40 GB VRAM (+8)1555 GB/s (+979)
C
Raises estimated decode speed by about 183%.153 tok/s decode
Raises estimated decode speed by about 183%.
Adds memory headroom for longer context windows and future model growth.
~$10,000 MSRP
Can internlm JanusCoder 14B run on RTX 5000 Ada 32GB?
YES — Runs Great
C50Usable
○Estimated from fit model
internlm JanusCoder 14B needs ~14.6 GB VRAM. RTX 5000 Ada 32GB has 32.0 GB. With Q4_K_M quantization, expect ~54 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: MediumStack: BasicBottleneck: Balanced
Share:
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) — 14.6 GB, 54.0 tok/s, Runs well
14.6 GB required32.0 GB available
46% VRAM used
Fit status
Runs well
Decode
54.0 tok/s
TTFT
3588 ms
Safe context
186K
Memory
14.6 GB / 32.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsinternlm JanusCoder 14B on RTX 5000 Ada 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: 54.0 tok/s decode · 3.6s TTFT (warm) · 135 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 | 54.0 tok/s | 1957 ms | 186K |
| Coding | C | Runs well | 54.0 tok/s | 3588 ms | 186K |
| Agentic Coding | C | Runs well | 54.0 tok/s | 5219 ms | 186K |
| Reasoning | C | Runs well | 54.0 tok/s | 4240 ms | 186K |
| RAG | C | Runs well | 54.0 tok/s | 6524 ms | 186K |
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 5000 Ada 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C43 |
Q3_K_S | 3 | 6.9 GB | Low | C44 |
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 startUpgrade options