Llm Jp 3.1 13B Instruct4 — Hardware Requirements & GPU Compatibility
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Llm Jp 3.1 13B Instruct4 is a 13.7B-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 10.20 GB of VRAM — see which GPUs and Macs can run it below.
30.3K downloads
19 likesSpecifications
- Publisher
- llm-jp
- Parameters
- 13.7B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 99,584
- Release Date
- 2025-05-23
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Llm Jp 3.1 13B Instruct4 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 7.8 GB | 9.5 GB | 5.83 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 8.7 GB | 10.3 GB | 6.68 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 10.2 GB | 11.9 GB | 8.22 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 11.7 GB | 13.4 GB | 9.77 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 13.3 GB | 15.0 GB | 11.31 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 15.7 GB | 17.4 GB | 13.71 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 29.4 GB | 31.1 GB | 27.42 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 13B Instruct4?
Q4_K_M · 10.2 GBLlm Jp 3.1 13B Instruct4 (Q4_K_M) requires 10.2 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 14+ GB is recommended. Using the full 4K context window can add up to 1.7 GB, bringing total usage to 11.9 GB. 37 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Llm Jp 3.1 13B Instruct4?
Q4_K_M · 10.2 GB48 devices with unified memory can run Llm Jp 3.1 13B Instruct4, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, NVIDIA Jetson Orin NX 16GB.
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightFrequently Asked Questions
- How much VRAM does Llm Jp 3.1 13B Instruct4 need?
Llm Jp 3.1 13B Instruct4 requires 10.2 GB of VRAM at Q4_K_M, or 29.4 GB at BF16. Full 4K context adds up to 1.7 GB (11.9 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 13.7B × 4.8 bits ÷ 8 = 8.2 GB
KV Cache + Overhead ≈ 2 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 3.7 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M10.2 GBQ4_K_M + full context11.9 GB- Can NVIDIA GeForce RTX 4090 run Llm Jp 3.1 13B Instruct4?
Yes, at Q8_0 (15.7 GB) or lower. Higher quantizations like BF16 (29.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Llm Jp 3.1 13B Instruct4?
For Llm Jp 3.1 13B Instruct4, Q4_K_M (10.2 GB) offers the best balance of quality and VRAM usage. Q5_K_M (11.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 7.8 GB.
VRAM requirement by quantization
Q2_K7.8 GBQ4_K_M ★10.2 GBQ5_K_M11.7 GBQ6_K13.3 GBQ8_015.7 GBBF1629.4 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llm Jp 3.1 13B Instruct4 on a Mac?
Llm Jp 3.1 13B Instruct4 requires at least 7.8 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 13B Instruct4 locally?
Yes — Llm Jp 3.1 13B Instruct4 can run locally on consumer hardware. At Q4_K_M quantization it needs 10.2 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llm Jp 3.1 13B Instruct4?
At Q4_K_M, Llm Jp 3.1 13B Instruct4 can reach ~431 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~64 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 ÷ 10.2 × 0.65 = ~510 tok/s
Estimated speed at Q4_K_M (10.2 GB)
~510 tok/s~64 tok/s~510 tok/s~431 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 13B Instruct4?
At Q4_K_M, the download is about 8.22 GB. The full-precision BF16 version is 27.42 GB. The smallest option (Q2_K) is 5.83 GB.
- Which GPUs can run Llm Jp 3.1 13B Instruct4?
37 consumer GPUs can run Llm Jp 3.1 13B Instruct4 at Q4_K_M (10.2 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llm Jp 3.1 13B Instruct4?
52 devices with unified memory can run Llm Jp 3.1 13B Instruct4 at Q4_K_M (10.2 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.