Can internlm JanusCoder 14B run on Mac Studio M3 Ultra 96GB?

YES — Runs Great

C47Usable

Estimated from fit model

internlm JanusCoder 14B needs ~21.4 GB VRAM. Mac Studio M3 Ultra 96GB has 69.1 GB. With Q4_K_M quantization, expect ~65 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 21.4 GB, 65.2 tok/s, Runs well

21.4 GB required69.1 GB available

31% VRAM used

Fit status

Runs well

Decode

65.2 tok/s

TTFT

2969 ms

Safe context

481K

Memory

21.4 GB / 69.1 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom10.4 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on Mac Studio M3 Ultra 96GB
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: 65.2 tok/s decode · 3.0s TTFT (warm) · 163 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	65.2 tok/s	1619 ms	481K
Coding	C	Runs well	65.2 tok/s	2969 ms	481K
Agentic Coding	C	Runs well	65.2 tok/s	4318 ms	481K
Reasoning	C	Runs well	65.2 tok/s	3508 ms	481K
RAG	C	Runs well	65.2 tok/s	5398 ms	481K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on Mac Studio M3 Ultra 96GB (69.1 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	D40
Q3_K_S	3	6.9 GB	Low	C40
NVFP4	4	7.8 GB	Medium	C40
Q4_K_M	4	8.5 GB	Medium	C40
Q5_K_M	5	10.1 GB	High	C40
Q6_K	6	11.5 GB	High	C41
Q8_0	8	15.0 GB	Very High	C41
F16Best for your GPU	16	28.7 GB	Maximum	C44

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

Frequently asked questions

Can Mac Studio M3 Ultra 96GB run internlm JanusCoder 14B?

Yes, Mac Studio M3 Ultra 96GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 65.2 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 21.4 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 Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, internlm JanusCoder 14B achieves approximately 65.2 tokens per second decode speed with a time-to-first-token of 2969ms using Q4_K_M quantization.

Can Mac Studio M3 Ultra 96GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on Mac Studio M3 Ultra 96GB receives a C grade with 65.2 tok/s and 481K context.

What context window can internlm JanusCoder 14B use on Mac Studio M3 Ultra 96GB?

On Mac Studio M3 Ultra 96GB, internlm JanusCoder 14B can safely use up to 481K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

Is unified memory on Mac Studio M3 Ultra 96GB as fast as VRAM for internlm JanusCoder 14B?

Not always. Mac Studio M3 Ultra 96GB 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 Mac Studio M3 Ultra 96GB See all hardware for internlm JanusCoder 14B

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