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 BF16 it needs about 29.39 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
- 2025-05-30
- License
- Apache 2.0
Get Started
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 |
|---|---|---|---|---|---|
| BF16 | 16.00 | 29.4 GB | 31.1 GB | 27.42 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Llm Jp 3.1 13B Instruct4?
BF16 · 29.4 GBLlm Jp 3.1 13B Instruct4 (BF16) requires 29.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 39+ GB is recommended. Using the full 4K context window can add up to 1.7 GB, bringing total usage to 31.1 GB. 1 GPU can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Decent
— Enough VRAM, may be tightWhich Devices Can Run Llm Jp 3.1 13B Instruct4?
BF16 · 29.4 GB15 devices with unified memory can run Llm Jp 3.1 13B Instruct4, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (36 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Llm Jp 3.1 13B Instruct4 need?
Llm Jp 3.1 13B Instruct4 requires 29.4 GB of VRAM at BF16. Full 4K context adds up to 1.7 GB (31.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 13.7B × 16 bits ÷ 8 = 27.4 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
BF1629.4 GBBF16 + full context31.1 GB- Can I run Llm Jp 3.1 13B Instruct4 on a Mac?
Llm Jp 3.1 13B Instruct4 requires at least 29.4 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 13B Instruct4 locally?
Yes — Llm Jp 3.1 13B Instruct4 can run locally on consumer hardware. At BF16 quantization it needs 29.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llm Jp 3.1 13B Instruct4?
At BF16, Llm Jp 3.1 13B Instruct4 can reach ~99 tok/s on AMD Instinct MI300X. 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 ÷ 29.4 × 0.55 = ~99 tok/s
Estimated speed at BF16 (29.4 GB)
~99 tok/s~74 tok/s~61 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 BF16, the download is about 27.42 GB.
- Which GPUs can run Llm Jp 3.1 13B Instruct4?
1 consumer GPU can run Llm Jp 3.1 13B Instruct4 at BF16 (29.4 GB). Top options include NVIDIA GeForce RTX 5090.
- Which devices can run Llm Jp 3.1 13B Instruct4?
15 devices with unified memory can run Llm Jp 3.1 13B Instruct4 at BF16 (29.4 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.