Meditron 70B — Hardware Requirements & GPU Compatibility
Chat
Meditron 70B is a 69.0B-parameter open language model from epfl-llm. At BF16 it needs about 151.75 GB of VRAM — see which GPUs and Macs can run it below.
277 downloads
263 likesSpecifications
- Publisher
- epfl-llm
- Parameters
- 69.0B
- Release Date
- 2023-12-07
- License
- Llama 2 Community
Get Started
HuggingFace
How Much VRAM Does Meditron 70B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 151.8 GB | — | 137.95 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Meditron 70B?
BF16 · 151.8 GBMeditron 70B (BF16) requires 151.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 198+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Meditron 70B?
BF16 · 151.8 GB4 devices with unified memory can run Meditron 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 Meditron 70B need?
Meditron 70B requires 151.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 69.0B × 16 bits ÷ 8 = 138 GB
KV Cache + Overhead ≈ 13.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF16151.8 GB- Can NVIDIA GeForce RTX 5090 run Meditron 70B?
No — Meditron 70B requires at least 151.8 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Meditron 70B on a Mac?
Meditron 70B requires at least 151.8 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 Meditron 70B locally?
Yes — Meditron 70B can run locally on consumer hardware. At BF16 quantization it needs 151.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Meditron 70B?
At BF16, Meditron 70B can reach ~19 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 ÷ 151.8 × 0.55 = ~19 tok/s
Estimated speed at BF16 (151.8 GB)
~19 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Meditron 70B?
At BF16, the download is about 137.95 GB.
- Which GPUs can run Meditron 70B?
No single consumer GPU has enough VRAM to run Meditron 70B at BF16 (151.8 GB). Multi-GPU or professional hardware is required.
- Which devices can run Meditron 70B?
4 devices with unified memory can run Meditron 70B at BF16 (151.8 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.