RTX 5090 32GB经济之选32 GB VRAM (+8)1792 GB/s (+1360)
C
Raises estimated decode speed by about 251%.140.6 tok/s 解码
Raises estimated decode speed by about 251%.
Adds memory headroom for longer context windows and future model growth.
~$1,999 MSRP
Can internlm JanusCoder 14B run on RTX 4500 Ada 24GB?
YES — Runs Great
C51Usable
Estimated from fit model
internlm JanusCoder 14B needs ~13.8 GB VRAM. RTX 4500 Ada 24GB has 24.0 GB. With Q4_K_M quantization, expect ~40 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) — 13.8 GB, 40.0 tok/s, Runs well
13.8 GB required24.0 GB available
58% VRAM used
Fit status
Runs well
Decode
40.0 tok/s
TTFT
4845 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 RTX 4500 Ada 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: 40.0 tok/s decode · 4.8s TTFT (warm) · 100 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 | 40.0 tok/s | 2642 ms | 116K |
| Coding | C | Runs well | 40.0 tok/s | 4845 ms | 116K |
| Agentic Coding | C | Runs well | 40.0 tok/s | 7047 ms | 116K |
| Reasoning | C | Runs well | 40.0 tok/s | 5725 ms | 116K |
| RAG | C | Runs well | 40.0 tok/s | 8808 ms | 116K |
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 4500 Ada 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 | 7.8 GB | Medium | C47 |
Q4_K_M | 4 | 8.5 GB | Medium | C47 |
Q5_K_M | 5 | 10.1 GB | High | C48 |
Q6_K | 6 | 11.5 GB | High | C49 |
Q8_0Best for your GPU | 8 | 15.0 GB | Very High | C50 |
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升级选项
能流畅运行 internlm JanusCoder 14B 的硬件
Frequently asked questions
Can RTX 4500 Ada 24GB run internlm JanusCoder 14B?
Yes, RTX 4500 Ada 24GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 40.0 tok/s.
How much VRAM does internlm JanusCoder 14B need?
internlm JanusCoder 14B (14B parameters) requires approximately 13.8 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 4500 Ada 24GB?
On RTX 4500 Ada 24GB, internlm JanusCoder 14B achieves approximately 40.0 tokens per second decode speed with a time-to-first-token of 4845ms using Q4_K_M quantization.
Can RTX 4500 Ada 24GB run internlm JanusCoder 14B for coding?
For coding workloads, internlm JanusCoder 14B on RTX 4500 Ada 24GB receives a C grade with 40.0 tok/s and 116K context.
What context window can internlm JanusCoder 14B use on RTX 4500 Ada 24GB?
On RTX 4500 Ada 24GB, internlm JanusCoder 14B can safely use up to 116K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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