96 GB VRAM (+32)1792 GB/s (+1192)
C
Raises estimated decode speed by about 222%.176.3 tok/s decode
Raises estimated decode speed by about 222%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
Can internlm JanusCoder 14B run on NVIDIA A16 64GB?
YES — Runs Great
C46Usable
○Estimated from fit model
internlm JanusCoder 14B needs ~17.8 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q4_K_M quantization, expect ~55 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) — 17.8 GB, 54.8 tok/s, Runs well
17.8 GB required64.0 GB available
28% VRAM used
Fit status
Runs well
Decode
54.8 tok/s
TTFT
3533 ms
Safe context
467K
Memory
17.8 GB / 64.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom6.4 GB
See how fast it feels
See how fast it feelsinternlm JanusCoder 14B on NVIDIA A16 64GB
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: 54.8 tok/s decode · 3.5s TTFT (warm) · 137 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 | 54.8 tok/s | 1927 ms | 467K |
| Coding | C | Runs well | 54.8 tok/s | 3533 ms | 467K |
| Agentic Coding | C | Runs well | 54.8 tok/s | 5139 ms | 467K |
| Reasoning | C | Runs well | 54.8 tok/s | 4175 ms | 467K |
| RAG | C | Runs well | 54.8 tok/s | 6423 ms | 467K |
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C40 |
Q3_K_S | 3 | 6.9 GB | Low | C40 |
NVFP4 | 4 | 7.8 GB | Medium | C40 |
Q4_K_M | 4 | 8.5 GB | Medium | C41 |
Q5_K_M | 5 | 10.1 GB | High | C41 |
Q6_K | 6 | 11.5 GB | High | C41 |
Q8_0 | 8 | 15.0 GB | Very High | C42 |
F16Best for your GPU | 16 | 28.7 GB | Maximum | C45 |
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
96 GB VRAM (+32)1597 GB/s (+997)
C
Raises estimated decode speed by about 187%.157.1 tok/s decode
Raises estimated decode speed by about 187%.
Adds memory headroom for longer context windows and future model growth.
~$9,999 MSRP
96 GB VRAM (+32)4000 GB/s (+3400)
C
Raises estimated decode speed by about 258%.196 tok/s decode
Raises estimated decode speed by about 258%.
Adds memory headroom for longer context windows and future model growth.
~$12,000 MSRP