Xor — Hardware Requirements & GPU Compatibility
ChatVision
Xor is a 35.1B-parameter open language model from juspay. It supports a context window of up to 262,144 tokens. At BF16 it needs about 70.60 GB of VRAM — see which GPUs and Macs can run it below.
757 downloads
21 likesSpecifications
- Publisher
- juspay
- Parameters
- 35.1B
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-09-21
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Xor Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 70.6 GB | 81.3 GB | 70.21 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Xor?
BF16 · 70.6 GBXor (BF16) requires 70.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 92+ GB is recommended. Using the full 262K context window can add up to 10.7 GB, bringing total usage to 81.3 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Xor?
BF16 · 70.6 GB19 devices with unified memory can run Xor, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 96GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Xor need?
Xor requires 70.6 GB of VRAM at BF16. Full 262K context adds up to 10.7 GB (81.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 35.1B × 16 bits ÷ 8 = 70.2 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11 GB (at full 262K context)
VRAM usage by quantization
BF1670.6 GBBF16 + full context81.3 GB- Can NVIDIA GeForce RTX 5090 run Xor?
No — Xor requires at least 70.6 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Xor on a Mac?
Xor requires at least 70.6 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 Xor locally?
Yes — Xor can run locally on consumer hardware. At BF16 quantization it needs 70.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Xor?
At BF16, Xor can reach ~104 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 70.6 × 0.65 = ~270 tok/s
Estimated speed at BF16 (70.6 GB)
~270 tok/s~270 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 Xor?
At BF16, the download is about 70.21 GB.
- Which GPUs can run Xor?
No single consumer GPU has enough VRAM to run Xor at BF16 (70.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run Xor?
19 devices with unified memory can run Xor at BF16 (70.6 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.