16 GB VRAM (+4)448 GB/s (+160)
C
Raises estimated decode speed by about 68%.31.5 tok/s decode
Raises estimated decode speed by about 68%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Can internlm JanusCoder 14B run on RTX A2000 12GB?
YES — With Offload
C48Usable
○Estimated from fit model
internlm JanusCoder 14B needs ~12.3 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q4_K_M quantization, expect ~19 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: 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) — 12.3 GB, 18.8 tok/s, Runs with offload (needs ~0.2 GB host RAM)
12.3 GB required12.0 GB available
103% VRAM needed
0.3 GB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.2 GB host RAM)
Decode
18.8 tok/s
TTFT
10303 ms
Safe context
13K
Memory
12.3 GB / 12.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom1.2 GB
See how fast it feels
See how fast it feelsinternlm JanusCoder 14B on RTX A2000 12GB
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: 18.8 tok/s decode · 10.3s TTFT (warm) · 47 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Best improvement path
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload | 26.3 tok/s | 4015 ms | 13K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 18.8 tok/s | 10303 ms | 13K |
| Agentic Coding | D | Very compromised (needs ~1.2 GB host RAM) | 14.4 tok/s | 19512 ms | 13K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 18.8 tok/s | 12176 ms | 13K |
| RAG | D | Very compromised (needs ~1.2 GB host RAM) | 14.4 tok/s |
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C52 |
Q3_K_S | 3 | 6.9 GB | Low | C52 |
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
16 GB VRAM (+4)
C
Raises estimated decode speed by about 37%.25.8 tok/s decode
Raises estimated decode speed by about 37%.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
16 GB VRAM (+4)
C
Raises estimated decode speed by about 36%.25.6 tok/s decode
Raises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
~$625 MSRP