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

Not always. MacBook Pro M4 Pro 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 Pro M4 Pro 24GB?

YES — Runs Great

C52Usable

Estimated — low-sample bucket· few comparable runs

internlm JanusCoder 14B needs ~13.7 GB VRAM. MacBook Pro M4 Pro 24GB has 17.3 GB. With Q4_K_M quantization, expect ~25 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: 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) — 13.7 GB, 21.7 tok/s, Runs well

13.7 GB required17.3 GB available

79% VRAM used

Fit status

Runs well

Decode

21.7 tok/s

TTFT

8938 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 Pro M4 Pro 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: 21.7 tok/s decode · 8.9s TTFT (warm) · 54 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	21.7 tok/s	4875 ms	51K
Coding	C	Runs well	24.6 tok/s	7865 ms	51K
Agentic Coding	C	Tight fit	21.7 tok/s	13000 ms	51K
Reasoning	C	Runs well	21.7 tok/s	10563 ms	51K
RAG	C	Tight fit	21.7 tok/s	16250 ms	51K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on MacBook Pro M4 Pro 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

RX 7900 XT 20GBBudget pick

800 GB/s (+527)

Raises estimated decode speed by about 159%.56.2 tok/s decode

Raises estimated decode speed by about 159%.

~$899 MSRP

RX 7900 XTX 24GBBest value

960 GB/s (+687)

Raises estimated decode speed by about 273%.80.9 tok/s decode

Raises estimated decode speed by about 273%.

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

~$999 MSRP

Frequently asked questions

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

Yes, MacBook Pro M4 Pro 24GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 24.6 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 Pro M4 Pro 24GB?

On MacBook Pro M4 Pro 24GB, internlm JanusCoder 14B achieves approximately 24.6 tokens per second decode speed with a time-to-first-token of 7865ms using Q4_K_M quantization.

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

For coding workloads, internlm JanusCoder 14B on MacBook Pro M4 Pro 24GB receives a C grade with 24.6 tok/s and 51K context.

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

On MacBook Pro M4 Pro 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 Pro M4 Pro 24GB See all hardware for internlm JanusCoder 14B

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