Is unified memory on MacBook Air M4 24GB as fast as VRAM for internlm JanusCoder 14B?

Not always. MacBook Air M4 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.

Can internlm JanusCoder 14B run on MacBook Air M4 24GB?

YES — Runs Great

C49Usable

Estimated — low-sample bucket· few comparable runs

internlm JanusCoder 14B needs ~13.7 GB VRAM. MacBook Air M4 24GB has 17.3 GB. With Q4_K_M quantization, expect ~10 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) — 13.7 GB, 8.9 tok/s, Runs well

13.7 GB required17.3 GB available

79% VRAM used

Fit status

Runs well

Decode

8.9 tok/s

TTFT

21745 ms

Safe context

51K

Memory

13.7 GB / 17.3 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom2.6 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on MacBook Air M4 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: 8.9 tok/s decode · 21.7s TTFT (warm) · 22 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	8.9 tok/s	11861 ms	51K
Coding	C	Runs well	10.1 tok/s	19136 ms	51K
Agentic Coding	C	Tight fit	8.9 tok/s	31630 ms	51K
Reasoning	C	Runs well	8.9 tok/s	25699 ms	51K
RAG	C	Tight fit	8.9 tok/s	39537 ms	51K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on MacBook Air M4 24GB (17.3 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C48
Q3_K_S	3	6.9 GB	Low	C49
NVFP4	4

Get started

Copy-paste commands to run internlm JanusCoder 14B on your machine.

Run

lms load hf-bartowski--internlm-januscoder-14b-gguf && lms server start

Upgrade options

Hardware that runs internlm JanusCoder 14B well

MacBook Pro M2 Max 32GBBudget pick

32 GB Unified (+8)400 GB/s (+280)

Raises estimated decode speed by about 206%.27.2 tok/s decode

Raises estimated decode speed by about 206%.

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

~$1,999 MSRP

MacBook Pro M2 Pro 32GBBest value

32 GB Unified (+8)200 GB/s (+80)

Raises estimated decode speed by about 84%.16.4 tok/s decode

Raises estimated decode speed by about 84%.

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

~$1,999 MSRP

MacBook Pro M1 Pro 32GBApple upgrade

32 GB Unified (+8)200 GB/s (+80)

Raises estimated decode speed by about 71%.15.2 tok/s decode

Raises estimated decode speed by about 71%.

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

~$1,999 MSRP

Frequently asked questions

Can MacBook Air M4 24GB run internlm JanusCoder 14B?

Yes, MacBook Air M4 24GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 10.1 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.

What is the best quantization for internlm JanusCoder 14B?

The recommended quantization for internlm JanusCoder 14B is Q4_K_M, which balances quality and memory efficiency.

What speed will internlm JanusCoder 14B run at on MacBook Air M4 24GB?

On MacBook Air M4 24GB, internlm JanusCoder 14B achieves approximately 10.1 tokens per second decode speed with a time-to-first-token of 19136ms using Q4_K_M quantization.

Can MacBook Air M4 24GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on MacBook Air M4 24GB receives a C grade with 10.1 tok/s and 51K context.

What context window can internlm JanusCoder 14B use on MacBook Air M4 24GB?

On MacBook Air M4 24GB, internlm JanusCoder 14B can safely use up to 51K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Air M4 24GB See all hardware for internlm JanusCoder 14B

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