DeepSeek R1 Distill Qwen 7B Abliterated v2 — Hardware Requirements & GPU Compatibility
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DeepSeek R1 Distill Qwen 7B Abliterated v2 is a 7.6B-parameter open language model from huihui-ai in the DeepSeek R1 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 4.99 GB of VRAM — see which GPUs and Macs can run it below.
591 downloads
51 likesSpecifications
- Publisher
- huihui-ai
- Family
- DeepSeek R1
- Parameters
- 7.6B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-01-31
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How Much VRAM Does DeepSeek R1 Distill Qwen 7B Abliterated v2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 3.6 GB | 11.1 GB | 3.24 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.1 GB | 11.5 GB | 3.71 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.0 GB | 12.4 GB | 4.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 5.8 GB | 13.2 GB | 5.43 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 6.7 GB | 14.1 GB | 6.28 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 8.0 GB | 15.4 GB | 7.62 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 15.7 GB | 23.1 GB | 15.23 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 DeepSeek R1 Distill Qwen 7B Abliterated v2?
Q4_K_M · 5.0 GBDeepSeek R1 Distill Qwen 7B Abliterated v2 (Q4_K_M) requires 5.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 131K context window can add up to 7.4 GB, bringing total usage to 12.4 GB. 50 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 7B Abliterated v2?
Q4_K_M · 5.0 GB59 devices with unified memory can run DeepSeek R1 Distill Qwen 7B Abliterated v2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does DeepSeek R1 Distill Qwen 7B Abliterated v2 need?
DeepSeek R1 Distill Qwen 7B Abliterated v2 requires 5.0 GB of VRAM at Q4_K_M, or 15.7 GB at BF16. Full 131K context adds up to 7.4 GB (12.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.6B × 4.8 bits ÷ 8 = 4.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 7.8 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M5.0 GBQ4_K_M + full context12.4 GB- What's the best quantization for DeepSeek R1 Distill Qwen 7B Abliterated v2?
For DeepSeek R1 Distill Qwen 7B Abliterated v2, Q4_K_M (5.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (5.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3.6 GB.
VRAM requirement by quantization
Q2_K3.6 GBQ4_K_M ★5.0 GBQ5_K_M5.8 GBQ6_K6.7 GBQ8_08.0 GBBF1615.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run DeepSeek R1 Distill Qwen 7B Abliterated v2 on a Mac?
DeepSeek R1 Distill Qwen 7B Abliterated v2 requires at least 3.6 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 DeepSeek R1 Distill Qwen 7B Abliterated v2 locally?
Yes — DeepSeek R1 Distill Qwen 7B Abliterated v2 can run locally on consumer hardware. At Q4_K_M quantization it needs 5.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DeepSeek R1 Distill Qwen 7B Abliterated v2?
At Q4_K_M, DeepSeek R1 Distill Qwen 7B Abliterated v2 can reach ~882 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~131 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 ÷ 5.0 × 0.65 = ~1042 tok/s
Estimated speed at Q4_K_M (5.0 GB)
~1042 tok/s~131 tok/s~1042 tok/s~882 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 7B Abliterated v2?
At Q4_K_M, the download is about 4.57 GB. The full-precision BF16 version is 15.23 GB. The smallest option (Q2_K) is 3.24 GB.
- Which GPUs can run DeepSeek R1 Distill Qwen 7B Abliterated v2?
50 consumer GPUs can run DeepSeek R1 Distill Qwen 7B Abliterated v2 at Q4_K_M (5.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run DeepSeek R1 Distill Qwen 7B Abliterated v2?
59 devices with unified memory can run DeepSeek R1 Distill Qwen 7B Abliterated v2 at Q4_K_M (5.0 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.