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Mistral AIDense

Ministral 3 3B 2512 RAM Calculator

The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.

Standard Recommendation

16GB RAM

Calculated for 4-bit (Q4_K_M) @ 8K Context

1. Select Quantization Level

Quantization compresses model weights to reduce RAM usage, with minor impacts on output quality.

2. Set Target Context Length

Longer contexts require more active memory for the Key-Value (KV) cache.

8,192 tokens
1K tokens32K64K128K tokens

Inference Bandwidth & Speed Matrix

Estimates generation speeds (tokens per second) based on physical memory channel bandwidth constraints.

DDR4 CPU Mode

45 GB/s

26.5 t/s

DDR5 CPU Mode

96 GB/s

56.5 t/s

Mac Unified Memory

300 GB/s

176.5 t/s

GPU VRAM (RTX 4090)

1008 GB/s

592.9 t/s

*Token throughput calculated strictly from weight volume transfers over memory channels. Actual generation speeds can be further throttled by processing threads or VRAM offloading parameters.

Technical Specifications

Total Parameter Count3 Billion
Active Parameters Per TokenDense (All active)
Maximum Context Window131K tokens
Primary Framework SupportOllama, llama.cpp, ExLlamaV2, vLLM

GPU & VRAM Sizing Profile

Budget / Entry GPU
Est. VRAM Required3.7 GB VRAM
Target GPU Hardware1x RTX 4060 Ti (16GB VRAM) or RTX 4070 (12GB VRAM)

Hardware Profile: Excellent for lightweight dense or edge models. Fits completely inside budget GPU VRAM for maximum processing speeds.

Ministral 3 3B 2512 Memory FAQs

How much RAM does Ministral 3 3B 2512 require?

To run Ministral 3 3B 2512 locally, memory size depends on your selected quantization. At 4-bit compression (Q4_K_M), the weights take up ~1.7GB of RAM. When combined with context cache and OS overhead, a standard **16GB system memory kit** is recommended. Unquantized FP16 execution requires a **16GB memory setup**.

What are the hardware requirements to run Ministral 3 3B 2512 at FP16 precision?

Running Ministral 3 3B 2512 at unquantized 16-bit precision requires loading ~6GB of model weights directly into VRAM or system memory. A minimum system memory target of **16GB RAM** is required to run the weights stably and avoid out-of-memory crashes.

Is Ministral 3 3B 2512 dense or Mixture-of-Experts (MoE)?

Ministral 3 3B 2512 is built on a **Dense** architecture. It features a dense parameter layout containing 3 Billion parameters. All weights are active and computed on every single pass during token generation.

How does context window size affect RAM usage for Ministral 3 3B 2512?

Context size directly scales the size of the Key-Value (KV) cache. At a standard 8,192 token context, the KV cache for Ministral 3 3B 2512 uses ~0.01GB. If you scale this to 131.072K tokens, the KV cache can scale past hundreds of GBs, requiring multi-GPU or workstation-class RAM configurations.