DeepSeek-R1-Distill-Llama-70B on NVIDIA RTX 3090 (24GB VRAM)
Even in 4-bit Q4_K_M, DeepSeek-R1-Distill-Llama-70B requires 47.25 GB VRAM (2x NVIDIA RTX 3090 GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~1.7 tok/s).
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
47.25 GB / 24 GB🚀 Ready-to-Run Launch Command
ollama run deepseek-r1:70b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf deepseek-ai/DeepSeek-R1-Distill-Llama-70B-GGUF -c 8192 -ngl 99 -fa
Need 47.25 GB+ VRAM to run DeepSeek-R1-Distill-Llama-70B?
Recommended cloud setup for 4-bit: L40S / RTX 6000 Ada (48GB) (or 148.6 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: DeepSeek-R1-Distill-Llama-70B on NVIDIA RTX 3090
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA RTX 3090 (24GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 145.44 GB | 2.5 GB | 148.6 GB | No (Over by 124.6GB) | ~1.2 tok/s | 7x NVIDIA RTX 3090 |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 77.26 GB | 2.5 GB | 80.43 GB | No (Over by 56.4GB) | ~1.2 tok/s | 4x NVIDIA RTX 3090 |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 77.26 GB | 2.5 GB | 80.43 GB | No (Over by 56.4GB) | ~1.2 tok/s | 4x NVIDIA RTX 3090 |
| GGUF Q6_K (6-bit) | 6.56 bpw | 59.63 GB | 2.5 GB | 62.79 GB | No (Over by 38.8GB) | ~1.3 tok/s | 3x NVIDIA RTX 3090 |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 51.72 GB | 2.5 GB | 54.89 GB | No (Over by 30.9GB) | ~1.5 tok/s | 3x NVIDIA RTX 3090 |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 44.09 GB | 2.5 GB | 47.25 GB | No (Over by 23.3GB) | ~1.7 tok/s | 2x NVIDIA RTX 3090 |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 38.63 GB | 2.5 GB | 41.8 GB | No (Over by 17.8GB) | ~2 tok/s | 2x NVIDIA RTX 3090 |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 35.54 GB | 2.5 GB | 38.71 GB | No (Over by 14.7GB) | ~2.2 tok/s | 2x NVIDIA RTX 3090 |
Frequently Asked Questions (DeepSeek-R1-Distill-Llama-70B + NVIDIA RTX 3090)
Can NVIDIA RTX 3090 (24GB) run DeepSeek-R1-Distill-Llama-70B locally?
No — A single NVIDIA RTX 3090 (24GB) cannot fit DeepSeek-R1-Distill-Llama-70B in VRAM. Even in 4-bit Q4_K_M, DeepSeek-R1-Distill-Llama-70B requires 47.25 GB VRAM (2x NVIDIA RTX 3090 GPUs needed for full VRAM execution, or heavy CPU RAM offloading at ~1.7 tok/s).
How is KV Cache calculated for DeepSeek-R1-Distill-Llama-70B?
DeepSeek-R1-Distill-Llama-70B 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.