Yi 1.5 34B needs ~26.4 GB but RTX 3070 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
18.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.5 tok/s
TTFT
78620 ms
Safe context
4K
Memory
26.4 GB / 8.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 26.4 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 | 2.5 tok/s | 42883 ms | 4K |
| Coding | F | Too heavy | 2.5 tok/s | 78620 ms | 4K |
| Agentic Coding | F | Too heavy | 2.5 tok/s | 114356 ms | 4K |
| Reasoning | F | Too heavy | 2.5 tok/s | 92914 ms | 4K |
| RAG | F | Too heavy | 2.5 tok/s | 142945 ms | 4K |
How Yi 1.5 34B (34B params) fits at each quantization level on RTX 3070 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 13.3 GB | Low | F0 |
Q3_K_S | 3 | 16.7 GB | Low | F0 |
NVFP4 | 4 | 19.0 GB | Medium | F0 |
Q4_K_M | 4 | 20.7 GB | Medium | F0 |
Q5_K_M | 5 | 24.5 GB | High | F0 |
Q6_K | 6 | 27.9 GB | High | F0 |
Q8_0 | 8 | 36.4 GB | Very High | F0 |
F16 | 16 | 69.7 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 644%.
~$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 768%.
~$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, Yi 1.5 34B requires more memory than RTX 3070 8GB provides.
Yi 1.5 34B (34B parameters) requires approximately 26.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 34B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3070 8GB, Yi 1.5 34B achieves approximately 2.5 tokens per second decode speed with a time-to-first-token of 78620ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 34B on RTX 3070 8GB receives a F grade with 2.5 tok/s and 4K context.
On RTX 3070 8GB, Yi 1.5 34B 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/yi-1.5-34b-on-rtx-3070-8gb" 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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