Qwopus3.5 9B v3 — Hardware Requirements & GPU Compatibility
VisionReasoningFunctions
Qwopus3.5-9B-v3 is a community fine-tune by Jackrong, built on Qwen3.5-9B, aimed at steadier and more efficient reasoning, especially for programming. The v3 release moves away from the earlier approach of distilling chain-of-thought traces from Claude and instead focuses on structural alignment of the reasoning process, with shorter and more stable reasoning. It keeps the base model's image input. At about 9.6 billion parameters, a Q4 build takes roughly 6 GB, so it runs on an 8 GB GPU or a recent laptop. The context length is 262,144 tokens, inherited from Qwen3.5. It is released under the Apache 2.0 license, and the card lists English, Chinese, and Korean. The model emits explicit reasoning before its answer, and the author published the training notebook and code for anyone who wants to reproduce the fine-tune. Treat the benchmark claims as the author's own, since this is an individual project rather than a lab release.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 32K)Best on 24 GB (Q4_K_M · ~103 tok/s · 256K)Macs from 8 GB (IQ2_M · ~19 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Qwopus3.5 9B v3
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/mradermacher/Qwopus3.5-9B-v3-i1-GGUF:Q4_K_MQ4_K_M fits fully on the RTX 4090 · 17.4 GB free · 16K context
Download: Qwopus3.5-9B-v3.i1-Q4_K_M.gguf5.63 GBmradermacher
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (5 GB headroom) · Q4_K_M
- Generation speed
- ~93 tok/s
- generation speed
- Cost per 1M output tokens
- $0.36
- per 1M output tokens
How Much VRAM Does Qwopus3.5 9B v3 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 4.7 GB | 5.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~140 tok/s | |
| 4.8 GB | 5.6 GB | 3-bit small — 3-bit small quantization | Fits: 256K context · ~137 tok/s | |
| 5.3 GB | 6.1 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~124 tok/s | |
| 5.4 GB | 6.2 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 256K context · ~122 tok/s | |
| 6.4 GB | 7.2 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~103 tok/s | |
| 7.5 GB | 8.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 256K context · ~88 tok/s | |
| 8.5 GB | 9.3 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 256K context · ~77 tok/s | |
| 10.2 GB | 11 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 256K context · ~64 tok/s |
Selected Q4_K_M
4-bit medium quantization6.6GBat 16K context
- Weights
- 5.8 GB
- KV cache
- 0.5 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 17.4 GB free · ~103 tok/s
≥50 tok/s needs ≥489 GB/s · RTX 5090 ~183, RTX 3090 Ti ~103
Which Hardware Can Run Qwopus3.5 9B v3?
Q4_K_M · 6.4 GBQwopus3.5 9B v3 (Q4_K_M) requires 6.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 9+ GB is recommended. Using the full 262K context window can add up to 8.3 GB, bringing total usage to 14.7 GB. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti. 151 devices with unified memory can run Qwopus3.5 9B v3, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
≤ 11 GB
≤ 11 GB tier →fits · 1.4 GB free · up to 32K ctx
16 GPUs · 1 device family
RTX 3080~78Simulate output on RTX 3080RTX 2080 Ti~63Simulate output on RTX 2080 TiRTX 3070 Ti~62Simulate output on RTX 3070 TiOrin Nano~10Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)20 GB
fits · 13.4 GB free · up to 256K ctx
1 GPU
RX 7900 XT~76Simulate output on RX 7900 XT32–40 GB
fits · 25.4 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~183Simulate output on RTX 5090M5 Max36 GB~51Simulate output on Mac Studio M5 Max (36 GB)M532 GB~17Simulate output on MacBook Air 13" M5 (32 GB)Orin~21Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~14Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Qwen3.5 9BAlibaba | 99.4 | 6.6 GB | ~103 | same size |
| Gemma 2 9B ITGoogle | 68.0 | 7.3 GB | ~107 | same size |
| Ornith 1.5 9Bornith-ai | — | 6.6 GB | ~103 | same size |
| MiniCPM O 4 5OpenBMB | — | 8.3 GB | ~105 | same size |
| GLM 4.1V 9B ThinkingZ.ai | — | 7.2 GB | ~100 | same size |
| Shieldgemma 9BGoogle | — | 8.8 GB | ~107 | same size |
Specifications
- 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
Frequently Asked Questions
How much VRAM does Qwopus3.5 9B v3 need?
Qwopus3.5 9B v3 requires 6.4 GB of VRAM at Q4_K_M, or 19.9 GB at BF16. Full 262K context adds up to 8.3 GB (14.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.7B × 4.8 bits ÷ 8 = 5.8 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 8.9 GB (at full 262K context)
VRAM usage by quantization
What's the best quantization for Qwopus3.5 9B v3?
For Qwopus3.5 9B v3, Q4_K_M (6.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (7.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 3.2 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Qwopus3.5 9B v3 on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run Qwopus3.5 9B v3, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 6.4 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M2 Pro (16 GB).
Can I run Qwopus3.5 9B v3 locally?
Yes — Qwopus3.5 9B v3 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Qwopus3.5 9B v3?
At Q4_K_M, Qwopus3.5 9B v3 can reach ~755 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~103 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 ÷ 6.36 × 0.65 = ~818 tok/s
Estimated speed at Q4_K_M (6.4 GB)
Real-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 Q4_K_M, the download is about 5.79 GB. The full-precision BF16 version is 19.31 GB. The smallest option (IQ2_XXS) is 2.65 GB.
Which GPUs can run Qwopus3.5 9B v3?
52 consumer GPUs can run Qwopus3.5 9B v3 at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 40 GPUs have plenty of headroom for comfortable inference.
Which devices can run Qwopus3.5 9B v3?
151 devices with unified memory can run Qwopus3.5 9B v3 at Q4_K_M (6.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.