OLMo 2 32B needs ~25.8 GB but RTX 4070 Super 12GB only has 12.0 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
13.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.2 tok/s
TTFT
60110 ms
Safe context
4K
Memory
25.8 GB / 12.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 25.8 GB, but this setup only exposes 12.0 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 | 3.5 tok/s | 30129 ms | 4K |
| Coding | F | Too heavy | 3.0 tok/s | 64919 ms | 4K |
| Agentic Coding | F | Too heavy | 3.0 tok/s | 94428 ms | 4K |
| Reasoning | F | Too heavy | 3.0 tok/s | 76723 ms | 4K |
| RAG | F | Too heavy | 3.0 tok/s | 118035 ms | 4K |
How OLMo 2 32B (32B params) fits at each quantization level on RTX 4070 Super 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.5 GB | Low | F0 |
Q3_K_S | 3 | 15.7 GB | Low | F0 |
NVFP4 | 4 | 17.9 GB | Medium | F0 |
Q4_K_M | 4 | 19.5 GB | Medium | F0 |
Q5_K_M | 5 | 23.0 GB | High | F0 |
Q6_K | 6 | 26.2 GB | High | F0 |
Q8_0 | 8 | 34.2 GB | Very High | F0 |
F16 | 16 | 65.6 GB | Maximum | F0 |
Opciones de mejora
RTX 3090 24GBMejor 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 563%.
~$1,499 MSRP
RTX 4090 24GBMejora NVIDIAHace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.
Sube la velocidad estimada de decodificación alrededor de un 675%.
~$1,599 MSRP
RTX 5090 32GBOpción 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.
~$1,999 MSRP
No, OLMo 2 32B requires more memory than RTX 4070 Super 12GB provides.
OLMo 2 32B (32B parameters) requires approximately 25.8 GB of memory with Q4_K_M quantization.
The recommended quantization for OLMo 2 32B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4070 Super 12GB, OLMo 2 32B achieves approximately 3.0 tokens per second decode speed with a time-to-first-token of 64919ms using Q4_K_M quantization.
For coding workloads, OLMo 2 32B on RTX 4070 Super 12GB receives a F grade with 3.0 tok/s and 4K context.
On RTX 4070 Super 12GB, OLMo 2 32B can safely use up to 4K tokens of context. The model's official context limit is 4K, 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.
<iframe src="https://willitrunai.com/embed/olmo-2-32b-on-rtx-4070-super-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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