RTX 5060 Ti 16GBOpción económica16 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.31.5 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$449 MSRP
Can internlm JanusCoder 14B run on RTX 3080 12GB?
YES — With Offload
C52Usable
Estimated from fit model
internlm JanusCoder 14B needs ~12.3 GB VRAM. RTX 3080 12GB has 12.0 GB. With Q4_K_M quantization, expect ~57 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: HighStack: StandardBottleneck: 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) — 12.3 GB, 56.8 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
56.8 tok/s
TTFT
3407 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 3080 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: 56.8 tok/s decode · 3.4s TTFT (warm) · 142 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 | 79.5 tok/s | 1328 ms | 13K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 56.8 tok/s | 3407 ms | 13K |
| Agentic Coding | C | Very compromised (needs ~1.2 GB host RAM) | 43.6 tok/s | 6453 ms | 13K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 56.8 tok/s | 4027 ms | 13K |
| RAG | C | Very compromised (needs ~1.2 GB host RAM) | 43.6 tok/s | 8066 ms | 13K |
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 3080 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 | 7.8 GB | Medium | C51 |
Q4_K_MBest for your GPU | 4 | 8.5 GB | Medium | C51 |
Q5_K_M | 5 | 10.1 GB | High | F0 |
Q6_K | 6 | 11.5 GB | High | F0 |
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 startOpciones de mejora
Hardware que ejecuta bien internlm JanusCoder 14B
RTX 5060 Ti 16GBOpción económicaRTX 4060 Ti 16GBMejor relación calidad-precio16 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.25.8 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$499 MSRP
RTX 2000 Ada 16GBMejora NVIDIA16 GB VRAM (+4)
C
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.25.6 tok/s decodificación
Añade margen de memoria para más contexto y para que el modelo envejezca mejor.
~$625 MSRP
Frequently asked questions
Can RTX 3080 12GB run internlm JanusCoder 14B?
Yes, RTX 3080 12GB can run internlm JanusCoder 14B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 56.8 tok/s.
How much VRAM does internlm JanusCoder 14B need?
internlm JanusCoder 14B (14B parameters) requires approximately 12.3 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 3080 12GB?
On RTX 3080 12GB, internlm JanusCoder 14B achieves approximately 56.8 tokens per second decode speed with a time-to-first-token of 3407ms using Q4_K_M quantization.
Can RTX 3080 12GB run internlm JanusCoder 14B for coding?
For coding workloads, internlm JanusCoder 14B on RTX 3080 12GB receives a C grade with 56.8 tok/s and 13K context.
What context window can internlm JanusCoder 14B use on RTX 3080 12GB?
On RTX 3080 12GB, internlm JanusCoder 14B can safely use up to 13K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
What should I upgrade first if internlm JanusCoder 14B feels slow on RTX 3080 12GB?
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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