Minimum VRAM
30GB
FP16 (full model) • Q4 option ≈ 8GB
Best Performance
AMD Instinct MI300X
~218 tok/s • FP16
Most Affordable
RTX 5090
FP16 • ~85 tok/s • From $1,999
Quick answer: Openpipe Qwen3 14B Instruct needs roughly 8GB VRAM for Q4_K_M and 12GB for Q5_K_M. Use Q8 (15GB) or FP16 (30GB) for higher quality output.
Full-model (FP16) requirements are shown by default. Quantized builds like Q4 trade accuracy for lower VRAM usage.
Ready to buy?
See our tested GPU picks for running Openpipe Qwen3 14B Instruct locally.
Best GPU for Running LLMs →Filter by quantization, price, and VRAM to compare performance estimates.
Showing FP16 compatibility. Switch tabs to explore other quantizations.
| GPU | Speed | VRAM Requirement | Typical price |
|---|---|---|---|
AMD Instinct MI300XEstimated AMD | ~218 tok/s FP16 | 30GB VRAM used192GB total on card | $15,000View GPU → |
NVIDIA H200 SXM 141GBEstimated NVIDIA | ~196 tok/s FP16 | 30GB VRAM used141GB total on card | $35,000View GPU → |
NVIDIA H100 SXM5 80GBEstimated NVIDIA | ~141 tok/s FP16 | 30GB VRAM used80GB total on card | $30,000View GPU → |
AMD Instinct MI250XEstimated AMD | ~136 tok/s FP16 | 30GB VRAM used128GB total on card | $11,000View GPU → |
NVIDIA H100 PCIe 80GBEstimated NVIDIA | ~90 tok/s FP16 | 30GB VRAM used80GB total on card | $25,000View GPU → |
RTX 5090Estimated NVIDIA | ~85 tok/s FP16 | 30GB VRAM used32GB total on card | $1,999View GPU → |
NVIDIA A100 80GB SXM4Estimated NVIDIA | ~83 tok/s FP16 | 30GB VRAM used80GB total on card | $11,000View GPU → |
AMD Instinct MI210Estimated AMD | ~68 tok/s FP16 | 30GB VRAM used64GB total on card | $6,000View GPU → |
NVIDIA A100 40GB PCIeEstimated NVIDIA | ~65 tok/s FP16 | 30GB VRAM used40GB total on card | $9,000View GPU → |
RTX 4090Estimated NVIDIA | ~51 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used24GB total on card | $1,599View GPU → |
NVIDIA RTX 6000 AdaEstimated NVIDIA | ~51 tok/s FP16 | 30GB VRAM used48GB total on card | $6,999View GPU → |
NVIDIA L40Estimated NVIDIA | ~47 tok/s FP16 | 30GB VRAM used48GB total on card | $7,999View GPU → |
NVIDIA L40SEstimated NVIDIA | ~47 tok/s FP16 | 30GB VRAM used48GB total on card | $10,000View GPU → |
RTX 5080Tight VRAM NVIDIA | ~45 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $1,199View GPU → |
RTX 3090Estimated NVIDIA | ~44 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used24GB total on card | $1,499View GPU → |
RX 7900 XTXEstimated AMD | ~41 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used24GB total on card | $999View GPU → |
AMD Radeon Pro W7900Estimated AMD | ~41 tok/s FP16 | 30GB VRAM used48GB total on card | $3,999View GPU → |
RTX 5070 TiTight VRAM NVIDIA | ~41 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $799View GPU → |
NVIDIA A6000Estimated NVIDIA | ~38 tok/s FP16 | 30GB VRAM used48GB total on card | $4,699View GPU → |
RTX 4080 SuperTight VRAM NVIDIA | ~36 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $999View GPU → |
RTX 3080Estimated NVIDIA | ~36 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used10GB total on card | $699View GPU → |
NVIDIA A5000Estimated NVIDIA | ~36 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used24GB total on card | $2,399View GPU → |
RTX 4080Tight VRAM NVIDIA | ~35 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $1,199View GPU → |
RX 7900 XTEstimated AMD | ~35 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used20GB total on card | $899View GPU → |
RTX 4070 Ti SuperTight VRAM NVIDIA | ~32 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $799View GPU → |
RTX 5070Estimated NVIDIA | ~31 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used12GB total on card | $599View GPU → |
Apple M2 UltraEstimated Apple | ~31 tok/s FP16 | 30GB VRAM used192GB total on card | $5,999View GPU → |
RX 9070 XTTight VRAM AMD | ~28 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $599View GPU → |
RX 7800 XTTight VRAM AMD | ~27 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $499View GPU → |
RX 7900 GRETight VRAM AMD | ~26 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $649View GPU → |
AMD Radeon Pro W7800Estimated AMD | ~25 tok/s FP16 | 30GB VRAM used32GB total on card | $2,499View GPU → |
RTX 4070 TiEstimated NVIDIA | ~25 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used12GB total on card | $799View GPU → |
RTX 4070 SuperEstimated NVIDIA | ~25 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used12GB total on card | $599View GPU → |
RX 9070Tight VRAM AMD | ~25 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $499View GPU → |
Intel Arc A770 16GBTight VRAM Intel | ~25 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $349View GPU → |
RTX 4070Estimated NVIDIA | ~24 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used12GB total on card | $599View GPU → |
RX 6900 XTTight VRAM AMD | ~24 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $999View GPU → |
RX 6800 XTTight VRAM AMD | ~23 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $649View GPU → |
Intel Arc A750Tight VRAM Intel | ~22 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used8GB total on card | $289View GPU → |
NVIDIA A4000Tight VRAM NVIDIA | ~22 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $999View GPU → |
RTX 3070Tight VRAM NVIDIA | ~22 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used8GB total on card | $499View GPU → |
Intel Arc B580Estimated Intel | ~21 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used12GB total on card | $249View GPU → |
Apple M4 MaxEstimated Apple | ~21 tok/s FP16 | 30GB VRAM used128GB total on card | $3,999View GPU → |
RX 7700 XTEstimated AMD | ~19 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used12GB total on card | $449View GPU → |
Intel Arc B570Estimated Intel | ~18 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used10GB total on card | $219View GPU → |
Intel Arc Pro A60Estimated Intel | ~17 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used12GB total on card | $599View GPU → |
NVIDIA L4Estimated NVIDIA | ~17 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used24GB total on card | $5,000View GPU → |
RTX 3060 12GBEstimated NVIDIA | ~17 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used12GB total on card | $329View GPU → |
Apple M3 MaxEstimated Apple | ~15 tok/s FP16 | 30GB VRAM used128GB total on card | $3,999View GPU → |
Apple M2 MaxEstimated Apple | ~15 tok/s FP16 | 30GB VRAM used96GB total on card | $3,199View GPU → |
RTX 4060 Ti 8GBTight VRAM NVIDIA | ~14 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used8GB total on card | $399View GPU → |
RTX 4060 Ti 16GBTight VRAM NVIDIA | ~14 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $499View GPU → |
RTX 4060Tight VRAM NVIDIA | ~13 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used8GB total on card | $299View GPU → |
RX 7600Tight VRAM AMD | ~13 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used8GB total on card | $269View GPU → |
RX 7600 XTTight VRAM AMD | ~13 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used16GB total on card | $329View GPU → |
Intel Arc Pro A40Data coming soon Intel | ~13 tok/s FP16⚠ Insufficient VRAM | 30GB VRAM used6GB total on card | $399View GPU → |
Apple M4 ProEstimated Apple | ~10 tok/s FP16 | 30GB VRAM used64GB total on card | $1,999View GPU → |
AMD Ryzen AI Max+ 395Estimated AMD | ~10 tok/s FP16 | 30GB VRAM used128GB total on card | EnterpriseView GPU → |
AMD Ryzen AI Max 385Estimated AMD | ~10 tok/s FP16 | 30GB VRAM used128GB total on card | EnterpriseView GPU → |
AMD Ryzen AI Max Pro 385Estimated AMD | ~10 tok/s FP16 | 30GB VRAM used128GB total on card | EnterpriseView GPU → |
Apple M2 ProEstimated Apple | ~8 tok/s FP16 | 30GB VRAM used32GB total on card | $1,999View GPU → |
Apple M3 ProEstimated Apple | ~6 tok/s FP16 | 30GB VRAM used36GB total on card | $1,999View GPU → |
Openpipe Qwen3 14B Instruct has 15B parameters and needs about 8GB of VRAM at Q4 - choose the best GPU for your needs
For Better Performance
Run Openpipe Qwen3 14B Instruct faster with AMD Instinct MI300X. For about $14,601 more, significantly boost your tokens/sec performance.
Hardware requirements and model sizes at a glance.
| Component | Minimum | Recommended | Optimal |
|---|---|---|---|
| VRAM | 8GB (Q4) | 15GB (Q8) | 30GB (FP16) |
| RAM | 16GB | 32GB | 64GB |
| Disk | 10GB | 20GB | - |
| Model size | 8GB (Q4) | 15GB (Q8) | 30GB (FP16) |
| CPU | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) | Modern CPU (Ryzen 5/Intel i5 or better) |
Note: Performance estimates are calculated. Real results may vary. Methodology · Submit real data
Common questions about running Openpipe Qwen3 14B Instruct locally
This model delivers strong local performance when paired with modern GPUs. Use the hardware guidance below to choose the right quantization tier for your build.
Use runtimes like llama.cpp, text-generation-webui, or vLLM. Download the quantized weights from Hugging Face, ensure you have enough VRAM for your target quantization, and launch with GPU acceleration (CUDA/ROCm/Metal).
Start with Q4 for wide GPU compatibility. Upgrade to Q8 if you have spare VRAM and want extra quality. FP16 delivers the highest fidelity but demands workstation or multi-GPU setups.
Q4_K_M and Q5_K_M are GGUF quantization formats that balance quality and VRAM usage. Q4_K_M uses about 8GB VRAM. Q5_K_M uses about 12GB VRAM and keeps more accuracy. Q8 (~15GB) offers near-FP16 quality. Standard Q4 is the most memory-efficient option for Openpipe Qwen3 14B Instruct.
Official weights are available via Hugging Face. Quantized builds (Q4, Q8) can be loaded into runtimes like llama.cpp, text-generation-webui, or vLLM. Always verify the publisher before downloading.
See how Openpipe Qwen3 14B Instruct compares to other popular models.