Can internlm JanusCoder 14B run on RTX 5000 Ada Laptop 16GB?

YES — Runs Great

C55Usable

○Estimated from fit model

internlm JanusCoder 14B needs ~12.7 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 12.7 GB, 51.7 tok/s, Runs well

12.7 GB required16.0 GB available

79% VRAM used

Fit status

Runs well

Decode

51.7 tok/s

TTFT

3745 ms

Safe context

48K

Memory

12.7 GB / 16.0 GB

Memory breakdown

Weights8.5 GB

KV Cache1.6 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsinternlm JanusCoder 14B on RTX 5000 Ada Laptop 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: 51.7 tok/s decode · 3.7s TTFT (warm) · 129 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	51.7 tok/s	2043 ms	48K
Coding	C	Runs well	51.7 tok/s	3745 ms	48K
Agentic Coding	C	Tight fit	51.7 tok/s	5447 ms	48K
Reasoning	C	Runs well	51.7 tok/s	4426 ms	48K
RAG	C	Tight fit	51.7 tok/s	6809 ms	48K

Quantization options

How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 5000 Ada Laptop 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.5 GB	Low	C49
Q3_K_S	3	6.9 GB	Low	C50
NVFP4	4	7.8 GB	Medium	C51
Q4_K_M	4	8.5 GB	Medium	C51
Q5_K_M	5	10.1 GB	High	C51
Q6_KBest for your GPU	6	11.5 GB	High	C50
Q8_0	8	15.0 GB	Very High	F0
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

Frequently asked questions

See all results for RTX 5000 Ada Laptop 16GB See all hardware for internlm JanusCoder 14B

URL: https://willitrunai.com/can-run/hf-bartowski--internlm-januscoder-14b-gguf-on-rtx-5000-ada-laptop-16gb

⇱ internlm JanusCoder 14B on RTX 5000 Ada Laptop 16GB? YES