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Mixtral-8x22B-Instruct (MoE) (141B) GPU VRAM & Hardware Calculator

141B sparse MoE model (39B active parameters) offering fast inference when loaded across multi-GPU setups. — Adjust quantization and context length below.

Total Required VRAM
-- GB
--
Model Weights Size
-- GB
Approx. GGUF disk file size
KV Cache Memory
-- GB
Scales with context window
Est. Speed on RTX 4090
-- tok/s
ollama run ...

Hardware Compatibility & Speed Matrix

Hardware Device VRAM / Unified RAM Bandwidth Compatibility Status Est. Speed (tok/s)

Compare Other LLM Models

⚡ DeepSeek-R1 671B (MoE) ⚡ DeepSeek-R1-Distill-Llama-70B ⚡ DeepSeek-R1-Distill-Qwen-32B ⚡ DeepSeek-R1-Distill-Qwen-14B ⚡ DeepSeek-R1-Distill-Llama-8B ⚡ DeepSeek-R1-Distill-Qwen-7B ⚡ DeepSeek-R1-Distill-Qwen-1.5B ⚡ DeepSeek-V3 671B (MoE) ⚡ Qwen-2.5-72B-Instruct ⚡ Qwen-2.5-32B-Instruct ⚡ Qwen-2.5-Coder-32B-Instruct ⚡ QwQ-32B (Reasoning) ⚡ Qwen-2.5-14B-Instruct ⚡ Qwen-2.5-7B-Instruct ⚡ Llama-3.3-70B-Instruct ⚡ Llama-3.1-405B-Instruct ⚡ Llama-3.1-8B-Instruct ⚡ Llama-3.2-11B-Vision-Instruct ⚡ Llama-3.2-3B-Instruct ⚡ Gemma-2-27B-IT ⚡ Gemma-2-9B-IT ⚡ Mistral-Large-2-123B ⚡ Mixtral-8x22B-Instruct (MoE) ⚡ Mixtral-8x7B-Instruct (MoE) ⚡ Mistral-Nemo-12B-Instruct ⚡ Microsoft Phi-4 14B ⚡ Cohere Command R+ 104B ⚡ Yi-1.5-34B-Chat ⚡ GLM-4-9B-Chat ⚡ InternLM2.5-20B-Chat ⚡ StarCoder2-15B ⚡ Mistral Codestral-22B ⚡ FLUX.1-dev 12B (Image Diffusion) ⚡ Stable Diffusion 3.5 Large (8B) ⚡ Wan2.1-T2V-14B (Video Generation) ⚡ Gemma-3-27B-IT (Multimodal)

📖 How to Use & Technical Guide

  1. Select a preset open-weight model (e.g., DeepSeek-R1-Distill-Qwen-32B, Llama-3.3-70B) or enter custom parameter count (Billions).
  2. Choose your quantization format (FP16, INT8/Q8_0, GGUF Q6_K, Q5_K_M, Q4_K_M, or Q3_K_M) and context window length (2K to 128K tokens).
  3. Review the total VRAM breakdown (Model Weights + KV Cache + CUDA/Runtime Overhead) and check which GPUs or Apple Silicon Macs can run it smoothly.

❓ Frequently Asked Questions

How much GPU VRAM is needed to run Mixtral-8x22B-Instruct (MoE)?
In 4-bit quantization (GGUF Q4_K_M / AWQ), Mixtral-8x22B-Instruct (MoE) (141B parameters) requires approximately 85.5 GB for model weights plus 1.5–3.0 GB for KV cache and runtime overhead. In native FP16/BF16, it requires ~282.0 GB VRAM.
How do I run Mixtral-8x22B-Instruct (MoE) locally with Ollama?
Install Ollama and run `ollama run mixtral:8x22b` in your terminal. Ollama automatically downloads the default 4-bit GGUF quantization.
How is LLM VRAM usage calculated?
Total LLM VRAM = Model Weights + KV Cache + Activation/CUDA Buffer (~0.6-1.2 GB). Model weights depend on parameter count and bits-per-weight (e.g., Q4_K_M uses ~4.85 bits per parameter, so a 32B model needs ~19.4 GB for weights alone).
Which quantization format offers the best quality-to-VRAM ratio?
GGUF Q4_K_M and Q5_K_M offer the best balance for consumer GPUs, retaining over 98-99% of FP16 perplexity while reducing VRAM consumption by nearly 70%.
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