Llm Jp 4.1 8B Thinking — Hardware Requirements & GPU Compatibility
Chat
llm-jp-4.1-8b-thinking is an 8.6-billion-parameter dense reasoning model from the Research and Development Center for Large Language Models at Japan's National Institute of Informatics. It has 32 layers, was pre-trained and mid-trained on a multi-stage pipeline, and was then aligned with supervised fine-tuning and direct preference optimization without reinforcement learning. It targets Japanese and English, and the card notes code data in several programming languages. At this size it fits on a single consumer GPU once quantized. The model supports a 65,536 token context window. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use, and was published in September 2026. It is the smallest model in the LLM-jp-4.1 thinking series, alongside 32B-A3B and 33B siblings, and an update to the earlier llm-jp-4-8b-thinking.
830 downloads
10 likesSpecifications
- Publisher
- llm-jp
- Parameters
- 8.6B
- Architecture
- LlamaForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 196,608
- Release Date
- 2026-09-15
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Llm Jp 4.1 8B Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.2 GB | 12.5 GB | 3.65 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.8 GB | 13.1 GB | 4.19 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 5.7 GB | 14.0 GB | 5.15 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 6.7 GB | 15.0 GB | 6.12 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 7.7 GB | 16.0 GB | 7.09 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 9.2 GB | 17.5 GB | 8.59 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 17.8 GB | 26.1 GB | 17.18 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 4.1 8B Thinking?
Q4_K_M · 5.7 GBLlm Jp 4.1 8B Thinking (Q4_K_M) requires 5.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 66K context window can add up to 8.3 GB, bringing total usage to 14.0 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Llm Jp 4.1 8B Thinking?
Q4_K_M · 5.7 GB58 devices with unified memory can run Llm Jp 4.1 8B Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomWhere to Download Llm Jp 4.1 8B Thinking
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Frequently Asked Questions
- How much VRAM does Llm Jp 4.1 8B Thinking need?
Llm Jp 4.1 8B Thinking requires 5.7 GB of VRAM at Q4_K_M, or 17.8 GB at BF16. Full 66K context adds up to 8.3 GB (14.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.6B × 4.8 bits ÷ 8 = 5.2 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 8.8 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M5.7 GBQ4_K_M + full context14.0 GB- What's the best quantization for Llm Jp 4.1 8B Thinking?
For Llm Jp 4.1 8B Thinking, Q4_K_M (5.7 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.7 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.2 GB.
VRAM requirement by quantization
Q2_K4.2 GBQ4_K_M ★5.7 GBQ5_K_M6.7 GBQ6_K7.7 GBQ8_09.2 GBBF1617.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llm Jp 4.1 8B Thinking on a Mac?
Llm Jp 4.1 8B Thinking requires at least 4.2 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 4.1 8B Thinking locally?
Yes — Llm Jp 4.1 8B Thinking can run locally on consumer hardware. At Q4_K_M quantization it needs 5.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llm Jp 4.1 8B Thinking?
At Q4_K_M, Llm Jp 4.1 8B Thinking can reach ~839 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~115 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 ÷ 5.7 × 0.65 = ~909 tok/s
Estimated speed at Q4_K_M (5.7 GB)
~909 tok/s~115 tok/s~909 tok/s~839 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 4.1 8B Thinking?
At Q4_K_M, the download is about 5.15 GB. The full-precision BF16 version is 17.18 GB. The smallest option (Q2_K) is 3.65 GB.
- Which GPUs can run Llm Jp 4.1 8B Thinking?
52 consumer GPUs can run Llm Jp 4.1 8B Thinking at Q4_K_M (5.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Llm Jp 4.1 8B Thinking?
59 devices with unified memory can run Llm Jp 4.1 8B Thinking at Q4_K_M (5.7 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.