internlm2 limarp chat 20b needs ~20.5 GB VRAM. RTX 6000 Ada 48GB has 48.0 GB. With Q4_K_M quantization, expect ~65 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
64.5 tok/s
TTFT
3000 ms
Safe context
203K
Memory
20.5 GB / 48.0 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 | 64.5 tok/s | 1637 ms | 203K |
| Coding | C | Runs well | 64.5 tok/s | 3000 ms | 203K |
| Agentic Coding | C | Runs well | 64.5 tok/s | 4364 ms | 203K |
| Reasoning | C | Runs well | 64.5 tok/s | 3546 ms | 203K |
| RAG | C | Runs well | 64.5 tok/s | 5455 ms | 203K |
How internlm2 limarp chat 20b (20B params) fits at each quantization level on RTX 6000 Ada 48GB (48.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C42 |
Q3_K_S | 3 | 9.8 GB | Low | C42 |
NVFP4 | 4 | 11.2 GB | Medium | C43 |
Q4_K_M | 4 | 12.2 GB | Medium | C43 |
Q5_K_M | 5 | 14.4 GB | High | C44 |
Q6_K | 6 | 16.4 GB | High | C44 |
Q8_0 | 8 | 21.4 GB | Very High | C46 |
F16Best for your GPU | 16 | 41.0 GB | Maximum | C47 |
Copy-paste commands to run internlm2 limarp chat 20b on your machine.
Run
lms load hf-intervitens-archive--internlm2-limarp-chat-20b-gguf && lms server startYes, RTX 6000 Ada 48GB can run internlm2 limarp chat 20b with a C grade (Runs well). Expected decode speed: 64.5 tok/s.
internlm2 limarp chat 20b (20B parameters) requires approximately 20.5 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 limarp chat 20b is Q4_K_M, which balances quality and memory efficiency.
On RTX 6000 Ada 48GB, internlm2 limarp chat 20b achieves approximately 64.5 tokens per second decode speed with a time-to-first-token of 3000ms using Q4_K_M quantization.
For coding workloads, internlm2 limarp chat 20b on RTX 6000 Ada 48GB receives a C grade with 64.5 tok/s and 203K context.
On RTX 6000 Ada 48GB, internlm2 limarp chat 20b can safely use up to 203K 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-intervitens-archive--internlm2-limarp-chat-20b-gguf-on-rtx-6000-ada-48gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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