Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF — Hardware Requirements & GPU Compatibility
ChatReasoning
A GGUF-quantized distillation of Qwen3 14B trained on reasoning traces from Claude 4.5 Opus by TeichAI. At 14 billion parameters, this model sits in a sweet spot for users with mid-range GPUs who want improved reasoning without the memory demands of larger models. The distillation process targets high-quality chain-of-thought and analytical outputs. The smaller parameter count compared to 27B or 70B alternatives means faster inference and lower VRAM requirements, making it accessible on GPUs with 12 to 16 GB of memory at common quantization levels. A good option for users who need capable reasoning on a budget and are willing to trade some depth for speed and efficiency.
105.9K downloads
291 likesSpecifications
- Publisher
- TeichAI
- Family
- Qwen
- Parameters
- 14B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2026-02-22
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_S | 3.50 | 6.8 GB | 13.1 GB | 6.13 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 7.5 GB | 13.8 GB | 6.83 GB | 3-bit medium quantization |
| IQ4_NL | 4.50 | 8.5 GB | 14.9 GB | 7.88 GB | Importance-weighted 4-bit, non-linear |
| Q4_K_M | 4.80 | 9.0 GB | 15.4 GB | 8.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q8_0 | 8.00 | 14.6 GB | 21.0 GB | 14.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF?
Q4_K_M · 9.0 GBQwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF (Q4_K_M) requires 9.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 41K context window can add up to 6.4 GB, bringing total usage to 15.4 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 Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF?
Q4_K_M · 9.0 GB27 devices with unified memory can run Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF need?
Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF requires 9.0 GB of VRAM at Q4_K_M, or 14.6 GB at Q8_0. Full 41K context adds up to 6.4 GB (15.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 14B × 4.8 bits ÷ 8 = 8.4 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M9.0 GBQ4_K_M + full context15.4 GB- What's the best quantization for Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF?
For Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF, Q4_K_M (9.0 GB) offers the best balance of quality and VRAM usage. Q8_0 (14.6 GB) provides better quality if you have the VRAM. The smallest option is Q3_K_S at 6.8 GB.
VRAM requirement by quantization
Q3_K_S6.8 GB~77%Q3_K_M7.5 GB~83%IQ4_NL8.5 GB~88%Q4_K_M ★9.0 GB~89%Q8_014.6 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF on a Mac?
Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF requires at least 6.8 GB at Q3_K_S, 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 Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF locally?
Yes — Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 9.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF?
At Q4_K_M, Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF can reach ~323 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~73 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 ÷ 9.0 × 0.55 = ~323 tok/s
Estimated speed at Q4_K_M (9.0 GB)
AMD Instinct MI300X~323 tok/sNVIDIA GeForce RTX 4090~73 tok/sNVIDIA H100 SXM~241 tok/sAMD Instinct MI250X~199 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3 14B Claude 4.5 Opus High Reasoning Distill GGUF?
At Q4_K_M, the download is about 8.40 GB. The full-precision Q8_0 version is 14.00 GB. The smallest option (Q3_K_S) is 6.13 GB.