Schematron 8B — Hardware Requirements & GPU Compatibility
Chat
1.4K downloads
31 likesSpecifications
- Publisher
- inference-net
- Parameters
- 8B
- Architecture
- LlamaForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-09-12
Get Started
HuggingFace
How Much VRAM Does Schematron 8B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 16.6 GB | 33.5 GB | 16.00 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Schematron 8B?
BF16 · 16.6 GBSchematron 8B (BF16) requires 16.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 22+ GB is recommended. Using the full 131K context window can add up to 16.9 GB, bringing total usage to 33.5 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Schematron 8B?
BF16 · 16.6 GB21 devices with unified memory can run Schematron 8B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Schematron 8B need?
Schematron 8B requires 16.6 GB of VRAM at BF16. Full 131K context adds up to 16.9 GB (33.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8B × 16 bits ÷ 8 = 16 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 131K context)
VRAM usage by quantization
BF1616.6 GBBF16 + full context33.5 GB- Can I run Schematron 8B on a Mac?
Schematron 8B requires at least 16.6 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 Schematron 8B locally?
Yes — Schematron 8B can run locally on consumer hardware. At BF16 quantization it needs 16.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Schematron 8B?
At BF16, Schematron 8B can reach ~176 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~40 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 ÷ 16.6 × 0.55 = ~176 tok/s
Estimated speed at BF16 (16.6 GB)
AMD Instinct MI300X~176 tok/sNVIDIA GeForce RTX 4090~40 tok/sNVIDIA H100 SXM~132 tok/sAMD Instinct MI250X~109 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Schematron 8B?
At BF16, the download is about 16.00 GB.
- Which GPUs can run Schematron 8B?
6 consumer GPUs can run Schematron 8B at BF16 (16.6 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Schematron 8B?
21 devices with unified memory can run Schematron 8B at BF16 (16.6 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.