Can Yi Coder 9B run on MacBook Pro M3 24GB?

Q: Is unified memory on MacBook Pro M3 24GB as fast as VRAM for Yi Coder 9B?

Not always. MacBook Pro M3 24GB 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.

YES — Runs Great

B61Good

Estimated from fit model

Yi Coder 9B needs ~10.4 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~14 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) — 10.4 GB, 13.5 tok/s, Runs well

10.4 GB required17.3 GB available

60% VRAM used

Fit status

Runs well

Decode

13.5 tok/s

TTFT

14373 ms

Safe context

91K

Memory

10.4 GB / 17.3 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsYi Coder 9B on MacBook Pro M3 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: 13.5 tok/s decode · 14.4s TTFT (warm) · 34 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	13.5 tok/s	7840 ms	91K
Coding	B	Runs well	13.5 tok/s	14373 ms	91K
Agentic Coding	B	Runs well	13.5 tok/s	20906 ms	91K
Reasoning	B	Runs well	13.5 tok/s	16986 ms	91K
RAG	B	Runs well	13.5 tok/s	26132 ms	91K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on MacBook Pro M3 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B59
Q3_K_S	3	4.4 GB	Low	B60
NVFP4	4

Get started

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

Run

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

Upgrade options

Hardware that runs Yi Coder 9B well

MacBook Pro M2 Max 32GBBudget pick

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 241%.46 tok/s decode

Raises estimated decode speed by about 241%.

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

~$1,999 MSRP

MacBook Pro M1 Max 32GBBest value

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 223%.43.6 tok/s decode

Raises estimated decode speed by about 223%.

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

~$2,499 MSRP

MacBook Pro M4 Max 36GBApple upgrade

36 GB Unified (+12)410 GB/s (+310)

Raises estimated decode speed by about 313%.55.8 tok/s decode

Raises estimated decode speed by about 313%.

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

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M3 24GB run Yi Coder 9B?

Yes, MacBook Pro M3 24GB can run Yi Coder 9B with a B grade (Runs well). Expected decode speed: 13.5 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 10.4 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 24GB?

On MacBook Pro M3 24GB, Yi Coder 9B achieves approximately 13.5 tokens per second decode speed with a time-to-first-token of 14373ms using Q4_K_M quantization.

Can MacBook Pro M3 24GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on MacBook Pro M3 24GB receives a B grade with 13.5 tok/s and 91K context.

What context window can Yi Coder 9B use on MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, Yi Coder 9B can safely use up to 91K 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 24GB as fast as VRAM for Yi Coder 9B?

See all results for MacBook Pro M3 24GB See all hardware for Yi Coder 9B

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