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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~360 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~39 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llm Jp 3.1 1.8B Instruct4
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
vllm serve llm-jp/llm-jp-3.1-1.8b-instruct4 --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Other ways
each opens step-by-stepRun in cloud
Fits on RTX 3060 12GB (9 GB headroom) · Q4_K_M
- Generation speed
- ~129 tok/s
- generation speed
- Cost per 1M output tokens
- $0.18
- per 1M output tokens
How Much VRAM Does Llm Jp 3.1 1.8B Instruct4 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 1.5 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~437 tok/s | |
| 1.6 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~407 tok/s | |
| 1.8 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~360 tok/s | |
| 2.0 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~323 tok/s | |
| 2.2 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~293 tok/s | |
| 2.6 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~255 tok/s | |
| 4.4 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~148 tok/s |
Selected Q4_K_M
4-bit medium quantization2.2GBat 4K context
- Weights
- 1.1 GB
- KV cache
- 0.8 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 21.8 GB free · ~360 tok/s
≥50 tok/s needs ≥140 GB/s · RTX 5090 ~640, RTX 3090 Ti ~360
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run Llm Jp 3.1 1.8B Instruct4, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 5.8 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~272Simulate output on RTX 3080RTX 2080 Ti~220Simulate output on RTX 2080 TiRTX 3070 Ti~217Simulate output on RTX 3070 TiM3*8 GB~39Simulate output on iMac 24" M3 (8 GB)Orin Nano~36Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~26Simulate output on iPhone 1720 GB
fits · 17.8 GB free · up to 4K ctx
1 GPU
RX 7900 XT~264Simulate output on RX 7900 XT32–40 GB
fits · 29.8 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~640Simulate output on RTX 5090M5 Max36 GB~177Simulate output on Mac Studio M5 Max (36 GB)M532 GB~59Simulate output on MacBook Air 13" M5 (32 GB)Orin~73Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~48Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
5| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen 1 8BAlibaba | — | 1.9 GB | ~542 | same size |
| Gemma 3n E2B IT Litert LmGoogle | — | 3.1 GB | ~496 | same size |
| DeepSeek R1 Distill Qwen 1.5BDeepSeek | — | 1.8 GB | ~461 | same size |
| MiniCPM 2B Sft BF16OpenBMB | — | 3 GB | ~291 | same size |
| Nemotron Labs Audex 2BNVIDIA | — | 6.1 GB | ~149 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 1.1 GB (at full 4K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run Llm Jp 3.1 1.8B Instruct4 on a Mac?
Yes — Mac Mini M2 (8 GB) and 145 other Macs can run Llm Jp 3.1 1.8B Instruct4. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.8 GB of usable unified memory (RAM minus macOS overhead).
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. 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 ~2637 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 ÷ 1.82 × 0.65 = ~2857 tok/s
Estimated speed at Q4_K_M (1.8 GB)
Real-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?
52 consumer GPUs can run Llm Jp 3.1 1.8B Instruct4 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run Llm Jp 3.1 1.8B Instruct4?
163 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, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.