Raptor 0.6 4B JANG 6M — Hardware Requirements & GPU Compatibility
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Raptor 0.6 4B JANG 6M is a 4.1B-parameter open language model from OsaurusAI. It supports a context window of up to 1,048,576 tokens. At BF16 it needs about 8.71 GB of VRAM — see which GPUs and Macs can run it below.
1.2K downloads
2 likesSpecifications
- Publisher
- OsaurusAI
- Parameters
- 4.1B
- Architecture
- Spark2_5ForCausalLM
- Context Length
- 1,048,576 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-09-10
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Raptor 0.6 4B JANG 6M Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 8.7 GB | 105.2 GB | 8.22 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 0.6 4B JANG 6M?
BF16 · 8.7 GBRaptor 0.6 4B JANG 6M (BF16) requires 8.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 1049K context window can add up to 96.4 GB, bringing total usage to 105.2 GB. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Raptor 0.6 4B JANG 6M?
BF16 · 8.7 GB49 devices with unified memory can run Raptor 0.6 4B JANG 6M, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Raptor 0.6 4B JANG 6M need?
Raptor 0.6 4B JANG 6M requires 8.7 GB of VRAM at BF16. Full 1049K context adds up to 96.4 GB (105.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.1B × 16 bits ÷ 8 = 8.2 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 97 GB (at full 1049K context)
VRAM usage by quantization
BF168.7 GBBF16 + full context105.2 GB- Can I run Raptor 0.6 4B JANG 6M on a Mac?
Raptor 0.6 4B JANG 6M requires at least 8.7 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 0.6 4B JANG 6M locally?
Yes — Raptor 0.6 4B JANG 6M can run locally on consumer hardware. At BF16 quantization it needs 8.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Raptor 0.6 4B JANG 6M?
At BF16, Raptor 0.6 4B JANG 6M can reach ~551 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~75 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 ÷ 8.7 × 0.65 = ~597 tok/s
Estimated speed at BF16 (8.7 GB)
~597 tok/s~75 tok/s~597 tok/s~551 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Raptor 0.6 4B JANG 6M?
At BF16, the download is about 8.22 GB.
- Which GPUs can run Raptor 0.6 4B JANG 6M?
40 consumer GPUs can run Raptor 0.6 4B JANG 6M at BF16 (8.7 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Raptor 0.6 4B JANG 6M?
52 devices with unified memory can run Raptor 0.6 4B JANG 6M at BF16 (8.7 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.