Raptor V0.5 8B A1B JANG 6M — Hardware Requirements & GPU Compatibility
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Raptor V0.5 8B A1B JANG 6M is a 6.2B-parameter open language model from OsaurusAI. It supports a context window of up to 131,072 tokens. At BF16 it needs about 12.92 GB of VRAM — see which GPUs and Macs can run it below.
1.1K downloads
8 likesSpecifications
- Publisher
- OsaurusAI
- Parameters
- 6.2B
- Architecture
- BailingMoeV3ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 157,184
- Release Date
- 2026-08-25
- License
- MIT
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HuggingFace
How Much VRAM Does Raptor V0.5 8B A1B JANG 6M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 12.9 GB | 31.9 GB | 12.31 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 Raptor V0.5 8B A1B JANG 6M?
BF16 · 12.9 GBRaptor V0.5 8B A1B JANG 6M (BF16) requires 12.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 17+ GB is recommended. Using the full 131K context window can add up to 19.0 GB, bringing total usage to 31.9 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Raptor V0.5 8B A1B JANG 6M?
BF16 · 12.9 GB48 devices with unified memory can run Raptor V0.5 8B A1B JANG 6M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Raptor V0.5 8B A1B JANG 6M need?
Raptor V0.5 8B A1B JANG 6M requires 12.9 GB of VRAM at BF16. Full 131K context adds up to 19.0 GB (31.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 6.2B × 16 bits ÷ 8 = 12.3 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 19.6 GB (at full 131K context)
VRAM usage by quantization
BF1612.9 GBBF16 + full context31.9 GB- Can I run Raptor V0.5 8B A1B JANG 6M on a Mac?
Raptor V0.5 8B A1B JANG 6M requires at least 12.9 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 Raptor V0.5 8B A1B JANG 6M locally?
Yes — Raptor V0.5 8B A1B JANG 6M can run locally on consumer hardware. At BF16 quantization it needs 12.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Raptor V0.5 8B A1B JANG 6M?
At BF16, Raptor V0.5 8B A1B JANG 6M can reach ~372 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~51 tok/s. 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 ÷ 12.9 × 0.65 = ~403 tok/s
Estimated speed at BF16 (12.9 GB)
~403 tok/s~51 tok/s~403 tok/s~372 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Raptor V0.5 8B A1B JANG 6M?
At BF16, the download is about 12.31 GB.
- Which GPUs can run Raptor V0.5 8B A1B JANG 6M?
26 consumer GPUs can run Raptor V0.5 8B A1B JANG 6M at BF16 (12.9 GB). Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6800. 8 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Raptor V0.5 8B A1B JANG 6M?
49 devices with unified memory can run Raptor V0.5 8B A1B JANG 6M at BF16 (12.9 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.