Llama 2 13B HF — Hardware Requirements & GPU Compatibility
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Llama 2 13B HF is a 13.0B-parameter open language model from Meta in the Llama 2 family. At Q4_K_M it needs about 8.59 GB of VRAM — see which GPUs and Macs can run it below.
21.7K downloads
630 likesSpecifications
- Publisher
- Meta
- Family
- Llama 2
- Parameters
- 13.0B
- Release Date
- 2023-07-13
- License
- Llama 2 Community
Get Started
HuggingFace
How Much VRAM Does Llama 2 13B HF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 6.1 GB | — | 5.53 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 7.0 GB | — | 6.35 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 8.6 GB | — | 7.81 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 10.2 GB | — | 9.27 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 11.8 GB | — | 10.74 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 14.3 GB | — | 13.02 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 28.6 GB | — | 26.03 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Llama 2 13B HF?
Q4_K_M · 8.6 GBLlama 2 13B HF (Q4_K_M) requires 8.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. 39 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Llama 2 13B HF?
Q4_K_M · 8.6 GB49 devices with unified memory can run Llama 2 13B HF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightBenchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Llama 2 13B HF need?
Llama 2 13B HF requires 8.6 GB of VRAM at Q4_K_M, or 28.6 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 13.0B × 4.8 bits ÷ 8 = 7.8 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M8.6 GB- Can NVIDIA GeForce RTX 4090 run Llama 2 13B HF?
Yes, at Q8_0 (14.3 GB) or lower. Higher quantizations like BF16 (28.6 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Llama 2 13B HF?
For Llama 2 13B HF, Q4_K_M (8.6 GB) offers the best balance of quality and VRAM usage. Q5_K_M (10.2 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 6.1 GB.
VRAM requirement by quantization
Q2_K6.1 GBQ4_K_M ★8.6 GBQ5_K_M10.2 GBQ6_K11.8 GBQ8_014.3 GBBF1628.6 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llama 2 13B HF on a Mac?
Llama 2 13B HF requires at least 6.1 GB at Q2_K, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Llama 2 13B HF locally?
Yes — Llama 2 13B HF can run locally on consumer hardware. At Q4_K_M quantization it needs 8.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 2 13B HF?
At Q4_K_M, Llama 2 13B HF can reach ~512 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~76 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 8.6 × 0.65 = ~605 tok/s
Estimated speed at Q4_K_M (8.6 GB)
~605 tok/s~76 tok/s~605 tok/s~512 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama 2 13B HF?
At Q4_K_M, the download is about 7.81 GB. The full-precision BF16 version is 26.03 GB. The smallest option (Q2_K) is 5.53 GB.
- Which GPUs can run Llama 2 13B HF?
39 consumer GPUs can run Llama 2 13B HF at Q4_K_M (8.6 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llama 2 13B HF?
52 devices with unified memory can run Llama 2 13B HF at Q4_K_M (8.6 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.