Josiefied Qwen3.5 0.8B Gabliterated V1 — Hardware Requirements & GPU Compatibility
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Josiefied Qwen3.5 0.8B Gabliterated V1 is a 853M-parameter open language model from Goekdeniz-Guelmez in the Qwen family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 0.86 GB of VRAM — see which GPUs and Macs can run it below.
619 downloads
4 likesSpecifications
- Publisher
- Goekdeniz-Guelmez
- Family
- Qwen
- Parameters
- 853M
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-13
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How Much VRAM Does Josiefied Qwen3.5 0.8B Gabliterated V1 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.7 GB | 7.1 GB | 0.36 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.7 GB | 7.1 GB | 0.37 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.8 GB | 7.2 GB | 0.42 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 0.8 GB | 7.2 GB | 0.44 GB | 3-bit large quantization |
| Q4_K_S | 4.50 | 0.8 GB | 7.2 GB | 0.48 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 0.9 GB | 7.3 GB | 0.51 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 0.9 GB | 7.3 GB | 0.59 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 1.0 GB | 7.3 GB | 0.61 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.1 GB | 7.5 GB | 0.70 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.2 GB | 7.6 GB | 0.85 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Josiefied Qwen3.5 0.8B Gabliterated V1?
Q4_K_M · 0.9 GBJosiefied Qwen3.5 0.8B Gabliterated V1 (Q4_K_M) requires 0.9 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 262K context window can add up to 6.4 GB, bringing total usage to 7.3 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Josiefied Qwen3.5 0.8B Gabliterated V1?
Q4_K_M · 0.9 GB33 devices with unified memory can run Josiefied Qwen3.5 0.8B Gabliterated V1, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Josiefied Qwen3.5 0.8B Gabliterated V1 need?
Josiefied Qwen3.5 0.8B Gabliterated V1 requires 0.9 GB of VRAM at Q4_K_M, or 1.2 GB at Q8_0. Full 262K context adds up to 6.4 GB (7.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 853M × 4.8 bits ÷ 8 = 0.5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.8 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M0.9 GBQ4_K_M + full context7.3 GB- What's the best quantization for Josiefied Qwen3.5 0.8B Gabliterated V1?
For Josiefied Qwen3.5 0.8B Gabliterated V1, Q4_K_M (0.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.9 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.7 GB.
VRAM requirement by quantization
Q2_K0.7 GBQ3_K_M0.8 GBQ4_K_M ★0.9 GBQ5_K_S0.9 GBQ5_K_M1.0 GBQ8_01.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Josiefied Qwen3.5 0.8B Gabliterated V1 on a Mac?
Josiefied Qwen3.5 0.8B Gabliterated V1 requires at least 0.7 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 Qwen3.5 0.8B Gabliterated V1 locally?
Yes — Josiefied Qwen3.5 0.8B Gabliterated V1 can run locally on consumer hardware. At Q4_K_M quantization it needs 0.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Josiefied Qwen3.5 0.8B Gabliterated V1?
At Q4_K_M, Josiefied Qwen3.5 0.8B Gabliterated V1 can reach ~3390 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~762 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 ÷ 0.9 × 0.55 = ~3390 tok/s
Estimated speed at Q4_K_M (0.9 GB)
~3390 tok/s~762 tok/s~2534 tok/s~2096 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Josiefied Qwen3.5 0.8B Gabliterated V1?
At Q4_K_M, the download is about 0.51 GB. The full-precision Q8_0 version is 0.85 GB. The smallest option (Q2_K) is 0.36 GB.
- Which GPUs can run Josiefied Qwen3.5 0.8B Gabliterated V1?
35 consumer GPUs can run Josiefied Qwen3.5 0.8B Gabliterated V1 at Q4_K_M (0.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Josiefied Qwen3.5 0.8B Gabliterated V1?
33 devices with unified memory can run Josiefied Qwen3.5 0.8B Gabliterated V1 at Q4_K_M (0.9 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.