internlm2 math plus 20b i1 needs ~16.5 GB but RTX 2070 Super 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
3.4 tok/s
TTFT
57619 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 3.9 tok/s | 27121 ms | 4K |
| Coding | F | Too heavy | 3.4 tok/s | 57619 ms | 4K |
| Agentic Coding | F | Too heavy | 3.4 tok/s | 83810 ms | 4K |
| Reasoning | F | Too heavy | 3.4 tok/s | 68095 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 2070 Super 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 | 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 |
Opciones de mejora
RTX 5060 Ti 16GBMejor relación calidad-precioHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 335%.
~$449 MSRP
RTX 4060 Ti 16GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 221%.
~$499 MSRP
RTX 4000 Ada 20GBOpción económicaHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,250 MSRP
RTX 4090 24GBMayor saltoHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.
~$1,599 MSRP
No, internlm2 math plus 20b i1 requires more memory than RTX 2070 Super 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 2070 Super 8GB, internlm2 math plus 20b i1 achieves approximately 3.4 tokens per second decode speed with a time-to-first-token of 57619ms using Q4_K_M quantization.
For coding workloads, internlm2 math plus 20b i1 on RTX 2070 Super 8GB receives a F grade with 3.4 tok/s and 4K context.
On RTX 2070 Super 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.
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.
Paste this snippet into any page to show a live fit card.
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