internlm2 limarp chat 20b needs ~12.5 GB VRAM. RTX A2000 12GB has 12.0 GB. With Q2_K quantization, expect ~17 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
4.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
6.7 tok/s
TTFT
28980 ms
Safe context
4K
Memory
16.9 GB / 12.0 GB
Offload
30%
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.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 7.8 tok/s | 13594 ms | 4K |
| Coding | F | Too heavy | 6.7 tok/s | 28980 ms | 4K |
| Agentic Coding | F | Too heavy | 5.1 tok/s | 55370 ms | 4K |
| Reasoning | F | Too heavy | 6.7 tok/s | 34250 ms | 4K |
| RAG | F | Too heavy | 5.1 tok/s | 69212 ms | 4K |
How internlm2 limarp chat 20b (20B params) fits at each quantization level on RTX A2000 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 7.8 GB | Low | C51 |
Q3_K_S | 3 | 9.8 GB | Low | F0 |
NVFP4 | 4 | 11.2 GB | Medium | F0 |
Q4_K_M | 4 | 12.2 GB | Medium | F0 |
Q5_K_M | 5 | 14.4 GB | High | F0 |
Q6_K | 6 | 16.4 GB | High | F0 |
Q8_0 | 8 | 21.4 GB | Very High | F0 |
F16 | 16 | 41.0 GB | Maximum | F0 |
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 startアップグレードオプション
RTX 5060 Ti 16GBコスパ最良Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 121%.
〜$449 MSRP
RTX 4060 Ti 16GBNVIDIAアップグレードMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 63%.
〜$499 MSRP
RTX 4000 Ada 20GBコスパ重視Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$1,250 MSRP
RTX 4090 24GB最大の飛躍Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
〜$1,599 MSRP
Yes, RTX A2000 12GB can run internlm2 limarp chat 20b at Q2_K quantization (Runs with offload (needs ~0.3 GB host RAM)). The recommended Q4_K_M requires 16.9 GB which exceeds available memory, but at Q2_K it needs only 12.5 GB. Expected decode speed: 16.7 tok/s.
internlm2 limarp chat 20b (20B parameters) requires approximately 16.9 GB at Q4_K_M quantization. On RTX A2000 12GB, it fits at Q2_K using 12.5 GB.
The recommended quantization is Q4_K_M, but on RTX A2000 12GB the best fitting quantization is Q2_K, which uses 12.5 GB.
On RTX A2000 12GB, internlm2 limarp chat 20b achieves approximately 16.7 tokens per second decode speed with a time-to-first-token of 11576ms using Q2_K quantization.
For coding workloads, internlm2 limarp chat 20b on RTX A2000 12GB receives a F grade with 6.7 tok/s and 4K context.
On RTX A2000 12GB, internlm2 limarp chat 20b can safely use up to 12K tokens of context at Q2_K quantization. The model's official context limit is —, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
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-a2000-12gb" 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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