DeepSeek R1 Distill Qwen 1.5B GGUF — Hardware Requirements & GPU Compatibility
ChatReasoning
A GGUF-quantized version of DeepSeek R1 Distill Qwen 1.5B, repackaged by Unsloth. This model distills the reasoning capabilities of the much larger DeepSeek R1 into a compact 1.5 billion parameter architecture based on Qwen. It is designed specifically for chain-of-thought reasoning tasks, offering surprisingly capable step-by-step problem solving for its size. Despite its small footprint, the R1 distillation process preserves a meaningful share of the original model's logical reasoning ability. It runs easily on low-end hardware and is well suited for users who want to explore reasoning-focused models without dedicating significant GPU memory.
55.2K downloads
133 likesSpecifications
- Publisher
- Unsloth
- Family
- Qwen
- Parameters
- 1.5B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-04-19
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does DeepSeek R1 Distill Qwen 1.5B GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| IQ2_XXS | 2.20 | 0.8 GB | 4.5 GB | 0.41 GB | Importance-weighted 2-bit, extreme compression — significant quality loss |
| IQ2_M | 2.70 | 0.9 GB | 4.6 GB | 0.51 GB | Importance-weighted 2-bit, medium |
| IQ3_XXS | 3.10 | 0.9 GB | 4.6 GB | 0.58 GB | Importance-weighted 3-bit |
| Q2_K | 3.40 | 1 GB | 4.7 GB | 0.64 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 1.1 GB | 4.8 GB | 0.73 GB | 3-bit medium quantization |
| IQ4_XS | 4.30 | 1.2 GB | 4.9 GB | 0.81 GB | Importance-weighted 4-bit, compact |
| Q4_K_M | 4.80 | 1.3 GB | 5.0 GB | 0.90 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.4 GB | 5.1 GB | 1.07 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.6 GB | 5.3 GB | 1.24 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.9 GB | 5.6 GB | 1.50 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run DeepSeek R1 Distill Qwen 1.5B GGUF?
Q4_K_M · 1.3 GBDeepSeek R1 Distill Qwen 1.5B GGUF (Q4_K_M) requires 1.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 131K context window can add up to 3.7 GB, bringing total usage to 5.0 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run DeepSeek R1 Distill Qwen 1.5B GGUF?
Q4_K_M · 1.3 GB33 devices with unified memory can run DeepSeek R1 Distill Qwen 1.5B GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does DeepSeek R1 Distill Qwen 1.5B GGUF need?
DeepSeek R1 Distill Qwen 1.5B GGUF requires 1.3 GB of VRAM at Q4_K_M, or 1.9 GB at Q8_0. Full 131K context adds up to 3.7 GB (5.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.5B × 4.8 bits ÷ 8 = 0.9 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 4.1 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M1.3 GBQ4_K_M + full context5.0 GB- What's the best quantization for DeepSeek R1 Distill Qwen 1.5B GGUF?
For DeepSeek R1 Distill Qwen 1.5B GGUF, Q4_K_M (1.3 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.4 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.8 GB.
VRAM requirement by quantization
IQ2_XXS0.8 GB~53%IQ3_XXS0.9 GB~70%IQ4_XS1.2 GB~87%Q4_K_M ★1.3 GB~89%Q5_K_M1.4 GB~92%Q8_01.9 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek R1 Distill Qwen 1.5B GGUF on a Mac?
DeepSeek R1 Distill Qwen 1.5B GGUF requires at least 0.8 GB at IQ2_XXS, 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 DeepSeek R1 Distill Qwen 1.5B GGUF locally?
Yes — DeepSeek R1 Distill Qwen 1.5B GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 1.3 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DeepSeek R1 Distill Qwen 1.5B GGUF?
At Q4_K_M, DeepSeek R1 Distill Qwen 1.5B GGUF can reach ~2314 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~520 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 ÷ 1.3 × 0.55 = ~2314 tok/s
Estimated speed at Q4_K_M (1.3 GB)
AMD Instinct MI300X~2314 tok/sNVIDIA GeForce RTX 4090~520 tok/sNVIDIA H100 SXM~1729 tok/sAMD Instinct MI250X~1430 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of DeepSeek R1 Distill Qwen 1.5B GGUF?
At Q4_K_M, the download is about 0.90 GB. The full-precision Q8_0 version is 1.50 GB. The smallest option (IQ2_XXS) is 0.41 GB.