Ministral 3 14B Instruct VRAM and RAM requirements
December 2025 instruction model. Text-only planning estimate using rounded published Ollama package sizes, including bundled vision weights. Image processing is not estimated.
Run this model with Ollama or LM Studio ↓
New to local AI? Find your GPU memory and understand these settings.
Text-only estimate using rounded published Ollama package sizes including bundled vision weights. Additional image-processing allocations are not modelled. The model supports 262,144 tokens; this calculator currently offers up to 131,072. vLLM estimates for this release are not yet verified.
This Mistral AI model has approximately 13.9 billion parameters. The estimates below use 8,192 context tokens, one conversation, FP16 KV cache and all model weights on the GPU.
| Precision | GPU VRAM target | System RAM target | Weight allowance |
|---|---|---|---|
| Q4_K_M | 11.9 GiB | 24 GiB | 9.10 GB |
| Q8_0 | 18.5 GiB | 24 GiB | 15.00 GB |
| FP16 | 33.3 GiB | 40 GiB | 28.00 GB |
What changes with a longer conversation?
For Ministral 3 14B Instruct, the architecture used here has 40 layers, 8 KV heads and a head dimension of 128. The following comparison keeps Q4_K_M weights fixed and changes only context. It uses the same calculation as the main tool.
| Context | KV cache | GPU target | Mac unified target |
|---|---|---|---|
| 1,024 tokens | 0.16 GiB | 10.7 GiB | 24 GiB |
| 4,096 tokens | 0.63 GiB | 11.2 GiB | 24 GiB |
| 8,192 tokens | 1.25 GiB | 11.9 GiB | 24 GiB |
| 32,768 tokens | 5.00 GiB | 16.1 GiB | 32 GiB |
| 131,072 tokens | 20.00 GiB | 32.8 GiB | 48 GiB |
Start with the shortest context that covers your task. If the GPU target is close to your available capacity, test a short conversation before loading a long document. These values do not measure speed or answer quality. Understand the trade-offs or troubleshoot a loading failure.
How much context can I use?
The reviewed model context limit is 262,144 tokens. The calculator currently offers settings up to 131,072 tokens. Longer prompts and generated responses share that budget. KV cache grows with context length; additional conversations need additional cache memory.
Can I run Ministral 3 14B Instruct in Ollama, LM Studio or vLLM?
Ministral 3 estimates use Ollama GGUF package sizes. Match the actual download in LM Studio. vLLM estimates are not yet verified for these models. Check the runtime version, model format and hardware support before downloading. A memory fit alone does not guarantee compatibility or speed.
What do these estimates include?
Weights use rounded published Ollama package sizes, including bundled vision weights. Image processing allocations are not included. We add FP16 attention cache, at least 1 GiB runtime allowance and 10% GPU headroom. System RAM includes an 8 GiB loading allowance. CPU offload, image processing and multi-GPU setups are not modelled.
Architecture reviewed 17 September 2026. These are planning estimates, not measured benchmarks. Catalogue coverage and limitations.
Official model architecture configuration
Run Ministral 3 14B Instruct locally
These steps use Q4_K_M weights. Compare the Q4_K_M row above with your available memory before downloading. The command does not choose Q8 or 16-bit weights, even if you explored those in the calculator.
View the exact Ollama download, file size and licence. Download tags checked 17 September 2026; publishers can update their files. File size is not the same as total running memory.
Option 1: Ollama
- Install and start Ollama from its official website. Check its supported GPUs.
- Open Terminal on Mac or Linux, or PowerShell on Windows. Run the command below. On first use, Ollama downloads the model before opening a chat.
ollama run ministral-3:14b-instruct-2512-q4_K_MInside the Ollama chat, enter this setting before your first prompt to use the same 8,192-token context as the table above:
/set parameter num_ctx 8192Then try a short prompt such as “Explain how a solar panel works in three sentences.” In a second terminal, run ollama ps to inspect whether the model is on the GPU or partly on the CPU. CPU offload changes performance and falls outside this calculator's full-GPU assumptions. See Ollama's context and GPU-loading instructions.
Option 2: LM Studio
- Install LM Studio and open its model discovery screen.
- Search for Ministral 3 14B Instruct. Match the family, size and variant exactly. Check the publisher and model card; community conversions are not necessarily published by the original model creator.
- Choose a supported GGUF download labelled Q4_K_M to compare with our balanced estimate. Check the actual file size before downloading.
- Load the model into a chat. Set the context length to 8,192 tokens and check the GPU offload settings. Try a short prompt before adding a long conversation or document.
LM Studio's official model download guide. Search results and available formats can change; we have verified the Ollama tags, not every community LM Studio conversion.
If the model will not load
Check your runtime version and GPU support, then close other memory-heavy apps. Try a shorter context or a smaller model and recalculate. Confirm that the downloaded format matches the estimate. Extra conversations, vision processing and runtime-specific allocations can increase memory use.
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