Hace 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.
~$329 MSRP
Ministral 3 8B needs ~10.3 GB but RTX 4060 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
2.3 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
15.5 tok/s
TTFT
12499 ms
Safe context
4K
Memory
10.3 GB / 8.0 GB
Offload
20%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 10.3 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 | 19.7 tok/s | 5374 ms | 4K |
| Coding | F | Too heavy | 14.4 tok/s | 13436 ms | 4K |
| Agentic Coding | F | Too heavy | 10.3 tok/s | 27336 ms | 4K |
| Reasoning | F | Too heavy | 15.5 tok/s | 14771 ms | 4K |
| RAG | F | Too heavy | 10.3 tok/s | 34169 ms | 4K |
How Ministral 3 8B (8B params) fits at each quantization level on RTX 4060 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A84 |
Q3_K_S | 3 | 3.9 GB | Low | A84 |
NVFP4 | 4 | 4.5 GB | Medium | A84 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | A83 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 GB | Maximum | F0 |
Opciones de mejora
Hace 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.
~$329 MSRP
Hace 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
Hace 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
No, Ministral 3 8B requires more memory than RTX 4060 8GB provides.
Ministral 3 8B (8B parameters) requires approximately 10.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 3 8B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4060 8GB, Ministral 3 8B achieves approximately 14.4 tokens per second decode speed with a time-to-first-token of 13436ms using Q4_K_M quantization.
For coding workloads, Ministral 3 8B on RTX 4060 8GB receives a F grade with 14.4 tok/s and 4K context.
On RTX 4060 8GB, Ministral 3 8B can safely use up to 4K tokens of context. The model's official context limit is 262K, 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/ministral-3-8b-on-rtx-4060-8gb" 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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