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

YES — Runs Great

B55Good

Estimated — low-sample bucket· few comparable runs

Yi Coder 9B needs ~14.8 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) — 14.8 GB, 15.7 tok/s, Runs well

14.8 GB required46.1 GB available

32% VRAM used

Fit status

Runs well

Decode

15.7 tok/s

TTFT

12296 ms

Safe context

131K

Memory

14.8 GB / 46.1 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsYi Coder 9B 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: 15.7 tok/s decode · 12.3s TTFT (warm) · 39 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	15.7 tok/s	6707 ms	131K
Coding	B	Runs well	15.7 tok/s	12296 ms	131K
Agentic Coding	B	Runs well	15.7 tok/s	17884 ms	131K
Reasoning	B	Runs well	15.7 tok/s	14531 ms	131K
RAG	B	Runs well	15.7 tok/s	22355 ms	131K

Quantization options

How Yi Coder 9B (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	C54
Q3_K_S	3	4.4 GB	Low	C54
NVFP4	4	5.0 GB	Medium	C54
Q4_K_M	4	5.5 GB	Medium	C54
Q5_K_M	5	6.5 GB	High	C54
Q6_K	6	7.4 GB	High	C55
Q8_0	8	9.6 GB	Very High	B55
F16Best for your GPU	16	18.5 GB	Maximum	B58

Get started

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

Run

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

Opções de upgrade

Hardware que roda bem Yi Coder 9B

MacBook Pro M4 Max 96GBOpção econômica

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

Raises estimated decode speed by about 373%.74.3 tok/s decodificação

Raises estimated decode speed by about 373%.

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

~$2,499 MSRP

MacBook Pro M3 Max 128GBMelhor custo-benefício

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

Raises estimated decode speed by about 203%.47.5 tok/s decodificação

Raises estimated decode speed by about 203%.

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

~$2,499 MSRP

Mac Studio M3 Ultra 96GBUpgrade Apple

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

Raises estimated decode speed by about 603%.110.3 tok/s decodificação

Raises estimated decode speed by about 603%.

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

~$3,999 MSRP

Frequently asked questions

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

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

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 14.8 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 Mac mini M4 64GB?

On Mac mini M4 64GB, Yi Coder 9B achieves approximately 15.7 tokens per second decode speed with a time-to-first-token of 12296ms using Q4_K_M quantization.

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

For coding workloads, Yi Coder 9B on Mac mini M4 64GB receives a B grade with 15.7 tok/s and 131K context.

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

On Mac mini M4 64GB, Yi Coder 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

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

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.

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

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