internlm2 math plus 20b i1 needs ~12.4 GB VRAM. RTX 2080 Ti 11GB has 11.0 GB. With Q2_K quantization, expect ~24 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
5.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
9.4 tok/s
TTFT
20614 ms
Safe context
4K
Memory
16.8 GB / 11.0 GB
Offload
30%
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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | F | Too heavy | 11.0 tok/s | 9597 ms | 4K |
| Coding | F | Too heavy | 9.4 tok/s | 20614 ms | 4K |
| Agentic Coding | F | Too heavy | 7.1 tok/s | 39915 ms | 4K |
| Reasoning | F | Too heavy | 9.4 tok/s | 24362 ms | 4K |
| RAG | F | Too heavy | 7.1 tok/s | 49894 ms | 4K |
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX 2080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 7.8 GB | Low | F0 |
Q3_K_S | 3 | 9.8 GB | Low | F0 |
NVFP4 | 4 |
Copy-paste commands to run internlm2 math plus 20b i1 on your machine.
Run
lms load hf-mradermacher--internlm2-math-plus-20b-i1-gguf && lms server startUpgrade options
RTX 5060 Ti 16GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 57%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Adds memory headroom for longer context windows and future model growth.
~$499 MSRP
RTX 4000 Ada 20GBBudget pickMakes 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 24GBBiggest leapMakes 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 2080 Ti 11GB can run internlm2 math plus 20b i1 at Q2_K quantization (Very compromised (needs ~0.9 GB host RAM)). The recommended Q4_K_M requires 16.8 GB which exceeds available memory, but at Q2_K it needs only 12.4 GB. Expected decode speed: 24.3 tok/s.
internlm2 math plus 20b i1 (20B parameters) requires approximately 16.8 GB at Q4_K_M quantization. On RTX 2080 Ti 11GB, it fits at Q2_K using 12.4 GB.
The recommended quantization is Q4_K_M, but on RTX 2080 Ti 11GB the best fitting quantization is Q2_K, which uses 12.4 GB.
On RTX 2080 Ti 11GB, internlm2 math plus 20b i1 achieves approximately 24.3 tokens per second decode speed with a time-to-first-token of 7975ms using Q2_K quantization.
For coding workloads, internlm2 math plus 20b i1 on RTX 2080 Ti 11GB receives a F grade with 9.4 tok/s and 4K context.
On RTX 2080 Ti 11GB, internlm2 math plus 20b i1 can safely use up to 6K tokens of context at Q2_K quantization. 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-mradermacher--internlm2-math-plus-20b-i1-gguf-on-rtx-2080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 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 |
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.