Can Yi 9B Coder i1 run on MacBook Pro M1 Pro 32GB?

Q: Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for Yi 9B Coder i1?

Not always. MacBook Pro M1 Pro 32GB 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

C47Usable

Estimated from fit model

Yi 9B Coder i1 needs ~10.9 GB VRAM. MacBook Pro M1 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~24 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) — 10.9 GB, 23.7 tok/s, Runs well

10.9 GB required23.0 GB available

47% VRAM used

Fit status

Runs well

Decode

23.7 tok/s

TTFT

8176 ms

Safe context

200K

Memory

10.9 GB / 23.0 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on MacBook Pro M1 Pro 32GB
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: 23.7 tok/s decode · 8.2s TTFT (warm) · 59 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	23.7 tok/s	4460 ms	200K
Coding	C	Runs well	23.7 tok/s	8176 ms	200K
Agentic Coding	C	Runs well	23.7 tok/s	11892 ms	200K
Reasoning	C	Runs well	23.7 tok/s	9662 ms	200K
RAG	C	Runs well	23.7 tok/s	14865 ms	200K

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on MacBook Pro M1 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C45
Q3_K_S	3	4.4 GB	Low	C45
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 M4 Max 36GBBudget pick

36 GB Unified (+4)410 GB/s (+210)

Raises estimated decode speed by about 116%.51.3 tok/s decode

Raises estimated decode speed by about 116%.

~$2,499 MSRP

MacBook Pro M4 Max 48GBBest value

48 GB Unified (+16)546 GB/s (+346)

Raises estimated decode speed by about 188%.68.3 tok/s decode

Raises estimated decode speed by about 188%.

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

~$2,499 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+32)800 GB/s (+600)

Raises estimated decode speed by about 257%.84.5 tok/s decode

Raises estimated decode speed by about 257%.

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

~$3,999 MSRP

Frequently asked questions

Can MacBook Pro M1 Pro 32GB run Yi 9B Coder i1?

Yes, MacBook Pro M1 Pro 32GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 23.7 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 10.9 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 M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, Yi 9B Coder i1 achieves approximately 23.7 tokens per second decode speed with a time-to-first-token of 8176ms using Q4_K_M quantization.

Can MacBook Pro M1 Pro 32GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on MacBook Pro M1 Pro 32GB receives a C grade with 23.7 tok/s and 200K context.

What context window can Yi 9B Coder i1 use on MacBook Pro M1 Pro 32GB?

On MacBook Pro M1 Pro 32GB, Yi 9B Coder i1 can safely use up to 200K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M1 Pro 32GB as fast as VRAM for Yi 9B Coder i1?

See all results for MacBook Pro M1 Pro 32GB See all hardware for Yi 9B Coder i1

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/hf-mradermacher--yi-9b-coder-i1-gguf-on-m1-pro-32gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: