H2o Danube3 4B Base — Hardware Requirements & GPU Compatibility
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H2o Danube3 4B Base is a 4.0B-parameter open language model from h2oai. It supports a context window of up to 8,192 tokens. At BF16 it needs about 8.41 GB of VRAM — see which GPUs and Macs can run it below.
1.6K downloads
23 likesSpecifications
- Publisher
- h2oai
- Parameters
- 4.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 8,192 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2024-07-15
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does H2o Danube3 4B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 8.4 GB | 9.0 GB | 7.92 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run H2o Danube3 4B Base?
BF16 · 8.4 GBH2o Danube3 4B Base (BF16) requires 8.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. Using the full 8K context window can add up to 0.6 GB, bringing total usage to 9.0 GB. 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 H2o Danube3 4B Base?
BF16 · 8.4 GB27 devices with unified memory can run H2o Danube3 4B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does H2o Danube3 4B Base need?
H2o Danube3 4B Base requires 8.4 GB of VRAM at BF16. Full 8K context adds up to 0.6 GB (9.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.0B × 16 bits ÷ 8 = 7.9 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.1 GB (at full 8K context)
VRAM usage by quantization
BF168.4 GBBF16 + full context9.0 GB- Can I run H2o Danube3 4B Base on a Mac?
H2o Danube3 4B Base requires at least 8.4 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 H2o Danube3 4B Base locally?
Yes — H2o Danube3 4B Base can run locally on consumer hardware. At BF16 quantization it needs 8.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is H2o Danube3 4B Base?
At BF16, H2o Danube3 4B Base can reach ~347 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~78 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 ÷ 8.4 × 0.55 = ~347 tok/s
Estimated speed at BF16 (8.4 GB)
~347 tok/s~78 tok/s~259 tok/s~214 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of H2o Danube3 4B Base?
At BF16, the download is about 7.92 GB.
- Which GPUs can run H2o Danube3 4B Base?
28 consumer GPUs can run H2o Danube3 4B Base at BF16 (8.4 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 H2o Danube3 4B Base?
27 devices with unified memory can run H2o Danube3 4B Base at BF16 (8.4 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.