Rnj 1.5 Instruct — Hardware Requirements & GPU Compatibility
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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.
421 downloads
13 likesSpecifications
- Publisher
- EssentialAI
- Parameters
- 8.3B
- Architecture
- Rnj1ForCausalLM
- Context Length
- 163,840 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2026-05-26
- 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 |
|---|---|---|---|---|---|
| BF16 | 16.00 | 17.2 GB | 38.4 GB | 16.62 GB | Brain floating point 16 — preferred for training |
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. 6 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 GB21 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 headroomRelated 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 ~170 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X → 5300 ÷ 17.2 × 0.55 = ~170 tok/s
Estimated speed at BF16 (17.2 GB)
~170 tok/s~38 tok/s~127 tok/s~105 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?
6 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?
21 devices with unified memory can run Rnj 1.5 Instruct at BF16 (17.2 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.