internlm2 math plus 20b i1 needs ~16.5 GB but RTX 5060 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
8.5 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.9 tok/s
TTFT
49759 ms
Safe context
4K
Memory
16.5 GB / 8.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 16.5 GB, but this setup only exposes 8.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 4.5 tok/s | 23364 ms | 4K |
| Coding | F | Too heavy | 3.9 tok/s | 49759 ms | 4K |
| Agentic Coding | F | Too heavy | 3.4 tok/s | 83810 ms | 4K |
| Reasoning | F | Too heavy | 3.9 tok/s | 58806 ms | 4K |
| RAG | F | Too heavy | 3.4 tok/s | 104762 ms | 4K |
How internlm2 math plus 20b i1 (20B params) fits at each quantization level on RTX 5060 8GB (8.0 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 |
Upgrade 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 279%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 179%.
~$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
No, internlm2 math plus 20b i1 requires more memory than RTX 5060 8GB provides.
internlm2 math plus 20b i1 (20B parameters) requires approximately 16.5 GB of memory with Q4_K_M quantization.
The recommended quantization for internlm2 math plus 20b i1 is Q4_K_M, which balances quality and memory efficiency.
On RTX 5060 8GB, internlm2 math plus 20b i1 achieves approximately 3.9 tokens per second decode speed with a time-to-first-token of 49759ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 20b i1 on RTX 5060 8GB receives a F grade with 3.9 tok/s and 4K context.
On RTX 5060 8GB, internlm2 math plus 20b i1 can safely use up to 4K 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-mradermacher--internlm2-math-plus-20b-i1-gguf-on-rtx-5060-8gb" 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 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
