Can Yi Coder 9B run on MacBook Pro M3 Pro 18GB?

YES — Runs Great

B64Good

Estimated from fit model

Yi Coder 9B needs ~9.8 GB VRAM. MacBook Pro M3 Pro 18GB has 13.0 GB. With Q4_K_M quantization, expect ~22 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth

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) — 9.8 GB, 21.7 tok/s, Runs well

9.8 GB required13.0 GB available

75% VRAM used

Fit status

Runs well

Decode

21.7 tok/s

TTFT

8926 ms

Safe context

51K

Memory

9.8 GB / 13.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.9 GB

See how fast it feels

See how fast it feelsYi Coder 9B on MacBook Pro M3 Pro 18GB
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: 21.7 tok/s decode · 8.9s TTFT (warm) · 54 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Shared-memory contention still exists

The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs well	21.7 tok/s	4868 ms	51K
Coding	B	Runs well	21.7 tok/s	8926 ms	51K
Agentic Coding	B	Tight fit	21.7 tok/s	12983 ms	51K
Reasoning	B	Runs well	21.7 tok/s	10548 ms	51K
RAG	B	Tight fit	21.7 tok/s	16228 ms	51K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on MacBook Pro M3 Pro 18GB (13.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B61
Q3_K_S	3	4.4 GB	Low	B62
NVFP4	4	5.0 GB	Medium	B63
Q4_K_M	4	5.5 GB	Medium	B64
Q5_K_M	5	6.5 GB	High	B64
Q6_K	6	7.4 GB	High	B64
Q8_0Best for your GPU	8	9.6 GB	Very High	B63
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

アップグレードオプション

Yi Coder 9Bを快適に動かすハードウェア

Intel Arc A770 16GBコスパ重視

560 GB/s (+410)

Raises estimated decode speed by about 130%.49.9 tok/sデコード

Raises estimated decode speed by about 130%.

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

〜$349 MSRP

RTX 5060 Ti 16GBコスパ最良

448 GB/s (+298)

Raises estimated decode speed by about 153%.55 tok/sデコード

Raises estimated decode speed by about 153%.

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

〜$449 MSRP

Frequently asked questions

Can MacBook Pro M3 Pro 18GB run Yi Coder 9B?

Yes, MacBook Pro M3 Pro 18GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 21.7 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 9.8 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 MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Yi Coder 9B achieves approximately 21.7 tokens per second decode speed with a time-to-first-token of 8926ms using Q4_K_M quantization.

Can MacBook Pro M3 Pro 18GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on MacBook Pro M3 Pro 18GB receives a B grade with 21.7 tok/s and 51K context.

What context window can Yi Coder 9B use on MacBook Pro M3 Pro 18GB?

On MacBook Pro M3 Pro 18GB, Yi Coder 9B can safely use up to 51K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 Pro 18GB as fast as VRAM for Yi Coder 9B?

Not always. MacBook Pro M3 Pro 18GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.

See all results for MacBook Pro M3 Pro 18GB See all hardware for Yi Coder 9B

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