Josiefied Qwen3.5 0.8B Gabliterated V1 — Hardware Requirements & GPU Compatibility
Chat
Josiefied Qwen3.5 0.8B Gabliterated V1 is a 853M-parameter open language model from Goekdeniz-Guelmez in the Qwen 3.5 family. It supports a context window of up to 262,144 tokens. At BF16 it needs about 2.06 GB of VRAM — see which GPUs and Macs can run it below.
619 downloads
4 likesSpecifications
- Publisher
- Goekdeniz-Guelmez
- Family
- Qwen 3.5
- Parameters
- 853M
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-09
Get Started
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 |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 2.1 GB | 8.4 GB | 1.71 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 Qwen3.5 0.8B Gabliterated V1?
BF16 · 2.1 GBJosiefied Qwen3.5 0.8B Gabliterated V1 (BF16) requires 2.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. Using the full 262K context window can add up to 6.4 GB, bringing total usage to 8.4 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Josiefied Qwen3.5 0.8B Gabliterated V1?
BF16 · 2.1 GB59 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 2.1 GB of VRAM at BF16. Full 262K context adds up to 6.4 GB (8.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 853M × 16 bits ÷ 8 = 1.7 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.7 GB (at full 262K context)
VRAM usage by quantization
BF162.1 GBBF16 + full context8.4 GB- Can I run Josiefied Qwen3.5 0.8B Gabliterated V1 on a Mac?
Josiefied Qwen3.5 0.8B Gabliterated V1 requires at least 2.1 GB at BF16, 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 BF16 quantization it needs 2.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Josiefied Qwen3.5 0.8B Gabliterated V1?
At BF16, Josiefied Qwen3.5 0.8B Gabliterated V1 can reach ~2136 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~318 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 ÷ 2.1 × 0.65 = ~2524 tok/s
Estimated speed at BF16 (2.1 GB)
~2524 tok/s~318 tok/s~2524 tok/s~2136 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 BF16, the download is about 1.71 GB.
- Which GPUs can run Josiefied Qwen3.5 0.8B Gabliterated V1?
50 consumer GPUs can run Josiefied Qwen3.5 0.8B Gabliterated V1 at BF16 (2.1 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 Josiefied Qwen3.5 0.8B Gabliterated V1?
59 devices with unified memory can run Josiefied Qwen3.5 0.8B Gabliterated V1 at BF16 (2.1 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.