✅ RUNS NATIVE FP16 & ALL QUANTIZATIONS
Qwen2.5-72B-Instruct on 8x NVIDIA H100 80GB (640GB) (640GB VRAM)
In 4-bit Q4_K_M, Qwen2.5-72B-Instruct uses only 48.56 GB of your 640 GB VRAM (8% utilization) at ~47.1 tokens/sec. In full 16-bit precision, it requires 152.93 GB.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
48.56 GB / 640 GB
🟦 Weights (45.4 GB)
🟪 KV Cache (2.5 GB)
🟨 Activation Buffer (0.67 GB)
Total Required VRAM48.56 GB
Available Hardware VRAM640 GB (8x NVIDIA H100 80GB (640GB))
VRAM Headroom / Overflow591.44 GB
Estimated Generation Speed~47.1 tokens/sec
Architecture & AttentionDense-GQA (80L, 8 KV heads)
🚀 Ready-to-Run Launch Command
ollama run qwen2.5:72b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf Qwen/Qwen2.5-72B-Instruct-GGUF -c 8192 -ngl 99 -fa
CLOUD GPU ALTERNATIVE
Need 48.56 GB+ VRAM to run Qwen2.5-72B-Instruct?
Recommended cloud setup for 4-bit: NVIDIA A100 / H100 (80GB) (or 152.93 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: Qwen2.5-72B-Instruct on 8x NVIDIA H100 80GB (640GB)
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x 8x NVIDIA H100 80GB (640GB) (640GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 149.76 GB | 2.5 GB | 152.93 GB | Yes (24%) | ~14.3 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 79.56 GB | 2.5 GB | 82.73 GB | Yes (13%) | ~26.9 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 79.56 GB | 2.5 GB | 82.73 GB | Yes (13%) | ~26.9 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF Q6_K (6-bit) | 6.56 bpw | 61.4 GB | 2.5 GB | 64.57 GB | Yes (10%) | ~34.8 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 53.26 GB | 2.5 GB | 56.42 GB | Yes (9%) | ~40.2 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 45.4 GB | 2.5 GB | 48.56 GB | Yes (8%) | ~47.1 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 39.78 GB | 2.5 GB | 42.95 GB | Yes (7%) | ~53.8 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 36.6 GB | 2.5 GB | 39.76 GB | Yes (6%) | ~58.5 tok/s | 1x 8x NVIDIA H100 80GB (640GB) |
Frequently Asked Questions (Qwen2.5-72B-Instruct + 8x NVIDIA H100 80GB (640GB))
Can 8x NVIDIA H100 80GB (640GB) (640GB) run Qwen2.5-72B-Instruct locally?
Yes — 8x NVIDIA H100 80GB (640GB) (640GB) easily runs Qwen2.5-72B-Instruct even in full FP16. In 4-bit Q4_K_M, Qwen2.5-72B-Instruct uses only 48.56 GB of your 640 GB VRAM (8% utilization) at ~47.1 tokens/sec. In full 16-bit precision, it requires 152.93 GB.
How is KV Cache calculated for Qwen2.5-72B-Instruct?
Qwen2.5-72B-Instruct uses Dense-GQA with 80 transformer layers, 64 attention heads, and 8 KV heads (head dimension 128). At 8,192 tokens in FP16, KV cache consumes 2.5 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 1.25 GB.
Compare Qwen2.5-72B-Instruct on Other GPUs
Qwen2.5-72B-Instruct on NVIDIA RTX 5090 (32GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4090 (24GB)Qwen2.5-72B-Instruct on NVIDIA RTX 5080 (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4080 Super (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4070 Ti Super (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 3090 (24GB)Qwen2.5-72B-Instruct on NVIDIA RTX 4060 Ti 16GB (16GB)Qwen2.5-72B-Instruct on NVIDIA RTX 3060 12GB (12GB)Qwen2.5-72B-Instruct on NVIDIA RTX 6000 Ada (48GB)Qwen2.5-72B-Instruct on NVIDIA RTX A6000 (48GB)
Other Models on 8x NVIDIA H100 80GB (640GB) (640GB)
DeepSeek-R1 671B (MoE) on 8x NVIDIA H100 80GB (640GB)DeepSeek-V3 671B (MoE) on 8x NVIDIA H100 80GB (640GB)DeepSeek-R1-Distill-Llama-70B on 8x NVIDIA H100 80GB (640GB)DeepSeek-R1-Distill-Qwen-32B on 8x NVIDIA H100 80GB (640GB)DeepSeek-R1-Distill-Qwen-14B on 8x NVIDIA H100 80GB (640GB)DeepSeek-R1-Distill-Llama-8B on 8x NVIDIA H100 80GB (640GB)DeepSeek-R1-Distill-Qwen-7B on 8x NVIDIA H100 80GB (640GB)Qwen3-235B-A22B (MoE) on 8x NVIDIA H100 80GB (640GB)Qwen3-30B-A3B (MoE) on 8x NVIDIA H100 80GB (640GB)Qwen3-32B on 8x NVIDIA H100 80GB (640GB)