Intel Arc Pro B60 24GBコスパ最良456 GB/s (+256)
C
Raises estimated decode speed by about 76%.28.8 tok/sデコード
Raises estimated decode speed by about 76%.
〜$599 MSRP
Can internlm JanusCoder 14B run on MacBook Pro M2 Pro 32GB?
YES — Runs Great
C49Usable
Estimated from fit model
internlm JanusCoder 14B needs ~14.5 GB VRAM. MacBook Pro M2 Pro 32GB has 23.0 GB. With Q4_K_M quantization, expect ~16 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.
Select quantization to explore
Q4_K_M (Medium quality) — 14.5 GB, 16.4 tok/s, Runs well
14.5 GB required23.0 GB available
63% VRAM used
Fit status
Runs well
Decode
16.4 tok/s
TTFT
11810 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 MacBook Pro M2 Pro 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: 16.4 tok/s decode · 11.8s TTFT (warm) · 41 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 | 16.4 tok/s | 6442 ms | 99K |
| Coding | C | Runs well | 16.4 tok/s | 11810 ms | 99K |
| Agentic Coding | C | Runs well | 16.4 tok/s | 17178 ms | 99K |
| Reasoning | C | Runs well | 16.4 tok/s | 13957 ms | 99K |
| RAG | C | Runs well | 16.4 tok/s | 21472 ms | 99K |
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on MacBook Pro M2 Pro 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 | 7.8 GB | Medium | C47 |
Q4_K_M | 4 | 8.5 GB | Medium | C48 |
Q5_K_M | 5 | 10.1 GB | High | C49 |
Q6_K | 6 | 11.5 GB | High | C50 |
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
Frequently asked questions
Can MacBook Pro M2 Pro 32GB run internlm JanusCoder 14B?
Yes, MacBook Pro M2 Pro 32GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 16.4 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 MacBook Pro M2 Pro 32GB?
On MacBook Pro M2 Pro 32GB, internlm JanusCoder 14B achieves approximately 16.4 tokens per second decode speed with a time-to-first-token of 11810ms using Q4_K_M quantization.
Can MacBook Pro M2 Pro 32GB run internlm JanusCoder 14B for coding?
For coding workloads, internlm JanusCoder 14B on MacBook Pro M2 Pro 32GB receives a C grade with 16.4 tok/s and 99K context.
What context window can internlm JanusCoder 14B use on MacBook Pro M2 Pro 32GB?
On MacBook Pro M2 Pro 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.
Is unified memory on MacBook Pro M2 Pro 32GB as fast as VRAM for internlm JanusCoder 14B?
Not always. MacBook Pro M2 Pro 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.
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