RTX PRO 6000 Blackwell Workstation Edition 96GBOpção econômica1792 GB/s (+992)
C
Raises estimated decode speed by about 242%.176.3 tok/s decodificação
Raises estimated decode speed by about 242%.
~$9,999 MSRP
Can internlm JanusCoder 14B run on Mac Studio M1 Ultra 128GB?
YES — Runs Great
C45Usable
Estimated from fit model
internlm JanusCoder 14B needs ~24.9 GB VRAM. Mac Studio M1 Ultra 128GB has 92.2 GB. With Q4_K_M quantization, expect ~52 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.
Select quantization to explore
Q4_K_M (Medium quality) — 24.9 GB, 51.5 tok/s, Runs well
24.9 GB required92.2 GB available
27% VRAM used
Fit status
Runs well
Decode
51.5 tok/s
TTFT
3758 ms
Safe context
672K
Memory
24.9 GB / 92.2 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom13.8 GB
See how fast it feels
See how fast it feelsinternlm JanusCoder 14B on Mac Studio M1 Ultra 128GB
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: 51.5 tok/s decode · 3.8s TTFT (warm) · 129 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 | 51.5 tok/s | 2050 ms | 672K |
| Coding | C | Runs well | 51.5 tok/s | 3758 ms | 672K |
| Agentic Coding | C | Runs well | 51.5 tok/s | 5466 ms | 672K |
| Reasoning | C | Runs well | 51.5 tok/s | 4441 ms | 672K |
| RAG | C | Runs well | 51.5 tok/s | 6832 ms | 672K |
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on Mac Studio M1 Ultra 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | D39 |
Q3_K_S | 3 | 6.9 GB | Low | D39 |
NVFP4 | 4 | 7.8 GB | Medium | D39 |
Q4_K_M | 4 | 8.5 GB | Medium | D39 |
Q5_K_M | 5 | 10.1 GB | High | D39 |
Q6_K | 6 | 11.5 GB | High | D39 |
Q8_0 | 8 | 15.0 GB | Very High | D40 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | C42 |
Get started
Copy-paste commands to run internlm JanusCoder 14B on your machine.
Run
lms load hf-bartowski--internlm-januscoder-14b-gguf && lms server startOpções de upgrade
Hardware que roda bem internlm JanusCoder 14B
Frequently asked questions
Can Mac Studio M1 Ultra 128GB run internlm JanusCoder 14B?
Yes, Mac Studio M1 Ultra 128GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 51.5 tok/s.
How much VRAM does internlm JanusCoder 14B need?
internlm JanusCoder 14B (14B parameters) requires approximately 24.9 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 M1 Ultra 128GB?
On Mac Studio M1 Ultra 128GB, internlm JanusCoder 14B achieves approximately 51.5 tokens per second decode speed with a time-to-first-token of 3758ms using Q4_K_M quantization.
Can Mac Studio M1 Ultra 128GB run internlm JanusCoder 14B for coding?
For coding workloads, internlm JanusCoder 14B on Mac Studio M1 Ultra 128GB receives a C grade with 51.5 tok/s and 672K context.
What context window can internlm JanusCoder 14B use on Mac Studio M1 Ultra 128GB?
On Mac Studio M1 Ultra 128GB, internlm JanusCoder 14B can safely use up to 672K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Is unified memory on Mac Studio M1 Ultra 128GB as fast as VRAM for internlm JanusCoder 14B?
Not always. Mac Studio M1 Ultra 128GB 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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