Can internlm JanusCoder 14B run on MacBook Pro M4 Max 36GB?

YES — Runs Great

C50Usable

○Estimated from fit model

internlm JanusCoder 14B needs ~15.0 GB VRAM. MacBook Pro M4 Max 36GB has 25.9 GB. With Q4_K_M quantization, expect ~30 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) — 15.0 GB, 30.2 tok/s, Runs well

15.0 GB required25.9 GB available

58% VRAM used

Fit status

Runs well

Decode

30.2 tok/s

TTFT

6401 ms

Safe context

123K

Memory

15.0 GB / 25.9 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom3.9 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on MacBook Pro M4 Max 36GB
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: 30.2 tok/s decode · 6.4s TTFT (warm) · 76 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	30.2 tok/s	3491 ms	123K
Coding	C	Runs well	30.2 tok/s	6401 ms	123K
Agentic Coding	C	Runs well	30.2 tok/s	9310 ms	123K
Reasoning	C	Runs well	30.2 tok/s	7565 ms	123K
RAG	C	Runs well	30.2 tok/s	11638 ms	123K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on MacBook Pro M4 Max 36GB (25.9 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C45
Q3_K_S	3	6.9 GB	Low	C46
NVFP4	4	7.8 GB	Medium	C46
Q4_K_M	4	8.5 GB	Medium	C46
Q5_K_M	5	10.1 GB	High	C47
Q6_K	6	11.5 GB	High	C48
Q8_0Best for your GPU	8	15.0 GB	Very High	C50
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

Upgrade options

Hardware that runs internlm JanusCoder 14B well

👁 NVIDIA
RTX 5090 32GBBudget pick

1792 GB/s (+1382)

Raises estimated decode speed by about 352%.136.4 tok/s decode

Raises estimated decode speed by about 352%.

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

~$1,999 MSRP

👁 NVIDIA
RTX PRO 4500 Blackwell 32GBBest value

896 GB/s (+486)

Raises estimated decode speed by about 192%.88.1 tok/s decode

Raises estimated decode speed by about 192%.