Can Yi Coder 9B run on Intel Arc Pro B60 24GB?

YES — Runs Great

B61Good

Estimated from fit model

Yi Coder 9B needs ~10.3 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~49 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 10.3 GB, 48.8 tok/s, Runs well

10.3 GB required24.0 GB available

43% VRAM used

Fit status

Runs well

Decode

48.8 tok/s

TTFT

3969 ms

Safe context

131K

Memory

10.3 GB / 24.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsYi Coder 9B on Intel Arc Pro B60 24GB
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: 48.8 tok/s decode · 4.0s TTFT (warm) · 122 tok/s prefill

What limits this setup

The raw memory story may look fine, but the software ecosystem is still a constraint here.

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.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	48.8 tok/s	2165 ms	131K
Coding	B	Runs well	48.8 tok/s	3969 ms	131K
Agentic Coding	B	Runs well	48.8 tok/s	5773 ms	131K
Reasoning	B	Runs well	48.8 tok/s	4691 ms	131K
RAG	B	Runs well	48.8 tok/s	7217 ms	131K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B57
Q3_K_S	3	4.4 GB	Low	B57
NVFP4	4	5.0 GB	Medium	B58
Q4_K_M	4	5.5 GB	Medium	B58
Q5_K_M	5	6.5 GB	High	B58
Q6_K	6	7.4 GB	High	B59
Q8_0	8	9.6 GB	Very High	B60
F16Best for your GPU	16	18.5 GB	Maximum	B62

Get started

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

Run

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

Upgrade-Optionen

Hardware, die Yi Coder 9B gut ausführt

RTX 5090 32GBGünstige Wahl

32 GB VRAM (+8)1792 GB/s (+1336)

Raises estimated decode speed by about 250%.171 tok/s Dekodierung

Raises estimated decode speed by about 250%.

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

This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.

ca. $1,999 MSRP

MacBook Pro M4 Max 36GBBestes Preis-Leistungs-Verhältnis

36 GB Unified (+12)

This setup is broadly balanced for this model.55.8 tok/s Dekodierung

ca. $2,499 MSRP

Frequently asked questions

Can Intel Arc Pro B60 24GB run Yi Coder 9B?

Yes, Intel Arc Pro B60 24GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 48.8 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 10.3 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 Pro B60 24GB?

On Intel Arc Pro B60 24GB, Yi Coder 9B achieves approximately 48.8 tokens per second decode speed with a time-to-first-token of 3969ms using Q4_K_M quantization.

Can Intel Arc Pro B60 24GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on Intel Arc Pro B60 24GB receives a B grade with 48.8 tok/s and 131K context.

What context window can Yi Coder 9B use on Intel Arc Pro B60 24GB?

On Intel Arc Pro B60 24GB, Yi Coder 9B can safely use up to 131K 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 Pro B60 24GB?

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.

Would CUDA be a better path than Intel Arc Pro B60 24GB 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 Pro B60 24GB See all hardware for Yi Coder 9B

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