Llama 3 8B Instruct GPTQ 4 Bit — Hardware Requirements & GPU Compatibility
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95 downloads
26 likesSpecifications
- Publisher
- astronomer
- Family
- Llama 3
- Parameters
- 8.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2024-04-22
- License
- Other
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HuggingFace
How Much VRAM Does Llama 3 8B Instruct GPTQ 4 Bit 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 Llama 3 8B Instruct GPTQ 4 Bit?
BF16 · 16.6 GBLlama 3 8B Instruct GPTQ 4 Bit (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 Llama 3 8B Instruct GPTQ 4 Bit?
BF16 · 16.6 GB21 devices with unified memory can run Llama 3 8B Instruct GPTQ 4 Bit, 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 Llama 3 8B Instruct GPTQ 4 Bit need?
Llama 3 8B Instruct GPTQ 4 Bit 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 Llama 3 8B Instruct GPTQ 4 Bit on a Mac?
Llama 3 8B Instruct GPTQ 4 Bit 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 Llama 3 8B Instruct GPTQ 4 Bit locally?
Yes — Llama 3 8B Instruct GPTQ 4 Bit 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 Llama 3 8B Instruct GPTQ 4 Bit?
At BF16, Llama 3 8B Instruct GPTQ 4 Bit 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 Llama 3 8B Instruct GPTQ 4 Bit?
At BF16, the download is about 16.06 GB.
- Which GPUs can run Llama 3 8B Instruct GPTQ 4 Bit?
6 consumer GPUs can run Llama 3 8B Instruct GPTQ 4 Bit 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 Llama 3 8B Instruct GPTQ 4 Bit?
21 devices with unified memory can run Llama 3 8B Instruct GPTQ 4 Bit 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.