internlm2 limarp chat 20b needs ~17.3 GB VRAM. RTX 5070 Ti 16GB has 16.0 GB. With Q4_K_M quantization, expect ~31 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
1.3 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.9 GB host RAM)
Decode
30.5 tok/s
TTFT
6349 ms
Safe context
7K
Memory
17.3 GB / 16.0 GB
Offload
10%
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.
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 0.9 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs with offload (needs ~0.1 GB host RAM) | 35.2 tok/s | 3003 ms | 7K |
| Coding | D | Very compromised (needs ~0.9 GB host RAM) | 30.5 tok/s | 6349 ms | 7K |
| Agentic Coding | F | Too heavy | 23.5 tok/s | 11961 ms | 7K |
| Reasoning | D | Very compromised (needs ~0.9 GB host RAM) | 30.5 tok/s | 7504 ms | 7K |
| RAG | F | Too heavy | 23.5 tok/s | 14951 ms | 7K |
How internlm2 limarp chat 20b (20B params) fits at each quantization level on RTX 5070 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | C51 |
Q3_K_S | 3 | 9.8 GB | Low | C51 |
NVFP4 | 4 | 11.2 GB | Medium | C50 |
Q4_K_MBest for your GPU | 4 | 12.2 GB | Medium | C50 |
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 startUpgrade-Optionen
RTX 4000 Ada 20GBGünstige WahlRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Adds memory headroom for longer context windows and future model growth.
ca. $1,250 MSRP
RTX 3090 24GBBestes Preis-Leistungs-VerhältnisRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 76%.
ca. $1,499 MSRP
RTX 4090 24GBNVIDIA-UpgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 106%.
ca. $1,599 MSRP
Yes, RTX 5070 Ti 16GB can run internlm2 limarp chat 20b with a D grade (Very compromised (needs ~0.9 GB host RAM)). Expected decode speed: 30.5 tok/s.
internlm2 limarp chat 20b (20B parameters) requires approximately 17.3 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 5070 Ti 16GB, internlm2 limarp chat 20b achieves approximately 30.5 tokens per second decode speed with a time-to-first-token of 6349ms using Q4_K_M quantization.
For coding workloads, internlm2 limarp chat 20b on RTX 5070 Ti 16GB receives a D grade with 30.5 tok/s and 7K context.
On RTX 5070 Ti 16GB, internlm2 limarp chat 20b can safely use up to 7K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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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