Will It Run AI

Can Yi Coder 9B Chat run on MacBook Air M3 24GB?

YES — Runs Great

C48Usable
Estimated from fit model

Yi Coder 9B Chat needs ~10.0 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~12 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
Share:

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) 10.0 GB, 12.4 tok/s, Runs well
10.0 GB required17.3 GB available
58% VRAM used

Fit status

Runs well

Decode

12.4 tok/s

TTFT

15630 ms

Safe context

126K

Memory

10.0 GB / 17.3 GB

Memory breakdown

Weights5.5 GB
KV Cache1.1 GB
Runtime0.9 GB
Headroom2.6 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on MacBook Air M3 24GB
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: 12.4 tok/s decode · 15.6s TTFT (warm) · 31 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 well12.4 tok/s8526 ms126K
CodingCRuns well12.4 tok/s15630 ms126K
Agentic CodingCRuns well12.4 tok/s22735 ms126K
ReasoningCRuns well12.4 tok/s18472 ms126K
RAGCRuns well12.4 tok/s28419 ms126K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on MacBook Air M3 24GB (17.3 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
3.5 GB
LowC47
Q3_K_S
3
4.4 GB
LowC48
NVFP4
4
5.0 GB
MediumC48
Q4_K_M
4
5.5 GB
MediumC49
Q5_K_M
5
6.5 GB
HighC49
Q6_K
6
7.4 GB
HighC50
Q8_0Best for your GPU
8
9.6 GB
Very HighC51
F16
16
18.5 GB
MaximumF0

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 MacBook Air M3 24GB run Yi Coder 9B Chat?

Yes, MacBook Air M3 24GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 12.4 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 10.0 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 Air M3 24GB?

On MacBook Air M3 24GB, Yi Coder 9B Chat achieves approximately 12.4 tokens per second decode speed with a time-to-first-token of 15630ms using Q4_K_M quantization.

Can MacBook Air M3 24GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on MacBook Air M3 24GB receives a C grade with 12.4 tok/s and 126K context.

What context window can Yi Coder 9B Chat use on MacBook Air M3 24GB?

On MacBook Air M3 24GB, Yi Coder 9B Chat can safely use up to 126K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Air M3 24GB as fast as VRAM for Yi Coder 9B Chat?

Not always. MacBook Air M3 24GB 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 Air M3 24GBSee all hardware for Yi Coder 9B 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-9b-chat-gguf-on-m3-air-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: