RTX 4090 24GBコスパ重視24 GB VRAM (+8)1008 GB/s (+560)
C
Raises estimated decode speed by about 144%.89.7 tok/sデコード
Raises estimated decode speed by about 144%.
Adds memory headroom for longer context windows and future model growth.
〜$1,599 MSRP
Can internlm JanusCoder 14B run on RTX A4000 16GB?
YES — Runs Great
C54Usable
Estimated from fit model
internlm JanusCoder 14B needs ~13.0 GB VRAM. RTX A4000 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: 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.
Select quantization to explore
Q4_K_M (Medium quality) — 13.0 GB, 36.7 tok/s, Runs well
13.0 GB required16.0 GB available
81% VRAM used
Fit status
Runs well
Decode
36.7 tok/s
TTFT
5272 ms
Safe context
45K
Memory
13.0 GB / 16.0 GB
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime1.2 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsinternlm JanusCoder 14B on RTX A4000 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: 36.7 tok/s decode · 5.3s TTFT (warm) · 92 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 | 36.7 tok/s | 2876 ms | 45K |
| Coding | C | Runs well | 36.7 tok/s | 5272 ms | 45K |
| Agentic Coding | C | Tight fit | 36.7 tok/s | 7669 ms | 45K |
| Reasoning | C | Runs well | 36.7 tok/s | 6231 ms | 45K |
| RAG | C | Tight fit | 36.7 tok/s | 9586 ms | 45K |
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX A4000 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アップグレードオプション
internlm JanusCoder 14Bを快適に動かすハードウェア
RTX 4090 24GBコスパ重視RTX 3090 Ti 24GBNVIDIAアップグレード24 GB VRAM (+8)1008 GB/s (+560)
C
Raises estimated decode speed by about 128%.83.8 tok/sデコード
Raises estimated decode speed by about 128%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
RTX A4500 20GBコスパ最良20 GB VRAM (+4)640 GB/s (+192)
C
Raises estimated decode speed by about 59%.58.5 tok/sデコード
Raises estimated decode speed by about 59%.
Adds memory headroom for longer context windows and future model growth.
〜$2,000 MSRP
Frequently asked questions
Can RTX A4000 16GB run internlm JanusCoder 14B?
Yes, RTX A4000 16GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 36.7 tok/s.
How much VRAM does internlm JanusCoder 14B need?
internlm JanusCoder 14B (14B parameters) requires approximately 13.0 GB of memory with Q4_K_M quantization.
What is the best quantization for internlm JanusCoder 14B?
The recommended quantization for internlm JanusCoder 14B is Q4_K_M, which balances quality and memory efficiency.
What speed will internlm JanusCoder 14B run at on RTX A4000 16GB?
On RTX A4000 16GB, internlm JanusCoder 14B achieves approximately 36.7 tokens per second decode speed with a time-to-first-token of 5272ms using Q4_K_M quantization.
Can RTX A4000 16GB run internlm JanusCoder 14B for coding?
For coding workloads, internlm JanusCoder 14B on RTX A4000 16GB receives a C grade with 36.7 tok/s and 45K context.
What context window can internlm JanusCoder 14B use on RTX A4000 16GB?
On RTX A4000 16GB, internlm JanusCoder 14B can safely use up to 45K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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