CodeUp Llama 2 13B Chat HF GGUF — Hardware Requirements & GPU Compatibility
Code
299 downloads
9 likesSpecifications
- Publisher
- TheBloke
- Family
- Llama 2
- Parameters
- 13B
- License
- OpenRAIL++
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HuggingFace
How Much VRAM Does CodeUp Llama 2 13B Chat HF GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 28.6 GB | — | 26.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run CodeUp Llama 2 13B Chat HF GGUF?
BF16 · 28.6 GBCodeUp Llama 2 13B Chat HF GGUF (BF16) requires 28.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 38+ GB is recommended. 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 CodeUp Llama 2 13B Chat HF GGUF?
BF16 · 28.6 GB15 devices with unified memory can run CodeUp Llama 2 13B Chat HF GGUF, 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 CodeUp Llama 2 13B Chat HF GGUF need?
CodeUp Llama 2 13B Chat HF GGUF requires 28.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 13B × 16 bits ÷ 8 = 26 GB
KV Cache + Overhead ≈ 2.6 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1628.6 GB- Can I run CodeUp Llama 2 13B Chat HF GGUF on a Mac?
CodeUp Llama 2 13B Chat HF GGUF requires at least 28.6 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 CodeUp Llama 2 13B Chat HF GGUF locally?
Yes — CodeUp Llama 2 13B Chat HF GGUF can run locally on consumer hardware. At BF16 quantization it needs 28.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is CodeUp Llama 2 13B Chat HF GGUF?
At BF16, CodeUp Llama 2 13B Chat HF GGUF can reach ~102 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 ÷ 28.6 × 0.55 = ~102 tok/s
Estimated speed at BF16 (28.6 GB)
AMD Instinct MI300X~102 tok/sNVIDIA H100 SXM~76 tok/sAMD Instinct MI250X~63 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of CodeUp Llama 2 13B Chat HF GGUF?
At BF16, the download is about 26.00 GB.
- Which GPUs can run CodeUp Llama 2 13B Chat HF GGUF?
1 consumer GPU can run CodeUp Llama 2 13B Chat HF GGUF at BF16 (28.6 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run CodeUp Llama 2 13B Chat HF GGUF?
15 devices with unified memory can run CodeUp Llama 2 13B Chat HF GGUF at BF16 (28.6 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.