RTX 3060 12GBコスパ重視12 GB VRAM (+1)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.19.5 tok/sデコード
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$329 MSRP
Can internlm JanusCoder 14B run on GTX 1080 Ti 11GB?
BARELY — Tight on Memory
D39Poor
Estimated from fit model
internlm JanusCoder 14B needs ~12.2 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~20 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: StandardBottleneck: Host offload
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) — 12.2 GB, 19.5 tok/s, Very compromised (needs ~0.8 GB host RAM)
12.2 GB required11.0 GB available
111% VRAM needed
1.2 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.8 GB host RAM)
Decode
19.5 tok/s
TTFT
9929 ms
Safe context
4K
Memory
12.2 GB / 11.0 GB
Offload
10%
Memory breakdown
Weights8.5 GB
KV Cache1.6 GB
Runtime0.9 GB
Headroom1.1 GB
See how fast it feels
See how fast it feelsinternlm JanusCoder 14B on GTX 1080 Ti 11GB
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: 19.5 tok/s decode · 9.9s TTFT (warm) · 49 tok/s prefill
What limits this setup
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Best improvement path
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.8 GB of extra host RAM just for the offloaded portion, before OS and other tools.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.3 GB host RAM) | 22.7 tok/s | 4647 ms | 4K |
| Coding | D | Very compromised (needs ~0.8 GB host RAM) | 19.5 tok/s | 9929 ms | 4K |
| Agentic Coding | F | Too heavy | 14.8 tok/s | 19062 ms | 4K |
| Reasoning | D | Very compromised (needs ~0.8 GB host RAM) | 19.5 tok/s | 11735 ms | 4K |
| RAG | F | Too heavy | 14.8 tok/s | 23827 ms | 4K |
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.5 GB | Low | C52 |
Q3_K_S | 3 | 6.9 GB | Low | C52 |
NVFP4Best for your GPU | 4 | 7.8 GB | Medium | C51 |
Q4_K_M | 4 | 8.5 GB | Medium | F0 |
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 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 3060 12GBコスパ重視RTX 5060 Ti 16GBコスパ最良16 GB VRAM (+5)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.31.5 tok/sデコード
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 62%.
〜$449 MSRP
RTX 4060 Ti 16GBNVIDIAアップグレード16 GB VRAM (+5)
C
Removes host-memory offload, which is usually the single biggest latency and throughput win.25.8 tok/sデコード
Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 32%.
〜$499 MSRP
Frequently asked questions
Can GTX 1080 Ti 11GB run internlm JanusCoder 14B?
Yes, GTX 1080 Ti 11GB can run internlm JanusCoder 14B with a D grade (Very compromised (needs ~0.8 GB host RAM)). Expected decode speed: 19.5 tok/s.
How much VRAM does internlm JanusCoder 14B need?
internlm JanusCoder 14B (14B parameters) requires approximately 12.2 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 GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, internlm JanusCoder 14B achieves approximately 19.5 tokens per second decode speed with a time-to-first-token of 9929ms using Q4_K_M quantization.
Can GTX 1080 Ti 11GB run internlm JanusCoder 14B for coding?
For coding workloads, internlm JanusCoder 14B on GTX 1080 Ti 11GB receives a D grade with 19.5 tok/s and 4K context.
What context window can internlm JanusCoder 14B use on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, internlm JanusCoder 14B can safely use up to 4K 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 GTX 1080 Ti 11GB?
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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