Can Yi 9B Coder i1 run on MacBook Pro M4 Max 64GB?

YES — Runs Great

C47Usable

Estimated from fit model

Yi 9B Coder i1 needs ~14.4 GB VRAM. MacBook Pro M4 Max 64GB has 46.1 GB. With Q4_K_M quantization, expect ~68 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 14.4 GB, 68.3 tok/s, Runs well

14.4 GB required46.1 GB available

31% VRAM used

Fit status

Runs well

Decode

68.3 tok/s

TTFT

2835 ms

Safe context

497K

Memory

14.4 GB / 46.1 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom6.9 GB

See how fast it feels

See how fast it feelsYi 9B Coder i1 on MacBook Pro M4 Max 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: 68.3 tok/s decode · 2.8s TTFT (warm) · 171 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	68.3 tok/s	1546 ms	497K
Coding	C	Runs well	68.3 tok/s	2835 ms	497K
Agentic Coding	C	Runs well	68.3 tok/s	4123 ms	497K
Reasoning	C	Runs well	68.3 tok/s	3350 ms	497K
RAG	C	Runs well	68.3 tok/s	5154 ms	497K

Inference speed

Yi 9B Coder i1 inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for Yi 9B Coder i1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~126 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	126.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	126.0	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	125.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	119.3	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	111.3	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	101.4	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	84.5	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	80.1	Fits
RTX 4070 12GB	12 GB	Q4_K_M	68.9	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	68.3	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	68.3	Fits
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	43.7	Fits
RTX 3060 12GB	12 GB	Q4_K_M	43.3	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	40.1	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	35.2	Fits
RTX 4060 8GB	8 GB	Q4_K_M	23.4	Offloads

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How Yi 9B Coder i1 (9B params) fits at each quantization level on MacBook Pro M4 Max 64GB (46.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C41
Q3_K_S	3	4.4 GB	Low	C41
NVFP4	4	5.0 GB	Medium	C42
Q4_K_M	4	5.5 GB	Medium	C42
Q5_K_M	5	6.5 GB	High	C42
Q6_K	6	7.4 GB	High	C42
Q8_0	8	9.6 GB	Very High	C43
F16Best for your GPU	16	18.5 GB	Maximum	C45

Get started

Copy-paste commands to run Yi 9B Coder i1 on your machine.

Run

lms load hf-mradermacher--yi-9b-coder-i1-gguf && lms server start

Frequently asked questions

Can MacBook Pro M4 Max 64GB run Yi 9B Coder i1?

Yes, MacBook Pro M4 Max 64GB can run Yi 9B Coder i1 with a C grade (Runs well). Expected decode speed: 68.3 tok/s.

How much VRAM does Yi 9B Coder i1 need?

Yi 9B Coder i1 (9B parameters) requires approximately 14.4 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi 9B Coder i1?

The recommended quantization for Yi 9B Coder i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi 9B Coder i1 run at on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Yi 9B Coder i1 achieves approximately 68.3 tokens per second decode speed with a time-to-first-token of 2835ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 64GB run Yi 9B Coder i1 for coding?

For coding workloads, Yi 9B Coder i1 on MacBook Pro M4 Max 64GB receives a C grade with 68.3 tok/s and 497K context.

What context window can Yi 9B Coder i1 use on MacBook Pro M4 Max 64GB?

On MacBook Pro M4 Max 64GB, Yi 9B Coder i1 can safely use up to 497K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 64GB as fast as VRAM for Yi 9B Coder i1?

Not always. MacBook Pro M4 Max 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 MacBook Pro M4 Max 64GB See all hardware for Yi 9B Coder i1

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