Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for Yi Coder 1.5B Chat?

Not always. MacBook Pro M2 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.

Can Yi Coder 1.5B Chat run on MacBook Pro M2 Pro 32GB?

YES — Runs Great

C42Usable

Estimated from fit model

Yi Coder 1.5B Chat needs ~5.4 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~21 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) — 5.4 GB, 21.0 tok/s, Runs well

5.4 GB required23.0 GB available

23% VRAM used

Fit status

Runs well

Decode

21.0 tok/s

TTFT

9219 ms

Safe context

1.6M

Memory

5.4 GB / 23.0 GB

Memory breakdown

Weights0.9 GB

KV Cache0.2 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsYi Coder 1.5B Chat on MacBook Pro M2 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: 21.0 tok/s decode · 9.2s TTFT (warm) · 53 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	21.0 tok/s	5029 ms	1.4M
Coding	C	Runs well	21.0 tok/s	9219 ms	1.6M
Agentic Coding	C	Runs well	21.0 tok/s	13410 ms	1.6M
Reasoning	C	Runs well	21.0 tok/s	10895 ms	1.6M
RAG	C	Runs well	21.0 tok/s	16762 ms	1.6M

Quantization options

How Yi Coder 1.5B Chat (1.5B params) fits at each quantization level on MacBook Pro M2 Pro 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	0.6 GB	Low	C44
Q3_K_S	3	0.7 GB	Low	C44
NVFP4	4

Get started

Copy-paste commands to run Yi Coder 1.5B Chat on your machine.

Run

lms load hf-maziyarpanahi--yi-coder-1-5b-chat-gguf && lms server start

Frequently asked questions

Can MacBook Pro M2 Pro 32GB run Yi Coder 1.5B Chat?

Yes, MacBook Pro M2 Pro 32GB can run Yi Coder 1.5B Chat with a C grade (Runs well). Expected decode speed: 21.0 tok/s.

How much VRAM does Yi Coder 1.5B Chat need?

Yi Coder 1.5B Chat (1.5B parameters) requires approximately 5.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 1.5B Chat?

The recommended quantization for Yi Coder 1.5B Chat is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 1.5B Chat run at on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Yi Coder 1.5B Chat achieves approximately 21.0 tokens per second decode speed with a time-to-first-token of 9219ms using Q4_K_M quantization.

Can MacBook Pro M2 Pro 32GB run Yi Coder 1.5B Chat for coding?

For coding workloads, Yi Coder 1.5B Chat on MacBook Pro M2 Pro 32GB receives a C grade with 21.0 tok/s and 1.6M context.

What context window can Yi Coder 1.5B Chat use on MacBook Pro M2 Pro 32GB?

On MacBook Pro M2 Pro 32GB, Yi Coder 1.5B Chat can safely use up to 1.6M tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Pro M2 Pro 32GB See all hardware for Yi Coder 1.5B Chat

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/hf-maziyarpanahi--yi-coder-1-5b-chat-gguf-on-m2-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: