Qwopus3.5 9B v3 — Hardware Requirements & GPU Compatibility
VisionReasoning
Qwopus3.5 9B v3 is a 9.7B-parameter open language model from Jackrong. It supports a context window of up to 262,144 tokens. At BF16 it needs about 19.87 GB of VRAM — see which GPUs and Macs can run it below.
4.0K downloads
92 likesSpecifications
- Publisher
- Jackrong
- Parameters
- 9.7B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-30
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwopus3.5 9B v3 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 19.9 GB | 54.0 GB | 19.31 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 Qwopus3.5 9B v3?
BF16 · 19.9 GBQwopus3.5 9B v3 (BF16) requires 19.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. Using the full 262K context window can add up to 34.1 GB, bringing total usage to 54.0 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Qwopus3.5 9B v3?
BF16 · 19.9 GB21 devices with unified memory can run Qwopus3.5 9B v3, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomWhere to Download Qwopus3.5 9B v3
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwopus3.5 9B v3 need?
Qwopus3.5 9B v3 requires 19.9 GB of VRAM at BF16. Full 262K context adds up to 34.1 GB (54.0 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.7B × 16 bits ÷ 8 = 19.3 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 34.7 GB (at full 262K context)
VRAM usage by quantization
BF1619.9 GBBF16 + full context54.0 GB- Can I run Qwopus3.5 9B v3 on a Mac?
Qwopus3.5 9B v3 requires at least 19.9 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 Qwopus3.5 9B v3 locally?
Yes — Qwopus3.5 9B v3 can run locally on consumer hardware. At BF16 quantization it needs 19.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwopus3.5 9B v3?
At BF16, Qwopus3.5 9B v3 can reach ~147 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~33 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 ÷ 19.9 × 0.55 = ~147 tok/s
Estimated speed at BF16 (19.9 GB)
~147 tok/s~33 tok/s~110 tok/s~91 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwopus3.5 9B v3?
At BF16, the download is about 19.31 GB.
- Which GPUs can run Qwopus3.5 9B v3?
6 consumer GPUs can run Qwopus3.5 9B v3 at BF16 (19.9 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwopus3.5 9B v3?
21 devices with unified memory can run Qwopus3.5 9B v3 at BF16 (19.9 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.