Can internlm JanusCoder 14B run on MacBook Pro M4 Max 48GB?

YES — Runs Great

C49Usable

Estimated from fit model

internlm JanusCoder 14B needs ~16.3 GB VRAM. MacBook Pro M4 Max 48GB has 34.6 GB. With Q4_K_M quantization, expect ~35 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: StandardBottleneck: Balanced

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) — 16.3 GB, 35.4 tok/s, Runs well

16.3 GB required34.6 GB available

47% VRAM used

Fit status

Runs well

Decode

35.4 tok/s

TTFT

5462 ms

Safe context

194K

Memory

16.3 GB / 34.6 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom5.2 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on MacBook Pro M4 Max 48GB
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: 35.4 tok/s decode · 5.5s TTFT (warm) · 89 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	35.4 tok/s	2979 ms	194K
Coding	C	Runs well	35.4 tok/s	5462 ms	194K
Agentic Coding	C	Runs well	35.4 tok/s	7945 ms	194K
Reasoning	C	Runs well	35.4 tok/s	6455 ms	194K
RAG	C	Runs well	35.4 tok/s	9931 ms	194K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on MacBook Pro M4 Max 48GB (34.6 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C43
Q3_K_S	3	6.9 GB	Low	C44
NVFP4	4	7.8 GB	Medium	C44
Q4_K_M	4	8.5 GB	Medium	C44
Q5_K_M	5	10.1 GB	High	C45
Q6_K	6	11.5 GB	High	C45
Q8_0	8	15.0 GB	Very High	C47
F16Best for your GPU	16	28.7 GB	Maximum	C48

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-Optionen

Hardware, die internlm JanusCoder 14B gut ausführt

RTX PRO 5000 Blackwell 48GBGünstige Wahl

1344 GB/s (+798)

Raises estimated decode speed by about 273%.132.2 tok/s Dekodierung

Raises estimated decode speed by about 273%.

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

ca. $4,999 MSRP

RTX 6000 Ada 48GBBestes Preis-Leistungs-Verhältnis

960 GB/s (+414)

Raises estimated decode speed by about 160%.92.2 tok/s Dekodierung

Raises estimated decode speed by about 160%.

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

ca. $6,800 MSRP

Frequently asked questions

Can MacBook Pro M4 Max 48GB run internlm JanusCoder 14B?

Yes, MacBook Pro M4 Max 48GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 35.4 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 16.3 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 Pro M4 Max 48GB?

On MacBook Pro M4 Max 48GB, internlm JanusCoder 14B achieves approximately 35.4 tokens per second decode speed with a time-to-first-token of 5462ms using Q4_K_M quantization.

Can MacBook Pro M4 Max 48GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on MacBook Pro M4 Max 48GB receives a C grade with 35.4 tok/s and 194K context.

What context window can internlm JanusCoder 14B use on MacBook Pro M4 Max 48GB?

On MacBook Pro M4 Max 48GB, internlm JanusCoder 14B can safely use up to 194K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on MacBook Pro M4 Max 48GB as fast as VRAM for internlm JanusCoder 14B?

Not always. MacBook Pro M4 Max 48GB 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 Pro M4 Max 48GB See all hardware for internlm JanusCoder 14B

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