Smaller alternatives
Models that need less memory than Ministral 3 14B and score about as well or better.
Ministral 3 14B
Vision
Mistral AI · 14B · Dense
Ministral 3 14B is a 14B model from Mistral AI with a 256K-token context. At Q4_K_M with an 8K context it needs about 9.2 GB; 13 GB leaves room for longer chats.
Quantization options
| Quant | Bits | Memory | Quality | On your hardware |
|---|---|---|---|---|
| Q2_K | 3.16 | — | Noticeably worse | … |
| Q3_K_M | 4 | — | Some loss | … |
| Q4_K_M | 4.89 | — | Good | … |
| Q5_K_M | 5.7 | — | Good | … |
| Q6_K | 6.56 | — | Near original | … |
| Q8_0 | 8.5 | — | Near original | … |
| F16 | 16 | — | Original | … |
Memory by context length
At Q4_K_M. The context cache grows with every token the model keeps in mind.
| Context | Context cache | Total |
|---|---|---|
| 4K | 0.6 GB | 8.6 GB |
| 8K | 1.3 GB | 9.2 GB |
| 32K | 5 GB | 13 GB |
| 128K | 20 GB | 28 GB |
| 256K | 40 GB | 48 GB |
Next steps
Compare it with
Scored models of a similar size, side by side on your device.
Least hardware that runs it well
Desktop devices with the least memory that give Ministral 3 14B grade A or S at Q4_K_M.
Can I run Ministral 3 14B locally?
Can I run Ministral 3 14B locally?
Yes, if your GPU or Mac has about 9.2 GB free for it at Q4_K_M. Open this page on that computer to see the grade for your exact hardware, then start it with llama.cpp, Ollama or LM Studio.
How much memory does Ministral 3 14B need?
About 9.2 GB at Q4_K_M with an 8K context and 14.9 GB at Q8_0. Lower quantizations fit smaller cards with some loss in quality; longer contexts add to the total.