Minimum VRAM
16GB
FP16 (full model) • Q4 option ≈ 4GB
Best Performance
AMD Instinct MI300X
~290 tok/s • FP16
Most Affordable
RX 7600 XT
FP16 • ~17 tok/s • From $329
Quick answer: Nousresearch Hermes 3 Llama 3 1 8B needs roughly 4GB VRAM for Q4_K_M and 6GB for Q5_K_M. Use Q8 (8GB) or FP16 (16GB) for higher quality output.
Full-model (FP16) requirements are shown by default. Quantized builds like Q4 trade accuracy for lower VRAM usage.
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 | ~290 tok/s FP16 | 16GB VRAM used192GB total on card | $15,000View GPU → |
NVIDIA H200 SXM 141GBEstimated NVIDIA | ~262 tok/s FP16 | 16GB VRAM used141GB total on card | $35,000View GPU → |
NVIDIA H100 SXM5 80GBEstimated NVIDIA | ~188 tok/s FP16 | 16GB VRAM used80GB total on card | $30,000View GPU → |
AMD Instinct MI250XEstimated AMD | ~181 tok/s FP16 | 16GB VRAM used128GB total on card | $11,000View GPU → |
NVIDIA H100 PCIe 80GBEstimated NVIDIA | ~119 tok/s FP16 | 16GB VRAM used80GB total on card | $25,000View GPU → |
RTX 5090Estimated NVIDIA | ~114 tok/s FP16 | 16GB VRAM used32GB total on card | $1,999View GPU → |
NVIDIA A100 80GB SXM4Estimated NVIDIA | ~111 tok/s FP16 | 16GB VRAM used80GB total on card | $11,000View GPU → |
AMD Instinct MI210Estimated AMD | ~90 tok/s FP16 | 16GB VRAM used64GB total on card | $6,000View GPU → |
NVIDIA A100 40GB PCIeEstimated NVIDIA | ~86 tok/s FP16 | 16GB VRAM used40GB total on card | $9,000View GPU → |
RTX 4090Estimated NVIDIA | ~68 tok/s FP16 | 16GB VRAM used24GB total on card | $1,599View GPU → |
NVIDIA RTX 6000 AdaEstimated NVIDIA | ~68 tok/s FP16 | 16GB VRAM used48GB total on card | $6,999View GPU → |
NVIDIA L40Estimated NVIDIA | ~63 tok/s FP16 | 16GB VRAM used48GB total on card | $7,999View GPU → |
NVIDIA L40SEstimated NVIDIA | ~63 tok/s FP16 | 16GB VRAM used48GB total on card | $10,000View GPU → |
RTX 5080Tight VRAM NVIDIA | ~60 tok/s FP16 | 16GB VRAM used16GB total on card | $1,199View GPU → |
RTX 3090Estimated NVIDIA | ~59 tok/s FP16 | 16GB VRAM used24GB total on card | $1,499View GPU → |
RX 7900 XTXEstimated AMD | ~55 tok/s FP16 | 16GB VRAM used24GB total on card | $999View GPU → |
AMD Radeon Pro W7900Estimated AMD | ~55 tok/s FP16 | 16GB VRAM used48GB total on card | $3,999View GPU → |
RTX 5070 TiTight VRAM NVIDIA | ~55 tok/s FP16 | 16GB VRAM used16GB total on card | $799View GPU → |
NVIDIA A6000Estimated NVIDIA | ~50 tok/s FP16 | 16GB VRAM used48GB total on card | $4,699View GPU → |
RTX 4080 SuperTight VRAM NVIDIA | ~48 tok/s FP16 | 16GB VRAM used16GB total on card | $999View GPU → |
RTX 3080Estimated NVIDIA | ~48 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used10GB total on card | $699View GPU → |
NVIDIA A5000Estimated NVIDIA | ~48 tok/s FP16 | 16GB VRAM used24GB total on card | $2,399View GPU → |
RTX 4080Tight VRAM NVIDIA | ~47 tok/s FP16 | 16GB VRAM used16GB total on card | $1,199View GPU → |
RX 7900 XTEstimated AMD | ~46 tok/s FP16 | 16GB VRAM used20GB total on card | $899View GPU → |
RTX 4070 Ti SuperTight VRAM NVIDIA | ~43 tok/s FP16 | 16GB VRAM used16GB total on card | $799View GPU → |
RTX 5070Estimated NVIDIA | ~41 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used12GB total on card | $599View GPU → |
Apple M2 UltraEstimated Apple | ~41 tok/s FP16 | 16GB VRAM used192GB total on card | $5,999View GPU → |
RX 9070 XTTight VRAM AMD | ~37 tok/s FP16 | 16GB VRAM used16GB total on card | $599View GPU → |
RX 7800 XTTight VRAM AMD | ~36 tok/s FP16 | 16GB VRAM used16GB total on card | $499View GPU → |
RX 7900 GRETight VRAM AMD | ~35 tok/s FP16 | 16GB VRAM used16GB total on card | $649View GPU → |
AMD Radeon Pro W7800Estimated AMD | ~34 tok/s FP16 | 16GB VRAM used32GB total on card | $2,499View GPU → |
RTX 4070 TiEstimated NVIDIA | ~34 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used12GB total on card | $799View GPU → |
RTX 4070 SuperEstimated NVIDIA | ~33 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used12GB total on card | $599View GPU → |
RX 9070Tight VRAM AMD | ~33 tok/s FP16 | 16GB VRAM used16GB total on card | $499View GPU → |
Intel Arc A770 16GBTight VRAM Intel | ~33 tok/s FP16 | 16GB VRAM used16GB total on card | $349View GPU → |
RTX 4070Estimated NVIDIA | ~32 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used12GB total on card | $599View GPU → |
RX 6900 XTTight VRAM AMD | ~31 tok/s FP16 | 16GB VRAM used16GB total on card | $999View GPU → |
RX 6800 XTTight VRAM AMD | ~31 tok/s FP16 | 16GB VRAM used16GB total on card | $649View GPU → |
Intel Arc A750Tight VRAM Intel | ~30 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used8GB total on card | $289View GPU → |
NVIDIA A4000Tight VRAM NVIDIA | ~29 tok/s FP16 | 16GB VRAM used16GB total on card | $999View GPU → |
RTX 3070Tight VRAM NVIDIA | ~29 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used8GB total on card | $499View GPU → |
Intel Arc B580Estimated Intel | ~29 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used12GB total on card | $249View GPU → |
Apple M4 MaxEstimated Apple | ~28 tok/s FP16 | 16GB VRAM used128GB total on card | $3,999View GPU → |
RX 7700 XTEstimated AMD | ~26 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used12GB total on card | $449View GPU → |
Intel Arc B570Estimated Intel | ~24 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used10GB total on card | $219View GPU → |
Intel Arc Pro A60Estimated Intel | ~23 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used12GB total on card | $599View GPU → |
NVIDIA L4Estimated NVIDIA | ~23 tok/s FP16 | 16GB VRAM used24GB total on card | $5,000View GPU → |
RTX 3060 12GBEstimated NVIDIA | ~22 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used12GB total on card | $329View GPU → |
Apple M3 MaxEstimated Apple | ~20 tok/s FP16 | 16GB VRAM used128GB total on card | $3,999View GPU → |
Apple M2 MaxEstimated Apple | ~20 tok/s FP16 | 16GB VRAM used96GB total on card | $3,199View GPU → |
RTX 4060 Ti 8GBTight VRAM NVIDIA | ~19 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used8GB total on card | $399View GPU → |
RTX 4060 Ti 16GBTight VRAM NVIDIA | ~19 tok/s FP16 | 16GB VRAM used16GB total on card | $499View GPU → |
RTX 4060Tight VRAM NVIDIA | ~17 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used8GB total on card | $299View GPU → |
Intel Arc Pro A40Estimated Intel | ~17 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used6GB total on card | $399View GPU → |
RX 7600Tight VRAM AMD | ~17 tok/s FP16⚠ Insufficient VRAM | 16GB VRAM used8GB total on card | $269View GPU → |
RX 7600 XTTight VRAM AMD | ~17 tok/s FP16 | 16GB VRAM used16GB total on card | $329View GPU → |
Apple M4 ProEstimated Apple | ~14 tok/s FP16 | 16GB VRAM used64GB total on card | $1,999View GPU → |
AMD Ryzen AI Max+ 395Estimated AMD | ~14 tok/s FP16 | 16GB VRAM used128GB total on card | EnterpriseView GPU → |
AMD Ryzen AI Max 385Estimated AMD | ~14 tok/s FP16 | 16GB VRAM used128GB total on card | EnterpriseView GPU → |
AMD Ryzen AI Max Pro 385Estimated AMD | ~14 tok/s FP16 | 16GB VRAM used128GB total on card | EnterpriseView GPU → |
Apple M2 ProEstimated Apple | ~10 tok/s FP16 | 16GB VRAM used32GB total on card | $1,999View GPU → |
Apple M3 ProEstimated Apple | ~8 tok/s FP16 | 16GB VRAM used36GB total on card | $1,999View GPU → |
Nousresearch Hermes 3 Llama 3 1 8B 8B parametre içerir ve 4GB VRAM gerektirir - choose the best GPU for your needs
For Better Performance
Run Nousresearch Hermes 3 Llama 3 1 8B faster with AMD Instinct MI300X. For just $150 more, significantly boost your tokens/sec performance.
Hardware requirements and model sizes at a glance.
| Component | Minimum | Recommended | Optimal |
|---|---|---|---|
| VRAM | 4GB (Q4) | 8GB (Q8) | 16GB (FP16) |
| RAM | 16GB | 32GB | 64GB |
| Disk | 10GB | 20GB | - |
| Model size | 4GB (Q4) | 8GB (Q8) | 16GB (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 Nousresearch Hermes 3 Llama 3 1 8B locally
Llama 3 8B is the go-to lightweight assistant. It runs on almost any 12GB GPU, making it ideal for chatbots, agent prototypes, and personal copilots.
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 4GB VRAM. Q5_K_M uses about 6GB VRAM and keeps more accuracy. Q8 (~8GB) offers near-FP16 quality. Standard Q4 is the most memory-efficient option for Nousresearch Hermes 3 Llama 3 1 8B.
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 Nousresearch Hermes 3 Llama 3 1 8B compares to other popular models.