MGPT 1.3B Ossetian — Hardware Requirements & GPU Compatibility
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29 downloads
3 likesSpecifications
- Publisher
- ai-forever
- Parameters
- 1.3B
- Architecture
- GPT2LMHeadModel
- Context Length
- 2,048 tokens
- Vocabulary Size
- 100,000
- Release Date
- 2023-08-11
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does MGPT 1.3B Ossetian Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 2.9 GB | — | 2.60 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run MGPT 1.3B Ossetian?
BF16 · 2.9 GBMGPT 1.3B Ossetian (BF16) requires 2.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run MGPT 1.3B Ossetian?
BF16 · 2.9 GB33 devices with unified memory can run MGPT 1.3B Ossetian, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does MGPT 1.3B Ossetian need?
MGPT 1.3B Ossetian requires 2.9 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.3B × 16 bits ÷ 8 = 2.6 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF162.9 GB- Can I run MGPT 1.3B Ossetian on a Mac?
MGPT 1.3B Ossetian requires at least 2.9 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 MGPT 1.3B Ossetian locally?
Yes — MGPT 1.3B Ossetian can run locally on consumer hardware. At BF16 quantization it needs 2.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MGPT 1.3B Ossetian?
At BF16, MGPT 1.3B Ossetian can reach ~1019 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~229 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 ÷ 2.9 × 0.55 = ~1019 tok/s
Estimated speed at BF16 (2.9 GB)
AMD Instinct MI300X~1019 tok/sNVIDIA GeForce RTX 4090~229 tok/sNVIDIA H100 SXM~762 tok/sAMD Instinct MI250X~630 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MGPT 1.3B Ossetian?
At BF16, the download is about 2.60 GB.
- Which GPUs can run MGPT 1.3B Ossetian?
35 consumer GPUs can run MGPT 1.3B Ossetian at BF16 (2.9 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 MGPT 1.3B Ossetian?
33 devices with unified memory can run MGPT 1.3B Ossetian at BF16 (2.9 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.