Llm Jp 4 33B Thinking — Hardware Requirements & GPU Compatibility
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Llm Jp 4 33B Thinking is a 33.2B-parameter open language model from llm-jp. It supports a context window of up to 65,536 tokens. At Q4_K_M it needs about 20.77 GB of VRAM — see which GPUs and Macs can run it below.
17.1K downloads
42 likesSpecifications
- Publisher
- llm-jp
- Parameters
- 33.2B
- Architecture
- LlamaForCausalLM
- Context Length
- 65,536 tokens
- Vocabulary Size
- 196,608
- Release Date
- 2026-08-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Llm Jp 4 33B Thinking Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 15.0 GB | 31.6 GB | 14.12 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 17.0 GB | 33.7 GB | 16.19 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 20.8 GB | 37.4 GB | 19.93 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 24.5 GB | 41.1 GB | 23.67 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 28.2 GB | 44.9 GB | 27.41 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 34.1 GB | 50.7 GB | 33.22 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 67.3 GB | 83.9 GB | 66.44 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 33B Thinking?
Q4_K_M · 20.8 GBLlm Jp 4 33B Thinking (Q4_K_M) requires 20.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 28+ GB is recommended. Using the full 66K context window can add up to 16.6 GB, bringing total usage to 37.4 GB. 7 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Llm Jp 4 33B Thinking?
Q4_K_M · 20.8 GB41 devices with unified memory can run Llm Jp 4 33B Thinking, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightFrequently Asked Questions
- How much VRAM does Llm Jp 4 33B Thinking need?
Llm Jp 4 33B Thinking requires 20.8 GB of VRAM at Q4_K_M, or 67.3 GB at BF16. Full 66K context adds up to 16.6 GB (37.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 33.2B × 4.8 bits ÷ 8 = 19.9 GB
KV Cache + Overhead ≈ 0.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.5 GB (at full 66K context)
VRAM usage by quantization
Q4_K_M20.8 GBQ4_K_M + full context37.4 GB- Can NVIDIA GeForce RTX 4090 run Llm Jp 4 33B Thinking?
Yes, at Q4_K_M (20.8 GB) or lower. Higher quantizations like Q5_K_M (24.5 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Llm Jp 4 33B Thinking?
For Llm Jp 4 33B Thinking, Q4_K_M (20.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (24.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 15.0 GB.
VRAM requirement by quantization
Q2_K15.0 GBQ4_K_M ★20.8 GBQ5_K_M24.5 GBQ6_K28.2 GBQ8_034.1 GBBF1667.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Llm Jp 4 33B Thinking on a Mac?
Llm Jp 4 33B Thinking requires at least 15.0 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 33B Thinking locally?
Yes — Llm Jp 4 33B Thinking can run locally on consumer hardware. At Q4_K_M quantization it needs 20.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llm Jp 4 33B Thinking?
At Q4_K_M, Llm Jp 4 33B Thinking can reach ~231 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~32 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 ÷ 20.8 × 0.65 = ~250 tok/s
Estimated speed at Q4_K_M (20.8 GB)
~250 tok/s~32 tok/s~250 tok/s~231 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 33B Thinking?
At Q4_K_M, the download is about 19.93 GB. The full-precision BF16 version is 66.44 GB. The smallest option (Q2_K) is 14.12 GB.
- Which GPUs can run Llm Jp 4 33B Thinking?
7 consumer GPUs can run Llm Jp 4 33B Thinking at Q4_K_M (20.8 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Llm Jp 4 33B Thinking?
41 devices with unified memory can run Llm Jp 4 33B Thinking at Q4_K_M (20.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.