Aryabhata 2.0 — Hardware Requirements & GPU Compatibility
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Aryabhata 2.0 is a 20.9B-parameter open language model from PhysicsWallahAI. It supports a context window of up to 131,072 tokens. At BF16 it needs about 42.20 GB of VRAM — see which GPUs and Macs can run it below.
331 downloads
3 likesSpecifications
- Publisher
- PhysicsWallahAI
- Parameters
- 20.9B
- Architecture
- GptOssForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 201,088
- Release Date
- 2026-06-03
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Aryabhata 2.0 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 42.2 GB | 46.7 GB | 41.83 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Aryabhata 2.0?
BF16 · 42.2 GBAryabhata 2.0 (BF16) requires 42.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 55+ GB is recommended. Using the full 131K context window can add up to 4.5 GB, bringing total usage to 46.7 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Aryabhata 2.0?
BF16 · 42.2 GB11 devices with unified memory can run Aryabhata 2.0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M4 Max (48 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Aryabhata 2.0 need?
Aryabhata 2.0 requires 42.2 GB of VRAM at BF16. Full 131K context adds up to 4.5 GB (46.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 20.9B × 16 bits ÷ 8 = 41.8 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.9 GB (at full 131K context)
VRAM usage by quantization
BF1642.2 GBBF16 + full context46.7 GB- Can NVIDIA GeForce RTX 5090 run Aryabhata 2.0?
No — Aryabhata 2.0 requires at least 42.2 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Aryabhata 2.0 on a Mac?
Aryabhata 2.0 requires at least 42.2 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 Aryabhata 2.0 locally?
Yes — Aryabhata 2.0 can run locally on consumer hardware. At BF16 quantization it needs 42.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Aryabhata 2.0?
At BF16, Aryabhata 2.0 can reach ~69 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 ÷ 42.2 × 0.55 = ~69 tok/s
Estimated speed at BF16 (42.2 GB)
~69 tok/s~52 tok/s~43 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Aryabhata 2.0?
At BF16, the download is about 41.83 GB.
- Which GPUs can run Aryabhata 2.0?
No single consumer GPU has enough VRAM to run Aryabhata 2.0 at BF16 (42.2 GB). Multi-GPU or professional hardware is required.
- Which devices can run Aryabhata 2.0?
11 devices with unified memory can run Aryabhata 2.0 at BF16 (42.2 GB), including Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.