Is unified memory on MacBook Air M1 16GB as fast as VRAM for Yi Coder 9B Chat?

Not always. MacBook Air M1 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.

Can Yi Coder 9B Chat run on MacBook Air M1 16GB?

Q: What should I upgrade first if Yi Coder 9B Chat feels slow on MacBook Air M1 16GB?

Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

YES — Runs Great

C49Usable

Estimated from fit model

Yi Coder 9B Chat needs ~9.2 GB VRAM. MacBook Air M1 16GB has 11.5 GB. With Q4_K_M quantization, expect ~7 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) — 9.2 GB, 7.4 tok/s, Runs well

9.2 GB required11.5 GB available

80% VRAM used

Fit status

Runs well

Decode

7.4 tok/s

TTFT

26051 ms

Safe context

52K

Memory

9.2 GB / 11.5 GB

Memory breakdown

Weights5.5 GB

KV Cache1.1 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsYi Coder 9B Chat on MacBook Air M1 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: 7.4 tok/s decode · 26.1s TTFT (warm) · 19 tok/s prefill

What limits this setup

The model fits in shared memory, but shared-memory bandwidth is now the real limiter.

Fit does not mean dedicated-VRAM speed

Unified or shared memory can make a model technically fit, but sustained tokens per second may still trail a discrete high-bandwidth GPU with less total memory.

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

Prioritize bandwidth, not only capacity

If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	7.4 tok/s	14209 ms	52K
Coding	C	Runs well	7.4 tok/s	26051 ms	52K
Agentic Coding	C	Tight fit	7.4 tok/s	37892 ms	52K
Reasoning	C	Runs well	7.4 tok/s	30787 ms	52K
RAG	C	Tight fit	7.4 tok/s	47365 ms	52K

Quantization options

How Yi Coder 9B Chat (9B params) fits at each quantization level on MacBook Air M1 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C50
Q3_K_S	3	4.4 GB	Low	C52
NVFP4	4

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

Upgrade options

Hardware that runs Yi Coder 9B Chat well

MacBook Pro M3 Pro 18GBBudget pick

18 GB Unified (+2)150 GB/s (+82)

Raises estimated decode speed by about 169%.19.9 tok/s decode

Raises estimated decode speed by about 169%.

~$1,999 MSRP

MacBook Pro M4 Pro 24GBBest value

24 GB Unified (+8)273 GB/s (+205)

Raises estimated decode speed by about 376%.35.2 tok/s decode

Raises estimated decode speed by about 376%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

MacBook Pro M2 Max 32GBApple upgrade

32 GB Unified (+16)400 GB/s (+332)

Raises estimated decode speed by about 472%.42.3 tok/s decode

Raises estimated decode speed by about 472%.

Adds memory headroom for longer context windows and future model growth.

~$1,999 MSRP

Frequently asked questions

Can MacBook Air M1 16GB run Yi Coder 9B Chat?

Yes, MacBook Air M1 16GB can run Yi Coder 9B Chat with a C grade (Runs well). Expected decode speed: 7.4 tok/s.

How much VRAM does Yi Coder 9B Chat need?

Yi Coder 9B Chat (9B parameters) requires approximately 9.2 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 M1 16GB?

On MacBook Air M1 16GB, Yi Coder 9B Chat achieves approximately 7.4 tokens per second decode speed with a time-to-first-token of 26051ms using Q4_K_M quantization.

Can MacBook Air M1 16GB run Yi Coder 9B Chat for coding?

For coding workloads, Yi Coder 9B Chat on MacBook Air M1 16GB receives a C grade with 7.4 tok/s and 52K context.

What context window can Yi Coder 9B Chat use on MacBook Air M1 16GB?

On MacBook Air M1 16GB, Yi Coder 9B Chat can safely use up to 52K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

What should I upgrade first if Yi Coder 9B Chat feels slow on MacBook Air M1 16GB?

See all results for MacBook Air M1 16GB See all hardware for Yi Coder 9B Chat

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