Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 4080 Super (16GB VRAM)
Even in 4-bit Q4_K_M, Mixtral-8x22B-Instruct (MoE) requires 90.46 GB VRAM (6x NVIDIA RTX 4080 Super GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~2.5 tok/s).
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
90.46 GB / 16 GB🚀 Ready-to-Run Launch Command
ollama run mixtral:8x22b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf mistralai/Mixtral-8x22B-Instruct-v0.1-GGUF -c 8192 -ngl 99 -fa
Need 90.46 GB+ VRAM to run Mixtral-8x22B-Instruct (MoE)?
Recommended cloud setup for 4-bit: Multi-GPU H100 / H200 Node (or 292.87 GB for full FP16). Spin up a pre-configured vLLM / Ollama instance in 60 seconds:
| Provider | Best For | RTX 4090 (24GB) | A100 (80GB) | H100 (80GB) | Bonus / Trial | Action |
|---|---|---|---|---|---|---|
| RunPod | 1-Click vLLM / Ollama Pods | $0.44 / hr | $1.39 / hr | $2.39 / hr | Instant PyTorch / vLLM Pods + Serverless GPU | Deploy on RunPod → |
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All Quantization Formats: Mixtral-8x22B-Instruct (MoE) on NVIDIA RTX 4080 Super
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA RTX 4080 Super (16GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 290.46 GB | 1.75 GB | 292.87 GB | No (Over by 276.9GB) | ~1.2 tok/s | 19x NVIDIA RTX 4080 Super |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 154.31 GB | 1.75 GB | 156.72 GB | No (Over by 140.7GB) | ~1.4 tok/s | 10x NVIDIA RTX 4080 Super |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 154.31 GB | 1.75 GB | 156.72 GB | No (Over by 140.7GB) | ~1.4 tok/s | 10x NVIDIA RTX 4080 Super |
| GGUF Q6_K (6-bit) | 6.56 bpw | 119.09 GB | 1.75 GB | 121.5 GB | No (Over by 105.5GB) | ~1.8 tok/s | 8x NVIDIA RTX 4080 Super |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 103.29 GB | 1.75 GB | 105.71 GB | No (Over by 89.7GB) | ~2.1 tok/s | 7x NVIDIA RTX 4080 Super |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 88.05 GB | 1.75 GB | 90.46 GB | No (Over by 74.5GB) | ~2.5 tok/s | 6x NVIDIA RTX 4080 Super |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 77.15 GB | 1.75 GB | 79.56 GB | No (Over by 63.6GB) | ~2.8 tok/s | 5x NVIDIA RTX 4080 Super |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 70.98 GB | 1.75 GB | 73.39 GB | No (Over by 57.4GB) | ~3.1 tok/s | 5x NVIDIA RTX 4080 Super |
Frequently Asked Questions (Mixtral-8x22B-Instruct (MoE) + NVIDIA RTX 4080 Super)
Can NVIDIA RTX 4080 Super (16GB) run Mixtral-8x22B-Instruct (MoE) locally?
No — A single NVIDIA RTX 4080 Super (16GB) cannot fit Mixtral-8x22B-Instruct (MoE) in VRAM. Even in 4-bit Q4_K_M, Mixtral-8x22B-Instruct (MoE) requires 90.46 GB VRAM (6x NVIDIA RTX 4080 Super GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~2.5 tok/s).
How is KV Cache calculated for Mixtral-8x22B-Instruct (MoE)?
Mixtral-8x22B-Instruct (MoE) uses MoE-GQA with 56 transformer layers, 48 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 1.75 GB. Enabling 8-bit KV cache (-ctk q8_0 -ctv q8_0 in llama.cpp or --kv-cache-dtype fp8 in vLLM) cuts this in half to 0.88 GB.