Hermes 3 Llama 3.1 8B Q8 0 GGUF — Hardware Requirements & GPU Compatibility
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11 downloads
1 likesSpecifications
- Publisher
- YorkieOH10
- Family
- Llama 3
- Parameters
- 8B
- License
- Llama 3 Community
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HuggingFace
How Much VRAM Does Hermes 3 Llama 3.1 8B Q8 0 GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q8_0 | 8.00 | 8.8 GB | — | 8.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Hermes 3 Llama 3.1 8B Q8 0 GGUF?
Q8_0 · 8.8 GBHermes 3 Llama 3.1 8B Q8 0 GGUF (Q8_0) requires 8.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. 28 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 Hermes 3 Llama 3.1 8B Q8 0 GGUF?
Q8_0 · 8.8 GB27 devices with unified memory can run Hermes 3 Llama 3.1 8B Q8 0 GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Hermes 3 Llama 3.1 8B Q8 0 GGUF need?
Hermes 3 Llama 3.1 8B Q8 0 GGUF requires 8.8 GB of VRAM at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 8 bits ÷ 8 = 8 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q8_08.8 GB- Can I run Hermes 3 Llama 3.1 8B Q8 0 GGUF on a Mac?
Hermes 3 Llama 3.1 8B Q8 0 GGUF requires at least 8.8 GB at Q8_0, 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 Hermes 3 Llama 3.1 8B Q8 0 GGUF locally?
Yes — Hermes 3 Llama 3.1 8B Q8 0 GGUF can run locally on consumer hardware. At Q8_0 quantization it needs 8.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Hermes 3 Llama 3.1 8B Q8 0 GGUF?
At Q8_0, Hermes 3 Llama 3.1 8B Q8 0 GGUF can reach ~331 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~75 tok/s. 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 ÷ 8.8 × 0.55 = ~331 tok/s
Estimated speed at Q8_0 (8.8 GB)
AMD Instinct MI300X~331 tok/sNVIDIA GeForce RTX 4090~75 tok/sNVIDIA H100 SXM~248 tok/sAMD Instinct MI250X~205 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Hermes 3 Llama 3.1 8B Q8 0 GGUF?
At Q8_0, the download is about 8.00 GB.
- Which GPUs can run Hermes 3 Llama 3.1 8B Q8 0 GGUF?
28 consumer GPUs can run Hermes 3 Llama 3.1 8B Q8 0 GGUF at Q8_0 (8.8 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Hermes 3 Llama 3.1 8B Q8 0 GGUF?
27 devices with unified memory can run Hermes 3 Llama 3.1 8B Q8 0 GGUF at Q8_0 (8.8 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.