RTX 5060 Ti 16GBコスパ重視16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.31.5 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$449 MSRP
Can internlm JanusCoder 14B run on RTX 4070 12GB?
YES — With Offload
C50Usable
Estimated from fit model
internlm JanusCoder 14B needs ~12.3 GB VRAM. RTX 4070 12GB has 12.0 GB. With Q4_K_M quantization, expect ~33 tok/s.
Runtime: llama.cppCapacity: OffloadBandwidth: MediumStack: 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, 33.2 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
33.2 tok/s
TTFT
5831 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 4070 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: 33.2 tok/s decode · 5.8s TTFT (warm) · 83 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 | 46.5 tok/s | 2272 ms | 13K |
| Coding | C | Runs with offload (needs ~0.2 GB host RAM) | 33.2 tok/s | 5831 ms | 13K |
| Agentic Coding | D | Very compromised (needs ~1.2 GB host RAM) | 25.5 tok/s | 11044 ms | 13K |
| Reasoning | C | Runs with offload (needs ~0.2 GB host RAM) | 33.2 tok/s | 6891 ms | 13K |
| RAG | D | Very compromised (needs ~1.2 GB host RAM) | 25.5 tok/s | 13805 ms | 13K |
Inference speed
internlm JanusCoder 14B inference speed — tokens per second by GPU & Mac
Estimated decode speed (tokens/sec) for internlm JanusCoder 14B at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~141 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
| RTX 5090 32GB | 32 GB | Q4_K_M | 140.6 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 89.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 80.9 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 76.7 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 75.1 | Fits |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 65.2 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 54.3 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 51.5 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 35.4 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 35.4 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 33.2 | Offloads |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 28.1 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 25.8 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.7 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 19.5 | Offloads |
| RTX 4060 8GB | 8 GB | Q4_K_M | 7.2 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
Quantization options
How internlm JanusCoder 14B (14B params) fits at each quantization level on RTX 4070 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 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 5060 Ti 16GBコスパ重視RTX 4060 Ti 16GBコスパ最良16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.25.8 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$499 MSRP
RTX 2000 Ada 16GBNVIDIAアップグレード16 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.25.6 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$625 MSRP
Frequently asked questions
Can RTX 4070 12GB run internlm JanusCoder 14B?
Yes, RTX 4070 12GB can run internlm JanusCoder 14B with a C grade (Runs with offload (needs ~0.2 GB host RAM)). Expected decode speed: 33.2 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 4070 12GB?
On RTX 4070 12GB, internlm JanusCoder 14B achieves approximately 33.2 tokens per second decode speed with a time-to-first-token of 5831ms using Q4_K_M quantization.
Can RTX 4070 12GB run internlm JanusCoder 14B for coding?
For coding workloads, internlm JanusCoder 14B on RTX 4070 12GB receives a C grade with 33.2 tok/s and 13K context.
What context window can internlm JanusCoder 14B use on RTX 4070 12GB?
On RTX 4070 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 4070 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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