DeepSeek-R1-Distill-Qwen-14B on NVIDIA H100 80GB SXM (80GB VRAM)
In 4-bit Q4_K_M, DeepSeek-R1-Distill-Qwen-14B uses only 11.4 GB of your 80 GB VRAM (14% utilization) at ~231.5 tokens/sec. In full 16-bit precision, it requires 32.65 GB.
🎛️ Interactive VRAM & KV-Cache Tuner
📊 Real-Time Memory Footprint
11.4 GB / 80 GB🚀 Ready-to-Run Launch Command
ollama run deepseek-r1:14b # Or with llama.cpp server (8K context, Flash Attention): llama-server -hf deepseek-ai/DeepSeek-R1-Distill-Qwen-14B-GGUF -c 8192 -ngl 99 -fa
Need 11.4 GB+ VRAM to run DeepSeek-R1-Distill-Qwen-14B?
Recommended cloud setup for 4-bit: RTX 4090 (24GB) (or 32.65 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: DeepSeek-R1-Distill-Qwen-14B on NVIDIA H100 80GB SXM
| Quantization Format | Bits/Weight | Weights VRAM | KV Cache (8K) | Total VRAM | Fits on 1x NVIDIA H100 80GB SXM (80GB)? | Est. Speed | GPUs Needed |
|---|---|---|---|---|---|---|---|
| FP16 / BF16 (Unquantized) | 16 bpw | 30.49 GB | 1.5 GB | 32.65 GB | Yes (41%) | ~70.2 tok/s | 1x NVIDIA H100 80GB SXM |
| FP8 (W8A8 / E4M3) | 8.5 bpw | 16.2 GB | 1.5 GB | 18.36 GB | Yes (23%) | ~132.1 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF Q8_0 (8-bit) | 8.5 bpw | 16.2 GB | 1.5 GB | 18.36 GB | Yes (23%) | ~132.1 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF Q6_K (6-bit) | 6.56 bpw | 12.5 GB | 1.5 GB | 14.66 GB | Yes (18%) | ~171.1 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF Q5_K_M (5-bit) | 5.69 bpw | 10.84 GB | 1.5 GB | 13 GB | Yes (16%) | ~197.3 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF Q4_K_M / AWQ (4-bit) | 4.85 bpw | 9.24 GB | 1.5 GB | 11.4 GB | Yes (14%) | ~231.5 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF IQ4_XS (4.25 bpw) | 4.25 bpw | 8.1 GB | 1.5 GB | 10.26 GB | Yes (13%) | ~264.2 tok/s | 1x NVIDIA H100 80GB SXM |
| GGUF Q3_K_M (3-bit) | 3.91 bpw | 7.45 GB | 1.5 GB | 9.61 GB | Yes (12%) | ~280 tok/s | 1x NVIDIA H100 80GB SXM |
Frequently Asked Questions (DeepSeek-R1-Distill-Qwen-14B + NVIDIA H100 80GB SXM)
Can NVIDIA H100 80GB SXM (80GB) run DeepSeek-R1-Distill-Qwen-14B locally?
Yes — NVIDIA H100 80GB SXM (80GB) easily runs DeepSeek-R1-Distill-Qwen-14B even in full FP16. In 4-bit Q4_K_M, DeepSeek-R1-Distill-Qwen-14B uses only 11.4 GB of your 80 GB VRAM (14% utilization) at ~231.5 tokens/sec. In full 16-bit precision, it requires 32.65 GB.
How is KV Cache calculated for DeepSeek-R1-Distill-Qwen-14B?
DeepSeek-R1-Distill-Qwen-14B uses Dense-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.