Human Like LLama3 8B Instruct — Hardware Requirements & GPU Compatibility
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105 downloads
24 likesSpecifications
- Publisher
- HumanLLMs
- Family
- Llama 3
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-01-15
- License
- Llama 3 Community
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HuggingFace
How Much VRAM Does Human Like LLama3 8B Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 16.6 GB | 17.4 GB | 16.06 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Human Like LLama3 8B Instruct?
BF16 · 16.6 GBHuman Like LLama3 8B Instruct (BF16) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 8K context window can add up to 0.8 GB, bringing total usage to 17.4 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Human Like LLama3 8B Instruct?
BF16 · 16.6 GB21 devices with unified memory can run Human Like LLama3 8B Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Human Like LLama3 8B Instruct need?
Human Like LLama3 8B Instruct requires 16.6 GB of VRAM at BF16. Full 8K context adds up to 0.8 GB (17.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.0B × 16 bits ÷ 8 = 16.1 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.3 GB (at full 8K context)
VRAM usage by quantization
BF1616.6 GBBF16 + full context17.4 GB- Can I run Human Like LLama3 8B Instruct on a Mac?
Human Like LLama3 8B Instruct requires at least 16.6 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 Human Like LLama3 8B Instruct locally?
Yes — Human Like LLama3 8B Instruct can run locally on consumer hardware. At BF16 quantization it needs 16.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Human Like LLama3 8B Instruct?
At BF16, Human Like LLama3 8B Instruct can reach ~175 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~39 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 ÷ 16.6 × 0.55 = ~175 tok/s
Estimated speed at BF16 (16.6 GB)
AMD Instinct MI300X~175 tok/sNVIDIA GeForce RTX 4090~39 tok/sNVIDIA H100 SXM~131 tok/sAMD Instinct MI250X~108 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Human Like LLama3 8B Instruct?
At BF16, the download is about 16.06 GB.
- Which GPUs can run Human Like LLama3 8B Instruct?
6 consumer GPUs can run Human Like LLama3 8B Instruct at BF16 (16.6 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Human Like LLama3 8B Instruct?
21 devices with unified memory can run Human Like LLama3 8B Instruct at BF16 (16.6 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.