Will It Run AI

Can Yi Coder 9B Chat run on Mac mini M4 64GB?

YES — Runs Great

C43Usable
Estimated — low-sample bucket· few comparable runs

Yi Coder 9B Chat needs ~14.4 GB VRAM. Mac mini M4 64GB has 46.1 GB. With Q4_K_M quantization, expect ~15 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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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, 14.5 tok/s, Runs well
14.4 GB required46.1 GB available
31% VRAM used

Fit status

Runs well

Decode

14.5 tok/s

TTFT

13371 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 Coder 9B Chat on Mac mini M4 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: 14.5 tok/s decode · 13.4s TTFT (warm) · 36 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

WorkloadGradeFitDecodeTTFTContext
ChatCRuns well14.5 tok/s7293 ms497K
CodingCRuns well14.5 tok/s13371 ms497K
Agentic CodingCRuns well14.5 tok/s19449 ms497K
ReasoningCRuns well15.7 tok/s14538 ms497K
RAGCRuns well14.5 tok/s24312 ms497K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on Mac mini M4 64GB (46.1 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowC42
Q3_K_S
3
4.4 GB
LowC42
NVFP4
4
5.0 GB
MediumC42
Q4_K_M
4
5.5 GB
MediumC42
Q5_K_M
5
6.5 GB
HighC42
Q6_K
6
7.4 GB
HighC42
Q8_0
8
9.6 GB
Very HighC43
F16Best for your GPU
16
18.5 GB
MaximumC46

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

Opciones de mejora

Hardware que ejecuta bien Yi Coder 9B Chat

Frequently asked questions

Can Mac mini M4 64GB run Yi Coder 9B Chat?

Yes, Mac mini M4 64GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 14.5 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 14.4 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 mini M4 64GB?

On Mac mini M4 64GB, Yi Coder 9B Chat achieves approximately 14.5 tokens per second decode speed with a time-to-first-token of 13371ms using Q4_K_M quantization.

Can Mac mini M4 64GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on Mac mini M4 64GB receives a C grade with 14.5 tok/s and 497K context.

What context window can Yi Coder 9B Chat use on Mac mini M4 64GB?

On Mac mini M4 64GB, Yi Coder 9B Chat 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 Mac mini M4 64GB as fast as VRAM for Yi Coder 9B Chat?

Not always. Mac mini M4 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 Mac mini M4 64GBSee all hardware for Yi Coder 9B Chat
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