Rnj 1.5 Instruct — Hardware Requirements & GPU Compatibility
Chat
Rnj 1.5 Instruct is a 8.3B-parameter open language model from EssentialAI. It supports a context window of up to 163,840 tokens. At BF16 it needs about 17.19 GB of VRAM — see which GPUs and Macs can run it below.
303 downloads
15 likesSpecifications
- Publisher
- EssentialAI
- Parameters
- 8.3B
- Architecture
- Rnj1ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-04-22
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Rnj 1.5 Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 17.2 GB | 38.4 GB | 16.62 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 Rnj 1.5 Instruct?
BF16 · 17.2 GBRnj 1.5 Instruct (BF16) requires 17.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 164K context window can add up to 21.2 GB, bringing total usage to 38.4 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Rnj 1.5 Instruct?
BF16 · 17.2 GB41 devices with unified memory can run Rnj 1.5 Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Rnj 1.5 Instruct need?
Rnj 1.5 Instruct requires 17.2 GB of VRAM at BF16. Full 164K context adds up to 21.2 GB (38.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.3B × 16 bits ÷ 8 = 16.6 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 21.8 GB (at full 164K context)
VRAM usage by quantization
BF1617.2 GBBF16 + full context38.4 GB- Can I run Rnj 1.5 Instruct on a Mac?
Rnj 1.5 Instruct requires at least 17.2 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 Rnj 1.5 Instruct locally?
Yes — Rnj 1.5 Instruct can run locally on consumer hardware. At BF16 quantization it needs 17.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Rnj 1.5 Instruct?
At BF16, Rnj 1.5 Instruct can reach ~256 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~38 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 ÷ 17.2 × 0.65 = ~303 tok/s
Estimated speed at BF16 (17.2 GB)
~303 tok/s~38 tok/s~303 tok/s~256 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Rnj 1.5 Instruct?
At BF16, the download is about 16.62 GB.
- Which GPUs can run Rnj 1.5 Instruct?
8 consumer GPUs can run Rnj 1.5 Instruct at BF16 (17.2 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 Rnj 1.5 Instruct?
41 devices with unified memory can run Rnj 1.5 Instruct at BF16 (17.2 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.