Ministral 8B needs ~8.8 GB VRAM. GTX 1070 Ti 8GB has 8.0 GB. With Q4_K_M quantization, expect ~18 tok/s.
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
0.8 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.4 GB host RAM)
Decode
19.8 tok/s
TTFT
9779 ms
Safe context
10K
Memory
8.8 GB / 8.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs with offload | 33.3 tok/s | 3174 ms | 10K |
| Coding | C | Very compromised | 18.4 tok/s | 10512 ms | 10K |
| Agentic Coding | F | Too heavy | 12.1 tok/s | 23232 ms | 10K |
| Reasoning | C | Very compromised (needs ~0.4 GB host RAM) | 19.8 tok/s | 11557 ms | 10K |
| RAG | F | Too heavy | 12.1 tok/s | 29040 ms | 10K |
How Ministral 8B (8B params) fits at each quantization level on GTX 1070 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | B63 |
Q3_K_S | 3 | 3.9 GB | Low | B63 |
NVFP4 | 4 |
Copy-paste commands to run Ministral 8B on your machine.
Run
ollama run ministralUpgrade options
RTX 3060 12GBBudget pickRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 122%.
~$329 MSRP
RTX 5060 Ti 16GBBest valueRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 209%.
~$449 MSRP
RTX 4060 Ti 16GBNVIDIA upgradeRemoves host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 115%.
~$499 MSRP
Yes, GTX 1070 Ti 8GB can run Ministral 8B with a C grade (Very compromised). Expected decode speed: 18.4 tok/s.
Ministral 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.
The recommended quantization for Ministral 8B is Q4_K_M, which balances quality and memory efficiency.
On GTX 1070 Ti 8GB, Ministral 8B achieves approximately 18.4 tokens per second decode speed with a time-to-first-token of 10512ms using Q4_K_M quantization.
For coding workloads, Ministral 8B on GTX 1070 Ti 8GB receives a C grade with 18.4 tok/s and 10K context.
On GTX 1070 Ti 8GB, Ministral 8B can safely use up to 10K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/ministral-8b-on-gtx-1070-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| B63 |
Q4_K_MBest for your GPU | 4 | 4.9 GB | Medium | B62 |
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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.