Can Yi 9B Coder i1 run on Mac mini M4 64GB?

Q: Is unified memory on Mac mini M4 64GB as fast as VRAM for Yi 9B Coder i1?

Not always. Mac mini M4 64GB 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

C43Usable

Estimated — low-sample bucket· few comparable runs

Yi 9B Coder i1 needs ~14.4 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~16 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) — 14.4 GB, 14.5 tok/s, Runs well

14.4 GB required46.1 GB available

31% VRAM used

Fit status

Runs well

Decode

14.5 tok/s

TTFT

13371 ms

Safe context

497K

Memory

14.4 GB / 46.1 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on Mac mini M4 64GB
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: 14.5 tok/s decode · 13.4s TTFT (warm) · 36 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	14.5 tok/s	7293 ms	497K
Coding	C	Runs well	15.7 tok/s	12302 ms	497K
Agentic Coding	C	Runs well	14.5 tok/s	19449 ms	497K
Reasoning	C	Runs well	14.5 tok/s	15803 ms	497K
RAG	C	Runs well	14.5 tok/s	24312 ms	497K

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C41
Q3_K_S	3	4.4 GB	Low	C41
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 96GBBudget pick

96 GB Unified (+32)546 GB/s (+426)

Raises estimated decode speed by about 371%.68.3 tok/s decode

Raises estimated decode speed by about 371%.

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

~$2,499 MSRP

MacBook Pro M3 Max 128GBBest value

128 GB Unified (+64)400 GB/s (+280)

Raises estimated decode speed by about 201%.43.7 tok/s decode

Raises estimated decode speed by about 201%.

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBApple upgrade

96 GB Unified (+32)819 GB/s (+699)

Raises estimated decode speed by about 599%.101.4 tok/s decode

Raises estimated decode speed by about 599%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 64GB run Yi 9B Coder i1?

Yes, Mac mini M4 64GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 15.7 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 14.4 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 Mac mini M4 64GB?

On Mac mini M4 64GB, Yi 9B Coder i1 achieves approximately 15.7 tokens per second decode speed with a time-to-first-token of 12302ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on Mac mini M4 64GB receives a C grade with 15.7 tok/s and 497K context.

What context window can Yi 9B Coder i1 use on Mac mini M4 64GB?

On Mac mini M4 64GB, Yi 9B Coder i1 can safely use up to 497K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac mini M4 64GB as fast as VRAM for Yi 9B Coder i1?

See all results for Mac mini M4 64GB See all hardware for Yi 9B Coder i1

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