32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 517%.140.6 tok/s decode
Raises estimated decode speed by about 517%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can internlm JanusCoder 14B run on NVIDIA L4 24GB?
YES — Runs Great
C49Usable
○Estimated from fit model
internlm JanusCoder 14B needs ~13.8 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~23 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: 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) — 13.8 GB, 22.8 tok/s, Runs well
13.8 GB required24.0 GB available
58% VRAM used
Fit status
Runs well
Decode
22.8 tok/s
TTFT
8479 ms
Safe context
116K
Memory
13.8 GB / 24.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsinternlm JanusCoder 14B on NVIDIA L4 24GB
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: 22.8 tok/s decode · 8.5s TTFT (warm) · 57 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 | 22.8 tok/s | 4625 ms | 116K |
| Coding | C | Runs well | 22.8 tok/s | 8479 ms | 116K |
| Agentic Coding | C | Runs well | 22.8 tok/s | 12333 ms | 116K |
| Reasoning | C | Runs well | 22.8 tok/s | 10020 ms | 116K |
| RAG | C | Runs well | 22.8 tok/s | 15416 ms | 116K |
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C45 |
Q3_K_S | 3 | 6.9 GB | Low | C46 |
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
Hardware that runs internlm JanusCoder 14B well
32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 286%.88.1 tok/s decode
Raises estimated decode speed by about 286%.
Adds memory headroom for longer context windows and future model growth.
~$2,499 MSRP
32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 137%.54 tok/s decode
Raises estimated decode speed by about 137%.
Adds memory headroom for longer context windows and future model growth.
~$4,000 MSRP