Can internlm JanusCoder 14B run on MacBook Air M3 24GB?

YES — Runs Great

C49Usable

Estimated from fit model

internlm JanusCoder 14B needs ~13.7 GB VRAM. MacBook Air M3 24GB has 17.3 GB. With Q4_K_M quantization, expect ~8 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, 8.0 tok/s, Runs well

13.7 GB required17.3 GB available

79% VRAM used

Fit status

Runs well

Decode

8.0 tok/s

TTFT

24314 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 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: 8.0 tok/s decode · 24.3s TTFT (warm) · 20 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	8.0 tok/s	13262 ms	51K
Coding	C	Runs well	8.0 tok/s	24314 ms	51K
Agentic Coding	C	Tight fit	8.0 tok/s	35366 ms	51K
Reasoning	C	Runs well	8.0 tok/s	28735 ms	51K
RAG	C	Tight fit	8.0 tok/s	44207 ms	51K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on MacBook Air M3 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	7.8 GB	Medium	C50
Q4_K_M	4	8.5 GB	Medium	C51
Q5_K_M	5	10.1 GB	High	C51
Q6_KBest for your GPU	6	11.5 GB	High	C50
Q8_0	8	15.0 GB	Very High	F0
F16	16	28.7 GB	Maximum	F0

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

アップグレードオプション

internlm JanusCoder 14Bを快適に動かすハードウェア

MacBook Pro M2 Max 32GBコスパ重視

32 GB Unified (+8)400 GB/s (+300)

Raises estimated decode speed by about 240%.27.2 tok/sデコード

Raises estimated decode speed by about 240%.

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

〜$1,999 MSRP

MacBook Pro M2 Pro 32GBコスパ最良

32 GB Unified (+8)200 GB/s (+100)

Raises estimated decode speed by about 105%.16.4 tok/sデコード

Raises estimated decode speed by about 105%.

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

〜$1,999 MSRP

MacBook Pro M1 Pro 32GBAppleアップグレード

32 GB Unified (+8)200 GB/s (+100)

Raises estimated decode speed by about 90%.15.2 tok/sデコード

Raises estimated decode speed by about 90%.

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

〜$1,999 MSRP

Frequently asked questions

Can MacBook Air M3 24GB run internlm JanusCoder 14B?

Yes, MacBook Air M3 24GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 8.0 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 M3 24GB?

On MacBook Air M3 24GB, internlm JanusCoder 14B achieves approximately 8.0 tokens per second decode speed with a time-to-first-token of 24314ms using Q4_K_M quantization.

Can MacBook Air M3 24GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on MacBook Air M3 24GB receives a C grade with 8.0 tok/s and 51K context.

What context window can internlm JanusCoder 14B use on MacBook Air M3 24GB?

On MacBook Air M3 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.

Is unified memory on MacBook Air M3 24GB as fast as VRAM for internlm JanusCoder 14B?

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 24GB See all hardware for internlm JanusCoder 14B

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