✅ FITS IN Q5_K_M QUANTIZATION
Llama-4-Maverick-17B-128E (400B MoE) on 4x NVIDIA A100 80GB (320GB) (320GB VRAM)
Full FP16 (826.16 GB) exceeds 320 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 251.93 GB (249.77 GB weights + 1.5 GB KV cache at 8K context), delivering ~122.7 tokens/sec.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
251.93 GB / 320 GB
🟦 Weights (249.77 GB)
🟪 KV Cache (1.5 GB)
🟨 Activation Buffer (0.66 GB)
Total Required VRAM251.93 GB
Available Hardware VRAM320 GB (4x NVIDIA A100 80GB (320GB))
VRAM Headroom / Overflow68.07 GB
Estimated Generation Speed~122.7 tokens/sec
Architecture & AttentionMoE-GQA (48L, 8 KV heads)
🚀 Ready-to-Run Launch Command
ollama run llama4:maverick # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf meta-llama/Llama-4-Maverick-17B-128E-Instruct-GGUF -c 8192 -ngl 99 -fa
CLOUD GPU ALTERNATIVE
Need 251.93 GB+ VRAM to run Llama-4-Maverick-17B-128E (400B MoE)?
Recommended cloud setup for 4-bit: Multi-GPU H100 / H200 Node (or 826.16 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 → |
| Vast.ai | Cheapest Spot GPU Market | $0.29 / hr | $0.95 / hr | $1.79 / hr | Save 50–70% on RTX 4090 / 5090 / A100 rentals | Check Spot GPUs → |
| Vultr Cloud GPU | Enterprise SLA & Bare Metal | — | $1.79 / hr | $2.69 / hr | $300 Free Credit for New Users | Claim $300 Credit → |
| DigitalOcean GPU Droplets | Managed H100 Droplets | — | — | $2.99 / hr | $200 Free Trial (60 Days) | Get $200 Free → |
All Quantization Formats: Llama-4-Maverick-17B-128E (400B MoE) on 4x NVIDIA A100 80GB (320GB)
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x 4x NVIDIA A100 80GB (320GB) (320GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 824 GB | 1.5 GB | 826.16 GB | No (Over by 506.2GB) | ~4.8 tok/s | 3x 4x NVIDIA A100 80GB (320GB) |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 437.75 GB | 1.5 GB | 439.91 GB | No (Over by 119.9GB) | ~9 tok/s | 2x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 437.75 GB | 1.5 GB | 439.91 GB | No (Over by 119.9GB) | ~9 tok/s | 2x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q6_K (6-bit) | 6.56 bpw | 337.84 GB | 1.5 GB | 340 GB | No (Over by 20.0GB) | ~11.7 tok/s | 2x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 293.04 GB | 1.5 GB | 295.19 GB | Tight (92%) | ~104.6 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 249.77 GB | 1.5 GB | 251.93 GB | Yes (79%) | ~122.7 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 218.88 GB | 1.5 GB | 221.03 GB | Yes (69%) | ~140 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 201.37 GB | 1.5 GB | 203.52 GB | Yes (64%) | ~152.2 tok/s | 1x 4x NVIDIA A100 80GB (320GB) |
Frequently Asked Questions (Llama-4-Maverick-17B-128E (400B MoE) + 4x NVIDIA A100 80GB (320GB))
Can 4x NVIDIA A100 80GB (320GB) (320GB) run Llama-4-Maverick-17B-128E (400B MoE) locally?
Yes — 4x NVIDIA A100 80GB (320GB) (320GB) can run Llama-4-Maverick-17B-128E (400B MoE) using GGUF Q5_K_M (5-bit). Full FP16 (826.16 GB) exceeds 320 GB VRAM, but 4-bit GGUF Q4_K_M / AWQ requires 251.93 GB (249.77 GB weights + 1.5 GB KV cache at 8K context), delivering ~122.7 tokens/sec.
How is KV Cache calculated for Llama-4-Maverick-17B-128E (400B MoE)?
Llama-4-Maverick-17B-128E (400B MoE) uses MoE-GQA with 48 transformer layers, 40 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 1.5 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 0.75 GB.
Compare Llama-4-Maverick-17B-128E (400B MoE) on Other GPUs
Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 5090 (32GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4090 (24GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 5080 (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4080 Super (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4070 Ti Super (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 3090 (24GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 4060 Ti 16GB (16GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 3060 12GB (12GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX 6000 Ada (48GB)Llama-4-Maverick-17B-128E (400B MoE) on NVIDIA RTX A6000 (48GB)
Other Models on 4x NVIDIA A100 80GB (320GB) (320GB)
DeepSeek-R1 671B (MoE) on 4x NVIDIA A100 80GB (320GB)DeepSeek-V3 671B (MoE) on 4x NVIDIA A100 80GB (320GB)DeepSeek-R1-Distill-Llama-70B on 4x NVIDIA A100 80GB (320GB)DeepSeek-R1-Distill-Qwen-32B on 4x NVIDIA A100 80GB (320GB)DeepSeek-R1-Distill-Qwen-14B on 4x NVIDIA A100 80GB (320GB)DeepSeek-R1-Distill-Llama-8B on 4x NVIDIA A100 80GB (320GB)DeepSeek-R1-Distill-Qwen-7B on 4x NVIDIA A100 80GB (320GB)Qwen3-235B-A22B (MoE) on 4x NVIDIA A100 80GB (320GB)Qwen3-30B-A3B (MoE) on 4x NVIDIA A100 80GB (320GB)Qwen3-32B on 4x NVIDIA A100 80GB (320GB)