internlm JanusCoder 14B needs ~13.8 GB VRAM. NVIDIA A30 24GB has 24.0 GB. With Q4_K_M quantization, expect ~85 tok/s.
Operating mode
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
Fit status
Runs well
Decode
85.2 tok/s
TTFT
2272 ms
Safe context
116K
Memory
13.8 GB / 24.0 GB
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 85.2 tok/s | 1239 ms | 116K |
| Coding | C | Runs well | 85.2 tok/s | 2272 ms | 116K |
| Agentic Coding | B | Runs well | 85.2 tok/s | 3305 ms | 116K |
| Reasoning | C | Runs well | 85.2 tok/s | 2685 ms | 116K |
| RAG | B | Runs well | 85.2 tok/s | 4131 ms | 116K |
How internlm JanusCoder 14B (14B params) fits at each quantization level on NVIDIA A30 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 |
Copy-paste commands to run internlm JanusCoder 14B on your machine.
Run
lms load hf-bartowski--internlm-januscoder-14b-gguf && lms server startYes, NVIDIA A30 24GB can run internlm JanusCoder 14B with a C grade (Runs well). Expected decode speed: 85.2 tok/s.
internlm JanusCoder 14B (14B parameters) requires approximately 13.8 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm JanusCoder 14B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA A30 24GB, internlm JanusCoder 14B achieves approximately 85.2 tokens per second decode speed with a time-to-first-token of 2272ms using Q4_K_M quantization.
For coding workloads, internlm JanusCoder 14B on NVIDIA A30 24GB receives a C grade with 85.2 tok/s and 116K context.
On NVIDIA A30 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.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/hf-bartowski--internlm-januscoder-14b-gguf-on-a30-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
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 |