Can Yi Coder 9B run on MacBook Pro M4 16GB?

YES — Tight Fit

B60Good

Estimated — low-sample bucket· few comparable runs

Yi Coder 9B needs ~9.6 GB VRAM. MacBook Pro M4 16GB has 11.5 GB. With Q4_K_M quantization, expect ~16 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 9.6 GB, 15.7 tok/s, Tight fit

9.6 GB required11.5 GB available

83% VRAM used

Fit status

Tight fit

Decode

15.7 tok/s

TTFT

12296 ms

Safe context

37K

Memory

9.6 GB / 11.5 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsYi Coder 9B on MacBook Pro M4 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: 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	B	Runs well	15.7 tok/s	6707 ms	37K
Coding	B	Tight fit	15.7 tok/s	12296 ms	37K
Agentic Coding	B	Runs with offload	15.7 tok/s	17884 ms	37K
Reasoning	B	Tight fit	15.7 tok/s	14531 ms	37K
RAG	B	Runs with offload	15.7 tok/s	22355 ms	37K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on MacBook Pro M4 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	B63
Q3_K_S	3	4.4 GB	Low	B64
NVFP4	4	5.0 GB	Medium	B65
Q4_K_M	4	5.5 GB	Medium	B65
Q5_K_M	5	6.5 GB	High	B64
Q6_KBest for your GPU	6	7.4 GB	High	B64
Q8_0	8	9.6 GB	Very High	F0
F16	16	18.5 GB	Maximum	F0

Get started

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

Run

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

Opciones de mejora

Hardware que ejecuta bien Yi Coder 9B

MacBook Air M4 24GBOpción económica

24 GB Unified (+8)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.15.7 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,099 MSRP

MacBook Pro M3 24GBMejor relación calidad-precio

24 GB Unified (+8)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.13.5 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,099 MSRP

MacBook Air M3 24GBMejora Apple

24 GB Unified (+8)

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.13.5 tok/s decodificación

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$1,099 MSRP

RTX 3080 Ti 12GBMayor salto

912 GB/s (+792)

Sube la velocidad estimada de decodificación alrededor de un 588%.108 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 588%.

~$1,199 MSRP

Frequently asked questions

Can MacBook Pro M4 16GB run Yi Coder 9B?

Yes, MacBook Pro M4 16GB can run Yi Coder 9B with a B grade (Tight fit). Expected decode speed: 15.7 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 9.6 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 MacBook Pro M4 16GB?

On MacBook Pro M4 16GB, 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 MacBook Pro M4 16GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on MacBook Pro M4 16GB receives a B grade with 15.7 tok/s and 37K context.

What context window can Yi Coder 9B use on MacBook Pro M4 16GB?

On MacBook Pro M4 16GB, Yi Coder 9B can safely use up to 37K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 16GB as fast as VRAM for Yi Coder 9B?

Not always. MacBook Pro M4 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.

See all results for MacBook Pro M4 16GB See all hardware for Yi Coder 9B

Embed this result▼

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

<iframe src="https://willitrunai.com/embed/yi-coder-9b-on-m4-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: