Can Yi Coder 9B Chat run on Mac Studio M2 Ultra 128GB?

YES — Runs Great

C47Usable

Estimated from fit model

Yi Coder 9B Chat needs ~21.3 GB VRAM. Mac Studio M2 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~85 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: StandardBottleneck: Balanced

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) — 21.3 GB, 84.5 tok/s, Runs well

21.3 GB required92.2 GB available

23% VRAM used

Fit status

Runs well

Decode

84.5 tok/s

TTFT

2291 ms

Safe context

1.1M

Memory

21.3 GB / 92.2 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom13.8 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on Mac Studio M2 Ultra 128GB
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: 84.5 tok/s decode · 2.3s TTFT (warm) · 211 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	84.5 tok/s	1249 ms	1.1M
Coding	C	Runs well	84.5 tok/s	2291 ms	1.1M
Agentic Coding	C	Runs well	84.5 tok/s	3332 ms	1.1M
Reasoning	C	Runs well	84.5 tok/s	2707 ms	1.1M
RAG	C	Runs well	84.5 tok/s	4165 ms	1.1M

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	D39
Q3_K_S	3	4.4 GB	Low	D39
NVFP4	4	5.0 GB	Medium	D39
Q4_K_M	4	5.5 GB	Medium	D39
Q5_K_M	5	6.5 GB	High	D39
Q6_K	6	7.4 GB	High	D39
Q8_0	8	9.6 GB	Very High	D40
F16Best for your GPU	16	18.5 GB	Maximum	C41

Get started

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

Run

lms load hf-maziyarpanahi--yi-coder-9b-chat-gguf && lms server start

Frequently asked questions

Can Mac Studio M2 Ultra 128GB run Yi Coder 9B Chat?

Yes, Mac Studio M2 Ultra 128GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 84.5 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 21.3 GB of memory with Q4_K_M quantization.

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

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

What speed will Yi Coder 9B Chat run at on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Yi Coder 9B Chat achieves approximately 84.5 tokens per second decode speed with a time-to-first-token of 2291ms using Q4_K_M quantization.

Can Mac Studio M2 Ultra 128GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on Mac Studio M2 Ultra 128GB receives a C grade with 84.5 tok/s and 1.1M context.

What context window can Yi Coder 9B Chat use on Mac Studio M2 Ultra 128GB?

On Mac Studio M2 Ultra 128GB, Yi Coder 9B Chat can safely use up to 1.1M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M2 Ultra 128GB as fast as VRAM for Yi Coder 9B Chat?

Not always. Mac Studio M2 Ultra 128GB 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 Studio M2 Ultra 128GB See all hardware for Yi Coder 9B Chat

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<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-9b-chat-gguf-on-m2-ultra-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

84.5 tok/s

1249 ms

1.1M

Coding

Runs well

84.5 tok/s

2291 ms

1.1M

Agentic Coding

Runs well

84.5 tok/s

3332 ms

1.1M

Reasoning

Runs well

84.5 tok/s

2707 ms

1.1M

RAG

Runs well

84.5 tok/s

4165 ms

1.1M

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on Mac Studio M2 Ultra 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	D39
Q3_K_S	3	4.4 GB	Low	D39
NVFP4	4	5.0 GB	Medium	D39
Q4_K_M	4	5.5 GB	Medium	D39
Q5_K_M	5	6.5 GB	High	D39
Q6_K	6	7.4 GB	High	D39
Q8_0	8	9.6 GB	Very High	D40
F16Best for your GPU	16	18.5 GB	Maximum	C41