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 Q4_K_M it needs about 1.82 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
- 2025-05-27
- License
- Apache 2.0
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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 |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 1.5 GB | 1.9 GB | 0.79 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 1.6 GB | 2.0 GB | 0.91 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 1.8 GB | 2.2 GB | 1.12 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 2.0 GB | 2.4 GB | 1.33 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 2.2 GB | 2.6 GB | 1.54 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 2.6 GB | 3.0 GB | 1.87 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 4.4 GB | 4.8 GB | 3.74 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 Llm Jp 3.1 1.8B Instruct4?
Q4_K_M · 1.8 GBLlm Jp 3.1 1.8B Instruct4 (Q4_K_M) requires 1.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 4K context window can add up to 0.4 GB, bringing total usage to 2.2 GB. 50 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?
Q4_K_M · 1.8 GB59 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 1.8 GB of VRAM at Q4_K_M, or 4.4 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.9B × 4.8 bits ÷ 8 = 1.1 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
Q4_K_M1.8 GBQ4_K_M + full context2.2 GB- What's the best quantization for Llm Jp 3.1 1.8B Instruct4?
For Llm Jp 3.1 1.8B Instruct4, Q4_K_M (1.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (2.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.5 GB.
VRAM requirement by quantization
Q2_K1.5 GBQ4_K_M ★1.8 GBQ5_K_M2.0 GBQ6_K2.2 GBQ8_02.6 GBBF164.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llm Jp 3.1 1.8B Instruct4 on a Mac?
Llm Jp 3.1 1.8B Instruct4 requires at least 1.5 GB at Q2_K, 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 Q4_K_M quantization it needs 1.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llm Jp 3.1 1.8B Instruct4?
At Q4_K_M, Llm Jp 3.1 1.8B Instruct4 can reach ~2418 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~360 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 ÷ 1.8 × 0.65 = ~2857 tok/s
Estimated speed at Q4_K_M (1.8 GB)
~2857 tok/s~360 tok/s~2857 tok/s~2418 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 Q4_K_M, the download is about 1.12 GB. The full-precision BF16 version is 3.74 GB. The smallest option (Q2_K) is 0.79 GB.
- Which GPUs can run Llm Jp 3.1 1.8B Instruct4?
50 consumer GPUs can run Llm Jp 3.1 1.8B Instruct4 at Q4_K_M (1.8 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llm Jp 3.1 1.8B Instruct4?
59 devices with unified memory can run Llm Jp 3.1 1.8B Instruct4 at Q4_K_M (1.8 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.