Jackrong·Qwen3_5ForConditionalGeneration

Qwopus3.5 9B V3.5 — Hardware Requirements & GPU Compatibility

ChatReasoningFunctions

Qwopus3.5 9B V3.5 is a 9.7B-parameter open language model from Jackrong. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 6.36 GB of VRAM — see which GPUs and Macs can run it below.

889 downloads 25 likes262K context

Specifications

Publisher
Jackrong
Parameters
9.7B
Architecture
Qwen3_5ForConditionalGeneration
Context Length
262,144 tokens
Vocabulary Size
248,320
Release Date
2026-04-16
License
Apache 2.0

Get Started

How Much VRAM Does Qwopus3.5 9B V3.5 Need?

Select a quantization to see compatible GPUs below.

QuantizationBitsVRAM
Q3_K_L4.105.5 GB
IQ4_XS4.305.8 GB
Q4_K_S4.506 GB
Q4_K_M4.806.4 GB
Q5_K_S5.507.2 GB
Q5_K_M5.707.5 GB
Q6_K6.608.5 GB
Q8_08.0010.2 GB

Which GPUs Can Run Qwopus3.5 9B V3.5?

Q4_K_M · 6.4 GB

Qwopus3.5 9B V3.5 (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 34.1 GB, bringing total usage to 40.5 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.

Which Devices Can Run Qwopus3.5 9B V3.5?

Q4_K_M · 6.4 GB

33 devices with unified memory can run Qwopus3.5 9B V3.5, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).

Related Models

Frequently Asked Questions

How much VRAM does Qwopus3.5 9B V3.5 need?

Qwopus3.5 9B V3.5 requires 6.4 GB of VRAM at Q4_K_M, or 10.2 GB at Q8_0. Full 262K context adds up to 34.1 GB (40.5 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)

KV Cache + Overhead 34.7 GB (at full 262K context)

VRAM usage by quantization

6.4 GB
40.5 GB

Learn more about VRAM estimation →

What's the best quantization for Qwopus3.5 9B V3.5?

For Qwopus3.5 9B V3.5, 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 Q3_K_L at 5.5 GB.

VRAM requirement by quantization

Q3_K_L
5.5 GB
Q4_K_S
6.0 GB
Q4_K_M
6.4 GB
Q5_K_S
7.2 GB
Q5_K_M
7.5 GB
Q8_0
10.2 GB

★ Recommended — best balance of quality and VRAM usage.

Learn more about quantization →

Can I run Qwopus3.5 9B V3.5 on a Mac?

Qwopus3.5 9B V3.5 requires at least 5.5 GB at Q3_K_L, 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 V3.5 locally?

Yes — Qwopus3.5 9B V3.5 can run locally on consumer hardware. At Q4_K_M quantization it needs 6.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.

How fast is Qwopus3.5 9B V3.5?

At Q4_K_M, Qwopus3.5 9B V3.5 can reach ~458 tok/s on AMD Instinct MI300X. 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: AMD Instinct MI300X5300 ÷ 6.4 × 0.55 = ~458 tok/s

Estimated speed at Q4_K_M (6.4 GB)

~458 tok/s
~103 tok/s
~343 tok/s
~283 tok/s

Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.

Learn more about tok/s estimation →

What's the download size of Qwopus3.5 9B V3.5?

At Q4_K_M, the download is about 5.79 GB. The full-precision Q8_0 version is 9.65 GB. The smallest option (Q3_K_L) is 4.95 GB.

Which GPUs can run Qwopus3.5 9B V3.5?

35 consumer GPUs can run Qwopus3.5 9B V3.5 at Q4_K_M (6.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.

Which devices can run Qwopus3.5 9B V3.5?

33 devices with unified memory can run Qwopus3.5 9B V3.5 at Q4_K_M (6.4 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.