	F0
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Get started

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

Run

lms load Yi-Coder-9B-Chat && lms server start

Opciones de mejora

Hardware que ejecuta bien Yi Coder 9B

Intel Arc B570 10GBOpción económica

10 GB VRAM (+2)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.40.6 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 28%.

~$219 MSRP

Intel Arc B580 12GBMejor relación calidad-precio

12 GB VRAM (+4)

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.43.4 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 37%.

~$249 MSRP

Intel Arc A770 16GBMejora Intel

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

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.49.9 tok/s decodificación

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

Sube la velocidad estimada de decodificación alrededor de un 58%.

~$349 MSRP

Frequently asked questions

Can Intel Arc A580 8GB run Yi Coder 9B?

Yes, Intel Arc A580 8GB can run Yi Coder 9B with a C grade (Very compromised (needs ~0.4 GB host RAM)). Expected decode speed: 31.6 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 8.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B?

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

What speed will Yi Coder 9B run at on Intel Arc A580 8GB?

On Intel Arc A580 8GB, Yi Coder 9B achieves approximately 31.6 tokens per second decode speed with a time-to-first-token of 6130ms using Q4_K_M quantization.

Can Intel Arc A580 8GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on Intel Arc A580 8GB receives a C grade with 31.6 tok/s and 9K context.

What context window can Yi Coder 9B use on Intel Arc A580 8GB?

On Intel Arc A580 8GB, Yi Coder 9B can safely use up to 9K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Yi Coder 9B feels slow on Intel Arc A580 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.

Would CUDA be a better path than Intel Arc A580 8GB for Yi Coder 9B?

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.

See all results for Intel Arc A580 8GB See all hardware for Yi Coder 9B

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