Llama3 OpenBioLLM 70B — Hardware Requirements & GPU Compatibility
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3.4K downloads
499 likesSpecifications
- Publisher
- aaditya
- Family
- Llama 3
- Parameters
- 70B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-01-18
- License
- Llama 3 Community
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HuggingFace
How Much VRAM Does Llama3 OpenBioLLM 70B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 141.0 GB | 143.0 GB | 140.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Llama3 OpenBioLLM 70B?
BF16 · 141.0 GBLlama3 OpenBioLLM 70B (BF16) requires 141.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 184+ GB is recommended. Using the full 8K context window can add up to 2.0 GB, bringing total usage to 143.0 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama3 OpenBioLLM 70B?
BF16 · 141.0 GB4 devices with unified memory can run Llama3 OpenBioLLM 70B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Pro M2 Ultra (192 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Llama3 OpenBioLLM 70B need?
Llama3 OpenBioLLM 70B requires 141.0 GB of VRAM at BF16. Full 8K context adds up to 2.0 GB (143.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 70B × 16 bits ÷ 8 = 140 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3 GB (at full 8K context)
VRAM usage by quantization
BF16141.0 GBBF16 + full context143.0 GB- Can NVIDIA GeForce RTX 5090 run Llama3 OpenBioLLM 70B?
No — Llama3 OpenBioLLM 70B requires at least 141.0 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Llama3 OpenBioLLM 70B on a Mac?
Llama3 OpenBioLLM 70B requires at least 141.0 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 Llama3 OpenBioLLM 70B locally?
Yes — Llama3 OpenBioLLM 70B can run locally on consumer hardware. At BF16 quantization it needs 141.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama3 OpenBioLLM 70B?
At BF16, Llama3 OpenBioLLM 70B can reach ~21 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 ÷ 141.0 × 0.55 = ~21 tok/s
Estimated speed at BF16 (141.0 GB)
AMD Instinct MI300X~21 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llama3 OpenBioLLM 70B?
At BF16, the download is about 140.00 GB.
- Which GPUs can run Llama3 OpenBioLLM 70B?
No single consumer GPU has enough VRAM to run Llama3 OpenBioLLM 70B at BF16 (141.0 GB). Multi-GPU or professional hardware is required.
- Which devices can run Llama3 OpenBioLLM 70B?
4 devices with unified memory can run Llama3 OpenBioLLM 70B at BF16 (141.0 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.