Is unified memory on MacBook Air M2 16GB as fast as VRAM for internlm JanusCoder 14B?

Not always. MacBook Air M2 16GB 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 Air M2 16GB?

Q: What should I upgrade first if internlm JanusCoder 14B feels slow on MacBook Air M2 16GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

BARELY — Tight on Memory

D35Poor

Estimated from fit model

internlm JanusCoder 14B needs ~12.8 GB VRAM. MacBook Air M2 16GB has 11.5 GB. With Q4_K_M quantization, expect ~6 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: Very lowStack: StandardBottleneck: Host offload

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) — 12.8 GB, 6.4 tok/s, Very compromised (needs ~0.9 GB host RAM)

12.8 GB required11.5 GB available

111% VRAM needed

1.3 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.9 GB host RAM)

Decode

6.4 tok/s

TTFT

30254 ms

Safe context

Memory

12.8 GB / 11.5 GB

Offload

10%

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.7 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on MacBook Air M2 16GB
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: 6.4 tok/s decode · 30.3s TTFT (warm) · 16 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Increase host RAM if you keep offloading

This setup may need roughly 0.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs with offload (needs ~0.3 GB host RAM)	7.0 tok/s	14995 ms	4K
Coding	D	Very compromised (needs ~0.9 GB host RAM)	6.4 tok/s	30254 ms	4K
Agentic Coding	F	Too heavy	5.5 tok/s	51311 ms	4K
Reasoning	D	Very compromised (needs ~0.9 GB host RAM)	6.4 tok/s	35755 ms	4K
RAG	F	Too heavy	5.5 tok/s	64139 ms

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on MacBook Air M2 16GB (11.5 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C52
Q3_K_S	3	6.9 GB	Low	C52
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 M4 32GBBudget pick

32 GB Unified (+16)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.8.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 39%.

~$799 MSRP

MacBook Air M4 24GBBest value

24 GB Unified (+8)120 GB/s (+20)

Removes host-memory offload, which is usually the single biggest latency and throughput win.8.9 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 39%.

~$1,099 MSRP

MacBook Pro M3 24GBApple upgrade

24 GB Unified (+8)

Removes host-memory offload, which is usually the single biggest latency and throughput win.8 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

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

~$1,099 MSRP

RTX 5080 Laptop 16GBBiggest leap

768 GB/s (+668)

Removes host-memory offload, which is usually the single biggest latency and throughput win.75.5 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 1080%.

Frequently asked questions

Can MacBook Air M2 16GB run internlm JanusCoder 14B?

Yes, MacBook Air M2 16GB can run internlm JanusCoder 14B with a D grade (Very compromised (needs ~0.9 GB host RAM)). Expected decode speed: 6.4 tok/s.

How much VRAM does internlm JanusCoder 14B need?

internlm JanusCoder 14B (14B parameters) requires approximately 12.8 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 Air M2 16GB?

On MacBook Air M2 16GB, internlm JanusCoder 14B achieves approximately 6.4 tokens per second decode speed with a time-to-first-token of 30254ms using Q4_K_M quantization.

Can MacBook Air M2 16GB run internlm JanusCoder 14B for coding?

For coding workloads, internlm JanusCoder 14B on MacBook Air M2 16GB receives a D grade with 6.4 tok/s and 4K context.

What context window can internlm JanusCoder 14B use on MacBook Air M2 16GB?

On MacBook Air M2 16GB, internlm JanusCoder 14B can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for MacBook Air M2 16GB See all hardware for internlm JanusCoder 14B

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