Can I run Ministral 3 14B?
Ministral 3 14B by Mistral AI needs around 16 GB of RAM at the recommended 4-bit quantization (8.5 GB download). Your hardware is checked below — instantly, nothing leaves your browser. Expect roughly ~36 tok/s on a NVIDIA RTX 3060 12GB.
Reading your hardware signals…
Specifications
Size by quantization
| Quantization | Bits/weight | Download | Min RAM | Quality |
|---|---|---|---|---|
| Q2_K | 3.35 | 5.9 GB | 12 GB | Noticeable loss |
| Q4_K_MRecommended | 4.85 | 8.5 GB | 16 GB | Recommended |
| Q5_K_M | 5.65 | 9.9 GB | 16 GB | High |
| Q8_0 | 8.5 | 14.9 GB | 24 GB | Near-original |
| F16 | 16 | 28.0 GB | 48 GB | Original |
Sizes are estimates from parameter count × bits per weight; real GGUF builds vary slightly. · Data updated: 2026-06-11 · How we calculate these numbers →
Memory needed by context length
| Context | KV cache (est.) | Total memory (Q4) |
|---|---|---|
| 4K tokens | ~0.7 GB | ~9.2 GB |
| 8K tokens | ~1.3 GB | ~9.8 GB |
| 32K tokens | ~5.4 GB | ~13.9 GB |
| 128K tokens | ~21.6 GB | ~30.1 GB |
The KV cache grows with context length — a model that fits at 4K can run out of memory at 32K. Estimates assume an FP16 cache with grouped-query attention; actual usage varies by runtime.
Estimated speed by hardware
| Hardware | Bandwidth | ~Speed |
|---|---|---|
| NVIDIA RTX 3060 12GB | 360 GB/s | ~36 tok/s |
| NVIDIA RTX 4090 24GB | 1008 GB/s | ~101 tok/s |
| Apple M-series (base) | 100 GB/s | ~10 tok/s |
| Apple M-series Pro | 270 GB/s | ~27 tok/s |
| Apple M-series Max | 410 GB/s | ~41 tok/s |
| CPU only (dual-channel DDR5) | 60 GB/s | ~6 tok/s |
Token generation is memory-bandwidth bound: tok/s ≈ bandwidth × 0.85 ÷ model size at Q4. Real-world numbers vary by runtime and context length.
Run it locally
The easiest path is Ollama — one command and you're chatting:
ollama run ministral-3:14b