Can Yi Coder 9B Chat run on MacBook Pro M2 Pro 16GB?

Q: Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Yi Coder 9B Chat?

Not always. MacBook Pro M2 Pro 16GB 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

C53Usable

Estimated from fit model

Yi Coder 9B Chat needs ~9.2 GB VRAM. MacBook Pro M2 Pro 16GB has 11.5 GB. With Q4_K_M quantization, expect ~26 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) — 9.2 GB, 25.5 tok/s, Runs well

9.2 GB required11.5 GB available

80% VRAM used

Fit status

Runs well

Decode

25.5 tok/s

TTFT

7592 ms

Safe context

52K

Memory

9.2 GB / 11.5 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on MacBook Pro M2 Pro 16GB
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: 25.5 tok/s decode · 7.6s TTFT (warm) · 64 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	25.5 tok/s	4141 ms	52K
Coding	C	Runs well	25.5 tok/s	7592 ms	52K
Agentic Coding	C	Tight fit	25.5 tok/s	11043 ms	52K
Reasoning	C	Runs well	25.5 tok/s	8972 ms	52K
RAG	C	Tight fit	25.5 tok/s	13803 ms	52K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on MacBook Pro M2 Pro 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C52
NVFP4	4

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

Upgrade options

Hardware that runs Yi Coder 9B Chat well

Intel Arc B580 12GBBudget pick

456 GB/s (+256)

Raises estimated decode speed by about 56%.39.9 tok/s decode

Raises estimated decode speed by about 56%.

~$249 MSRP

RTX 3060 12GBBest value

360 GB/s (+160)

Raises estimated decode speed by about 43%.36.4 tok/s decode

Raises estimated decode speed by about 43%.

~$329 MSRP

Frequently asked questions

Can MacBook Pro M2 Pro 16GB run Yi Coder 9B Chat?

Yes, MacBook Pro M2 Pro 16GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 25.5 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 9.2 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 MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Yi Coder 9B Chat achieves approximately 25.5 tokens per second decode speed with a time-to-first-token of 7592ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 16GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on MacBook Pro M2 Pro 16GB receives a C grade with 25.5 tok/s and 52K context.

What context window can Yi Coder 9B Chat use on MacBook Pro M2 Pro 16GB?

On MacBook Pro M2 Pro 16GB, Yi Coder 9B Chat can safely use up to 52K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M2 Pro 16GB as fast as VRAM for Yi Coder 9B Chat?

See all results for MacBook Pro M2 Pro 16GB See all hardware for Yi Coder 9B Chat

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