14B Qwen2.5 Kunou V1 — Hardware Requirements & GPU Compatibility
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68 downloads
31 likesSpecifications
- Publisher
- Sao10K
- Family
- Qwen 2.5
- Parameters
- 14.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2024-12-15
- License
- Other
Get Started
HuggingFace
How Much VRAM Does 14B Qwen2.5 Kunou V1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_0 | 4.00 | 8.1 GB | 33.5 GB | 7.39 GB | 4-bit legacy quantization |
| Q4_1 | 4.50 | 9.0 GB | 34.4 GB | 8.31 GB | 4-bit legacy quantization with offset |
| Q5_0 | 5.00 | 9.9 GB | 35.3 GB | 9.23 GB | 5-bit legacy quantization |
| Q5_1 | 5.50 | 10.9 GB | 36.2 GB | 10.15 GB | 5-bit legacy quantization with offset |
| Q8_0 | 8.00 | 15.5 GB | 40.8 GB | 14.77 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run 14B Qwen2.5 Kunou V1?
Q4_0 · 8.1 GB14B Qwen2.5 Kunou V1 (Q4_0) requires 8.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. Using the full 131K context window can add up to 25.4 GB, bringing total usage to 33.5 GB. 28 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 14B Qwen2.5 Kunou V1?
Q4_0 · 8.1 GB27 devices with unified memory can run 14B Qwen2.5 Kunou V1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does 14B Qwen2.5 Kunou V1 need?
14B Qwen2.5 Kunou V1 requires 8.1 GB of VRAM at Q4_0, or 15.5 GB at Q8_0. Full 131K context adds up to 25.4 GB (33.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14.8B × 4 bits ÷ 8 = 7.4 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 26.1 GB (at full 131K context)
VRAM usage by quantization
Q4_08.1 GBQ4_0 + full context33.5 GB- What's the best quantization for 14B Qwen2.5 Kunou V1?
For 14B Qwen2.5 Kunou V1, Q5_0 (9.9 GB) offers the best balance of quality and VRAM usage. Q5_1 (10.9 GB) provides better quality if you have the VRAM. The smallest option is Q4_0 at 8.1 GB.
VRAM requirement by quantization
Q4_08.1 GB~85%Q4_19.0 GB~88%Q5_0 ★9.9 GB~90%Q5_110.9 GB~92%Q8_015.5 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run 14B Qwen2.5 Kunou V1 on a Mac?
14B Qwen2.5 Kunou V1 requires at least 8.1 GB at Q4_0, 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 14B Qwen2.5 Kunou V1 locally?
Yes — 14B Qwen2.5 Kunou V1 can run locally on consumer hardware. At Q4_0 quantization it needs 8.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is 14B Qwen2.5 Kunou V1?
At Q4_0, 14B Qwen2.5 Kunou V1 can reach ~360 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~81 tok/s. 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 ÷ 8.1 × 0.55 = ~360 tok/s
Estimated speed at Q4_0 (8.1 GB)
AMD Instinct MI300X~360 tok/sNVIDIA GeForce RTX 4090~81 tok/sNVIDIA H100 SXM~269 tok/sAMD Instinct MI250X~223 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of 14B Qwen2.5 Kunou V1?
At Q4_0, the download is about 7.39 GB. The full-precision Q8_0 version is 14.77 GB.
- Which GPUs can run 14B Qwen2.5 Kunou V1?
28 consumer GPUs can run 14B Qwen2.5 Kunou V1 at Q4_0 (8.1 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run 14B Qwen2.5 Kunou V1?
27 devices with unified memory can run 14B Qwen2.5 Kunou V1 at Q4_0 (8.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.