Minimum VRAM
26GB
FP16 (full model) • Q4 option ≈ 7GB
Best Performance
AMD Instinct MI300X
~218 tok/s • FP16
Most Affordable
RTX 5090
FP16 • ~85 tok/s • From $1,999
Quick answer: Meta Llama Llama 2 13B Chat HF needs roughly 7GB VRAM for Q4_K_M and 10GB for Q5_K_M. Use Q8 (13GB) or FP16 (26GB) 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 Meta Llama Llama 2 13B Chat HF 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 | 26GB VRAM used192GB total on card | $15,000View GPU → |
NVIDIA H200 SXM 141GBEstimated NVIDIA | ~196 tok/s FP16 | 26GB VRAM used141GB total on card | $35,000View GPU → |
NVIDIA H100 SXM5 80GBEstimated NVIDIA | ~141 tok/s FP16 | 26GB VRAM used80GB total on card | $30,000View GPU → |
AMD Instinct MI250XEstimated AMD | ~136 tok/s FP16 | 26GB VRAM used128GB total on card | $11,000View GPU → |
NVIDIA H100 PCIe 80GBEstimated NVIDIA | ~90 tok/s FP16 | 26GB VRAM used80GB total on card | $25,000View GPU → |
RTX 5090Estimated NVIDIA | ~85 tok/s FP16 | 26GB VRAM used32GB total on card | $1,999View GPU → |
NVIDIA A100 80GB SXM4Estimated NVIDIA | ~83 tok/s FP16 | 26GB VRAM used80GB total on card | $11,000View GPU → |
AMD Instinct MI210Estimated AMD | ~68 tok/s FP16 | 26GB VRAM used64GB total on card | $6,000View GPU → |
NVIDIA A100 40GB PCIeEstimated NVIDIA | ~65 tok/s FP16 | 26GB VRAM used40GB total on card | $9,000View GPU → |
RTX 4090Estimated NVIDIA | ~51 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used24GB total on card | $1,599View GPU → |
NVIDIA RTX 6000 AdaEstimated NVIDIA | ~51 tok/s FP16 | 26GB VRAM used48GB total on card | $6,999View GPU → |
NVIDIA L40Estimated NVIDIA | ~47 tok/s FP16 | 26GB VRAM used48GB total on card | $7,999View GPU → |
NVIDIA L40SEstimated NVIDIA | ~47 tok/s FP16 | 26GB VRAM used48GB total on card | $10,000View GPU → |
RTX 5080Estimated NVIDIA | ~45 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $1,199View GPU → |
RTX 3090Estimated NVIDIA | ~44 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used24GB total on card | $1,499View GPU → |
RX 7900 XTXEstimated AMD | ~41 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used24GB total on card | $999View GPU → |
AMD Radeon Pro W7900Estimated AMD | ~41 tok/s FP16 | 26GB VRAM used48GB total on card | $3,999View GPU → |
RTX 5070 TiEstimated NVIDIA | ~41 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $799View GPU → |
NVIDIA A6000Estimated NVIDIA | ~38 tok/s FP16 | 26GB VRAM used48GB total on card | $4,699View GPU → |
RTX 4080 SuperEstimated NVIDIA | ~36 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $999View GPU → |
RTX 3080Estimated NVIDIA | ~36 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used10GB total on card | $699View GPU → |
NVIDIA A5000Estimated NVIDIA | ~36 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used24GB total on card | $2,399View GPU → |
RTX 4080Estimated NVIDIA | ~35 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $1,199View GPU → |
RX 7900 XTEstimated AMD | ~35 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used20GB total on card | $899View GPU → |
RTX 4070 Ti SuperEstimated NVIDIA | ~32 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $799View GPU → |
RTX 5070Estimated NVIDIA | ~31 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used12GB total on card | $599View GPU → |
Apple M2 UltraEstimated Apple | ~31 tok/s FP16 | 26GB VRAM used192GB total on card | $5,999View GPU → |
RX 9070 XTEstimated AMD | ~28 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $599View GPU → |
RX 7800 XTEstimated AMD | ~27 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $499View GPU → |
RX 7900 GREEstimated AMD | ~26 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $649View GPU → |
AMD Radeon Pro W7800Estimated AMD | ~25 tok/s FP16 | 26GB VRAM used32GB total on card | $2,499View GPU → |
RTX 4070 TiEstimated NVIDIA | ~25 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used12GB total on card | $799View GPU → |
RTX 4070 SuperEstimated NVIDIA | ~25 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used12GB total on card | $599View GPU → |
RX 9070Estimated AMD | ~25 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $499View GPU → |
Intel Arc A770 16GBEstimated Intel | ~25 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $349View GPU → |
RTX 4070Estimated NVIDIA | ~24 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used12GB total on card | $599View GPU → |
RX 6900 XTEstimated AMD | ~24 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $999View GPU → |
RX 6800 XTEstimated AMD | ~23 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $649View GPU → |
Intel Arc A750Tight VRAM Intel | ~22 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used8GB total on card | $289View GPU → |
NVIDIA A4000Estimated NVIDIA | ~22 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $999View GPU → |
RTX 3070Tight VRAM NVIDIA | ~22 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used8GB total on card | $499View GPU → |
Intel Arc B580Estimated Intel | ~21 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used12GB total on card | $249View GPU → |
Apple M4 MaxEstimated Apple | ~21 tok/s FP16 | 26GB VRAM used128GB total on card | $3,999View GPU → |
RX 7700 XTEstimated AMD | ~19 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used12GB total on card | $449View GPU → |
Intel Arc B570Estimated Intel | ~18 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used10GB total on card | $219View GPU → |
Intel Arc Pro A60Estimated Intel | ~17 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used12GB total on card | $599View GPU → |
NVIDIA L4Estimated NVIDIA | ~17 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used24GB total on card | $5,000View GPU → |
RTX 3060 12GBEstimated NVIDIA | ~17 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used12GB total on card | $329View GPU → |
Apple M3 MaxEstimated Apple | ~15 tok/s FP16 | 26GB VRAM used128GB total on card | $3,999View GPU → |
Apple M2 MaxEstimated Apple | ~15 tok/s FP16 | 26GB VRAM used96GB total on card | $3,199View GPU → |
RTX 4060 Ti 16GBEstimated NVIDIA | ~14 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $499View GPU → |
RTX 4060 Ti 8GBTight VRAM NVIDIA | ~14 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used8GB total on card | $399View GPU → |
RTX 4060Tight VRAM NVIDIA | ~13 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used8GB total on card | $299View GPU → |
RX 7600 XTEstimated AMD | ~13 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used16GB total on card | $329View GPU → |
RX 7600Tight VRAM AMD | ~13 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used8GB total on card | $269View GPU → |
Intel Arc Pro A40Data coming soon Intel | ~13 tok/s FP16⚠ Insufficient VRAM | 26GB VRAM used6GB total on card | $399View GPU → |
Apple M4 ProEstimated Apple | ~10 tok/s FP16 | 26GB VRAM used64GB total on card | $1,999View GPU → |
AMD Ryzen AI Max+ 395Estimated AMD | ~10 tok/s FP16 | 26GB VRAM used128GB total on card | EnterpriseView GPU → |
AMD Ryzen AI Max 385Estimated AMD | ~10 tok/s FP16 | 26GB VRAM used128GB total on card | EnterpriseView GPU → |
AMD Ryzen AI Max Pro 385Estimated AMD | ~10 tok/s FP16 | 26GB VRAM used128GB total on card | EnterpriseView GPU → |
Apple M2 ProEstimated Apple | ~8 tok/s FP16 | 26GB VRAM used32GB total on card | $1,999View GPU → |
Apple M3 ProEstimated Apple | ~6 tok/s FP16 | 26GB VRAM used36GB total on card | $1,999View GPU → |
Meta Llama Llama 2 13B Chat HF 13B parametre içerir ve 7GB VRAM gerektirir - choose the best GPU for your needs
For Better Performance
Run Meta Llama Llama 2 13B Chat HF faster with AMD Instinct MI300X. For just $146 more, significantly boost your tokens/sec performance.
Hardware requirements and model sizes at a glance.
| Component | Minimum | Recommended | Optimal |
|---|---|---|---|
| VRAM | 7GB (Q4) | 13GB (Q8) | 26GB (FP16) |
| RAM | 16GB | 32GB | 64GB |
| Disk | 10GB | 20GB | - |
| Model size | 7GB (Q4) | 13GB (Q8) | 26GB (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 Meta Llama Llama 2 13B Chat HF locally
Llama 3 13B hits the sweet spot between small-model cost and 70B accuracy. Give it a 16GB GPU and it becomes a capable coding and reasoning partner.
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 7GB VRAM. Q5_K_M uses about 10GB VRAM and keeps more accuracy. Q8 (~13GB) offers near-FP16 quality. Standard Q4 is the most memory-efficient option for Meta Llama Llama 2 13B Chat HF.
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 Meta Llama Llama 2 13B Chat HF compares to other popular models.