Helium 1 2B — Hardware Requirements & GPU Compatibility
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Helium 1 2B is a 2.0B-parameter open language model from kyutai. It supports a context window of up to 4,096 tokens. At BF16 it needs about 4.58 GB of VRAM — see which GPUs and Macs can run it below.
16.2K downloads
52 likesSpecifications
- Publisher
- kyutai
- Parameters
- 2.0B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 64,000
- Release Date
- 2025-04-30
- License
- CC BY-SA 4.0
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HuggingFace
How Much VRAM Does Helium 1 2B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 4.6 GB | 4.8 GB | 4.05 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Helium 1 2B?
BF16 · 4.6 GBHelium 1 2B (BF16) requires 4.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. Using the full 4K context window can add up to 0.2 GB, bringing total usage to 4.8 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Helium 1 2B?
BF16 · 4.6 GB33 devices with unified memory can run Helium 1 2B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Helium 1 2B need?
Helium 1 2B requires 4.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 2.0B × 16 bits ÷ 8 = 4 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 0.8 GB (at full 4K context)
VRAM usage by quantization
BF164.6 GBBF16 + full context4.8 GB- Can I run Helium 1 2B on a Mac?
Helium 1 2B requires at least 4.6 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 Helium 1 2B locally?
Yes — Helium 1 2B can run locally on consumer hardware. At BF16 quantization it needs 4.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Helium 1 2B?
At BF16, Helium 1 2B can reach ~637 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~143 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 ÷ 4.6 × 0.55 = ~637 tok/s
Estimated speed at BF16 (4.6 GB)
~637 tok/s~143 tok/s~476 tok/s~394 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Helium 1 2B?
At BF16, the download is about 4.05 GB.
- Which GPUs can run Helium 1 2B?
35 consumer GPUs can run Helium 1 2B at BF16 (4.6 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Helium 1 2B?
33 devices with unified memory can run Helium 1 2B at BF16 (4.6 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.