Ministral 8B needs ~9.1 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~27 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
1.1 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~0.6 GB host RAM)
Decode
26.6 tok/s
TTFT
7272 ms
Safe context
8K
Memory
9.1 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.
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 0.6 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 | 43.1 tok/s | 2451 ms | 8K |
| Coding | C | Very compromised (needs ~0.6 GB host RAM) | 26.6 tok/s | 7272 ms | 8K |
| Agentic Coding | F | Too heavy | 16.9 tok/s | 16693 ms | 8K |
| Reasoning | C | Very compromised (needs ~0.6 GB host RAM) | 26.6 tok/s | 8594 ms | 8K |
| RAG | F | Too heavy | 16.9 tok/s | 20866 ms |
How Ministral 8B (8B params) fits at each quantization level on RTX 3000 Ada Laptop 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 97%.
~$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 130%.
~$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 74%.
~$499 MSRP
Yes, RTX 3000 Ada Laptop 8GB can run Ministral 8B with a C grade (Very compromised (needs ~0.6 GB host RAM)). Expected decode speed: 26.6 tok/s.
Ministral 8B (8B parameters) requires approximately 9.1 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 RTX 3000 Ada Laptop 8GB, Ministral 8B achieves approximately 26.6 tokens per second decode speed with a time-to-first-token of 7272ms using Q4_K_M quantization.
For coding workloads, Ministral 8B on RTX 3000 Ada Laptop 8GB receives a C grade with 26.6 tok/s and 8K context.
On RTX 3000 Ada Laptop 8GB, Ministral 8B can safely use up to 8K 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-rtx-3000-ada-laptop-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 8K |
| 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.