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 ~22 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	21.7 tok/s	8938 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

Inference speed

internlm JanusCoder 14B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for internlm JanusCoder 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q4_K_M	140.6	Fits
RTX 4090 24GB	24 GB	Q4_K_M	89.7	Fits
RX 7900 XTX 24GB	24 GB	Q4_K_M	80.9	Fits
RTX 3090 24GB	24 GB	Q4_K_M	76.7	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	75.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q4_K_M	65.2	Fits
Mac Studio M2 Ultra 128GB	128 GB	Q4_K_M	54.3	Fits
Mac Studio M1 Ultra 128GB	128 GB	Q4_K_M	51.5	Fits
MacBook Pro M4 Max 128GB	128 GB	Q4_K_M	35.4	Fits
MacBook Pro M4 Max 64GB	64 GB	Q4_K_M	35.4	Fits
RTX 4070 12GB	12 GB	Q4_K_M	33.2	Offloads
MacBook Pro M3 Max 64GB	64 GB	Q4_K_M	28.1	Fits
MacBook Pro M1 Max 64GB	64 GB	Q4_K_M	25.8	Fits
MacBook Pro M4 Pro 48GB	48 GB	Q4_K_M	21.7	Fits
RTX 3060 12GB	12 GB	Q4_K_M	19.5	Offloads
RTX 4060 8GB	8 GB	Q4_K_M	7.2	Too big

Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

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	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

RX 7900 XT 20GBコスパ重視

800 GB/s (+527)

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

Raises estimated decode speed by about 159%.

〜$899 MSRP

RX 7900 XTX 24GBコスパ最良

960 GB/s (+687)

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

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: 21.7 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 21.7 tokens per second decode speed with a time-to-first-token of 8938ms 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 21.7 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.

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.

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

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