Can Yi 9B Coder i1 run on MacBook Pro M3 24GB?

Q: Is unified memory on MacBook Pro M3 24GB as fast as VRAM for Yi 9B Coder i1?

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

C48Usable

Estimated from fit model

Yi 9B Coder i1 needs ~10.0 GB VRAM. MacBook Pro M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~12 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) — 10.0 GB, 12.4 tok/s, Runs well

10.0 GB required17.3 GB available

58% VRAM used

Fit status

Runs well

Decode

12.4 tok/s

TTFT

15630 ms

Safe context

126K

Memory

10.0 GB / 17.3 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 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: 12.4 tok/s decode · 15.6s TTFT (warm) · 31 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	C	Runs well	12.4 tok/s	8526 ms	126K
Coding	C	Runs well	12.4 tok/s	15630 ms	126K
Agentic Coding	C	Runs well	12.4 tok/s	22735 ms	126K
Reasoning	C	Runs well	12.4 tok/s	18472 ms	126K
RAG	C	Runs well	12.4 tok/s	28419 ms	126K

Quantization options

How Yi 9B Coder i1 (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	C46
Q3_K_S	3	4.4 GB	Low	C47
NVFP4	4

Get started

Copy-paste commands to run Yi 9B Coder i1 on your machine.

Run

lms load hf-mradermacher--yi-9b-coder-i1-gguf && lms server start

Upgrade options

Hardware that runs Yi 9B Coder i1 well

MacBook Pro M2 Max 32GBBudget pick

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

Raises estimated decode speed by about 241%.42.3 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 M2 Pro 32GBBest value

32 GB Unified (+8)200 GB/s (+100)

Raises estimated decode speed by about 106%.25.5 tok/s decode

Raises estimated decode speed by about 106%.

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

~$1,999 MSRP

MacBook Pro M1 Max 32GBApple upgrade

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

Raises estimated decode speed by about 223%.40.1 tok/s decode

Raises estimated decode speed by about 223%.

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

~$2,499 MSRP

Frequently asked questions

Can MacBook Pro M3 24GB run Yi 9B Coder i1?

Yes, MacBook Pro M3 24GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 12.4 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 10.0 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 9B Coder i1?

The recommended quantization for Yi 9B Coder i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 9B Coder i1 run at on MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, Yi 9B Coder i1 achieves approximately 12.4 tokens per second decode speed with a time-to-first-token of 15630ms using Q4_K_M quantization.

Can MacBook Pro M3 24GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on MacBook Pro M3 24GB receives a C grade with 12.4 tok/s and 126K context.

What context window can Yi 9B Coder i1 use on MacBook Pro M3 24GB?

On MacBook Pro M3 24GB, Yi 9B Coder i1 can safely use up to 126K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M3 24GB as fast as VRAM for Yi 9B Coder i1?

See all results for MacBook Pro M3 24GB See all hardware for Yi 9B Coder i1

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