Llm Jp 3.1 1.8B Instruct4 — Hardware Requirements & GPU Compatibility
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Llm Jp 3.1 1.8B Instruct4 is a 1.9B-parameter open language model from llm-jp. It supports a context window of up to 4,096 tokens. At BF16 it needs about 4.44 GB of VRAM — see which GPUs and Macs can run it below.
7.0K downloads
18 likesSpecifications
- Publisher
- llm-jp
- Parameters
- 1.9B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 99,584
- Release Date
- 2025-05-30
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Llm Jp 3.1 1.8B Instruct4 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 4.4 GB | 4.8 GB | 3.74 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Llm Jp 3.1 1.8B Instruct4?
BF16 · 4.4 GBLlm Jp 3.1 1.8B Instruct4 (BF16) requires 4.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. Using the full 4K context window can add up to 0.4 GB, bringing total usage to 4.8 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Llm Jp 3.1 1.8B Instruct4?
BF16 · 4.4 GB33 devices with unified memory can run Llm Jp 3.1 1.8B Instruct4, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Llm Jp 3.1 1.8B Instruct4 need?
Llm Jp 3.1 1.8B Instruct4 requires 4.4 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.9B × 16 bits ÷ 8 = 3.7 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 1.1 GB (at full 4K context)
VRAM usage by quantization
BF164.4 GBBF16 + full context4.8 GB- Can I run Llm Jp 3.1 1.8B Instruct4 on a Mac?
Llm Jp 3.1 1.8B Instruct4 requires at least 4.4 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 Llm Jp 3.1 1.8B Instruct4 locally?
Yes — Llm Jp 3.1 1.8B Instruct4 can run locally on consumer hardware. At BF16 quantization it needs 4.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llm Jp 3.1 1.8B Instruct4?
At BF16, Llm Jp 3.1 1.8B Instruct4 can reach ~657 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~148 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 ÷ 4.4 × 0.55 = ~657 tok/s
Estimated speed at BF16 (4.4 GB)
~657 tok/s~148 tok/s~491 tok/s~406 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Llm Jp 3.1 1.8B Instruct4?
At BF16, the download is about 3.74 GB.
- Which GPUs can run Llm Jp 3.1 1.8B Instruct4?
35 consumer GPUs can run Llm Jp 3.1 1.8B Instruct4 at BF16 (4.4 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 Llm Jp 3.1 1.8B Instruct4?
33 devices with unified memory can run Llm Jp 3.1 1.8B Instruct4 at BF16 (4.4 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.