Vicuna 7B needs ~14.1 GB but GTX 1070 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
6.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
7.5 tok/s
TTFT
25977 ms
Safe context
4K
Memory
14.1 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 14.1 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 | 15.2 tok/s | 6942 ms | 4K |
| Coding | F | Too heavy | 7.5 tok/s | 25977 ms | 4K |
| Agentic Coding | F | Too heavy | 5.3 tok/s | 53073 ms | 4K |
| Reasoning | F | Too heavy | 7.5 tok/s | 30700 ms | 4K |
| RAG | F | Too heavy | 5.3 tok/s | 66342 ms | 4K |
How Vicuna 7B (7B params) fits at each quantization level on GTX 1070 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C54 |
Q3_K_S | 3 | 3.4 GB | Low | C54 |
NVFP4 | 4 | 3.9 GB | Medium | C54 |
Q4_K_M | 4 | 4.3 GB | Medium | C54 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C53 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Opciones de mejora
RTX 5060 Ti 16GBOpción 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.
~$449 MSRP
RTX 4060 Ti 16GBMejor relación calidad-precioHace 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.
~$499 MSRP
RTX 2000 Ada 16GBMejora NVIDIAHace 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.
~$625 MSRP
No, Vicuna 7B requires more memory than GTX 1070 8GB provides.
Vicuna 7B (7B parameters) requires approximately 14.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Vicuna 7B is Q4_K_M, which balances quality and memory efficiency.
On GTX 1070 8GB, Vicuna 7B achieves approximately 7.5 tokens per second decode speed with a time-to-first-token of 25977ms using Q4_K_M quantization.
For coding workloads, Vicuna 7B on GTX 1070 8GB receives a F grade with 7.5 tok/s and 4K context.
On GTX 1070 8GB, Vicuna 7B 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/vicuna-7b-on-gtx-1070-8gb" 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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