Josiefied Qwen3 8B Abliterated V1 — Hardware Requirements & GPU Compatibility
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Josiefied Qwen3 8B Abliterated V1 is a 8.2B-parameter open language model from Goekdeniz-Guelmez in the Qwen 3 family. It supports a context window of up to 40,960 tokens. At Q4_K_M it needs about 5.52 GB of VRAM — see which GPUs and Macs can run it below.
4.0K downloads
205 likesSpecifications
- Publisher
- Goekdeniz-Guelmez
- Family
- Qwen 3
- Parameters
- 8.2B
- Architecture
- Qwen3ForCausalLM
- Context Length
- 40,960 tokens
- Vocabulary Size
- 151,936
- Release Date
- 2025-04-29
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How Much VRAM Does Josiefied Qwen3 8B Abliterated V1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.1 GB | 9.8 GB | 3.48 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 4.6 GB | 10.3 GB | 3.99 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 5.5 GB | 11.3 GB | 4.91 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 6.4 GB | 12.2 GB | 5.84 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 7.4 GB | 13.1 GB | 6.76 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 8.8 GB | 14.5 GB | 8.19 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 17.0 GB | 22.7 GB | 16.38 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 Josiefied Qwen3 8B Abliterated V1?
Q4_K_M · 5.5 GBJosiefied Qwen3 8B Abliterated V1 (Q4_K_M) requires 5.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 41K context window can add up to 5.7 GB, bringing total usage to 11.3 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Josiefied Qwen3 8B Abliterated V1?
Q4_K_M · 5.5 GB58 devices with unified memory can run Josiefied Qwen3 8B Abliterated V1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Josiefied Qwen3 8B Abliterated V1 need?
Josiefied Qwen3 8B Abliterated V1 requires 5.5 GB of VRAM at Q4_K_M, or 17.0 GB at BF16. Full 41K context adds up to 5.7 GB (11.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 8.2B × 4.8 bits ÷ 8 = 4.9 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.4 GB (at full 41K context)
VRAM usage by quantization
Q4_K_M5.5 GBQ4_K_M + full context11.3 GB- What's the best quantization for Josiefied Qwen3 8B Abliterated V1?
For Josiefied Qwen3 8B Abliterated V1, Q4_K_M (5.5 GB) offers the best balance of quality and VRAM usage. Q5_K_M (6.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.1 GB.
VRAM requirement by quantization
Q2_K4.1 GBQ4_K_M ★5.5 GBQ5_K_M6.4 GBQ6_K7.4 GBQ8_08.8 GBBF1617.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Josiefied Qwen3 8B Abliterated V1 on a Mac?
Josiefied Qwen3 8B Abliterated V1 requires at least 4.1 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 Josiefied Qwen3 8B Abliterated V1 locally?
Yes — Josiefied Qwen3 8B Abliterated V1 can run locally on consumer hardware. At Q4_K_M quantization it needs 5.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Josiefied Qwen3 8B Abliterated V1?
At Q4_K_M, Josiefied Qwen3 8B Abliterated V1 can reach ~797 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~119 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 ÷ 5.5 × 0.65 = ~942 tok/s
Estimated speed at Q4_K_M (5.5 GB)
~942 tok/s~119 tok/s~942 tok/s~797 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Josiefied Qwen3 8B Abliterated V1?
At Q4_K_M, the download is about 4.91 GB. The full-precision BF16 version is 16.38 GB. The smallest option (Q2_K) is 3.48 GB.
- Which GPUs can run Josiefied Qwen3 8B Abliterated V1?
50 consumer GPUs can run Josiefied Qwen3 8B Abliterated V1 at Q4_K_M (5.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Josiefied Qwen3 8B Abliterated V1?
59 devices with unified memory can run Josiefied Qwen3 8B Abliterated V1 at Q4_K_M (5.5 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.