Is unified memory on Mac mini M4 32GB as fast as VRAM for internlm JanusCoder 14B?

Not always. Mac mini M4 32GB 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 Mac mini M4 32GB?

YES — Runs Great

C48Usable

Estimated — low-sample bucket· few comparable runs

internlm JanusCoder 14B needs ~14.5 GB VRAM. Mac mini M4 32GB has 23.0 GB. With Q4_K_M quantization, expect ~9 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) — 14.5 GB, 8.9 tok/s, Runs well

14.5 GB required23.0 GB available

63% VRAM used

Fit status

Runs well

Decode

8.9 tok/s

TTFT

21745 ms

Safe context

99K

Memory

14.5 GB / 23.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom3.5 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on Mac mini M4 32GB
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: 8.9 tok/s decode · 21.7s TTFT (warm) · 22 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	8.9 tok/s	11861 ms	99K
Coding	C	Runs well	8.9 tok/s	21745 ms	99K
Agentic Coding	C	Runs well	8.9 tok/s	31630 ms	99K
Reasoning	C	Runs well	8.9 tok/s	25699 ms	99K
RAG	C	Runs well	8.9 tok/s	39537 ms	99K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on Mac mini M4 32GB (23.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C46
Q3_K_S	3	6.9 GB	Low	C47
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

MacBook Pro M3 Pro 36GBBudget pick

36 GB Unified (+4)150 GB/s (+30)

Raises estimated decode speed by about 44%.12.8 tok/s decode

Raises estimated decode speed by about 44%.

~$1,999 MSRP

MacBook Pro M4 Max 36GBBest value

36 GB Unified (+4)410 GB/s (+290)

Raises estimated decode speed by about 239%.30.2 tok/s decode

Raises estimated decode speed by about 239%.

~$2,499 MSRP

Mac Studio M2 Ultra 64GBApple upgrade

64 GB Unified (+32)800 GB/s (+680)

Raises estimated decode speed by about 510%.54.3 tok/s decode

Raises estimated decode speed by about 510%.

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

~$3,999 MSRP

Frequently asked questions

Can Mac mini M4 32GB run internlm JanusCoder 14B?

Yes, Mac mini M4 32GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 8.9 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 14.5 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 Mac mini M4 32GB?

On Mac mini M4 32GB, internlm JanusCoder 14B achieves approximately 8.9 tokens per second decode speed with a time-to-first-token of 21745ms using Q4_K_M quantization.

Can Mac mini M4 32GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Mac mini M4 32GB receives a C grade with 8.9 tok/s and 99K context.

What context window can internlm JanusCoder 14B use on Mac mini M4 32GB?

On Mac mini M4 32GB, internlm JanusCoder 14B can safely use up to 99K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for Mac mini M4 32GB See all hardware for internlm JanusCoder 14B

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