Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged — Hardware Requirements & GPU Compatibility
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Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged is a 13.0B-parameter open language model from dhmeltzer in the Llama 2 family. It supports a context window of up to 4,096 tokens. At BF16 it needs about 28.01 GB of VRAM — see which GPUs and Macs can run it below.
7 downloads
1 likesSpecifications
- Publisher
- dhmeltzer
- Family
- Llama 2
- Parameters
- 13.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2023-09-14
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How Much VRAM Does Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 28.0 GB | 29.7 GB | 26.03 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged?
BF16 · 28.0 GBLlama 2 13B HF Eli5 Cleaned 1024 Qlora Merged (BF16) requires 28.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 37+ GB is recommended. Using the full 4K context window can add up to 1.7 GB, bringing total usage to 29.7 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged?
BF16 · 28.0 GB15 devices with unified memory can run Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged need?
Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged requires 28.0 GB of VRAM at BF16. Full 4K context adds up to 1.7 GB (29.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 13.0B × 16 bits ÷ 8 = 26 GB
KV Cache + Overhead ≈ 2 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3.7 GB (at full 4K context)
VRAM usage by quantization
BF1628.0 GBBF16 + full context29.7 GB- Can I run Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged on a Mac?
Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged requires at least 28.0 GB at BF16, 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 Eli5 Cleaned 1024 Qlora Merged locally?
Yes — Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged can run locally on consumer hardware. At BF16 quantization it needs 28.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged?
At BF16, Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged can reach ~104 tok/s on AMD Instinct MI300X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 28.0 × 0.55 = ~104 tok/s
Estimated speed at BF16 (28.0 GB)
~104 tok/s~78 tok/s~64 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 Eli5 Cleaned 1024 Qlora Merged?
At BF16, the download is about 26.03 GB.
- Which GPUs can run Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged?
1 consumer GPU can run Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged at BF16 (28.0 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged?
15 devices with unified memory can run Llama 2 13B HF Eli5 Cleaned 1024 Qlora Merged at BF16 (28.0 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.