Qwopus3.5 4B Coder — Hardware Requirements & GPU Compatibility
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Qwopus3.5 4B Coder is a 4.7B-parameter open language model from Jackrong. It supports a context window of up to 262,144 tokens. At BF16 it needs about 9.79 GB of VRAM — see which GPUs and Macs can run it below.
1.4K downloads
11 likesSpecifications
- Publisher
- Jackrong
- Parameters
- 4.7B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-05-28
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwopus3.5 4B Coder Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 9.8 GB | 31.1 GB | 9.32 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Qwopus3.5 4B Coder?
BF16 · 9.8 GBQwopus3.5 4B Coder (BF16) requires 9.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. Using the full 262K context window can add up to 21.3 GB, bringing total usage to 31.1 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwopus3.5 4B Coder?
BF16 · 9.8 GB27 devices with unified memory can run Qwopus3.5 4B Coder, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwopus3.5 4B Coder need?
Qwopus3.5 4B Coder requires 9.8 GB of VRAM at BF16. Full 262K context adds up to 21.3 GB (31.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.7B × 16 bits ÷ 8 = 9.3 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 21.8 GB (at full 262K context)
VRAM usage by quantization
BF169.8 GBBF16 + full context31.1 GB- Can I run Qwopus3.5 4B Coder on a Mac?
Qwopus3.5 4B Coder requires at least 9.8 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 4B Coder locally?
Yes — Qwopus3.5 4B Coder can run locally on consumer hardware. At BF16 quantization it needs 9.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwopus3.5 4B Coder?
At BF16, Qwopus3.5 4B Coder can reach ~298 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~67 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 ÷ 9.8 × 0.55 = ~298 tok/s
Estimated speed at BF16 (9.8 GB)
~298 tok/s~67 tok/s~223 tok/s~184 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 4B Coder?
At BF16, the download is about 9.32 GB.
- Which GPUs can run Qwopus3.5 4B Coder?
28 consumer GPUs can run Qwopus3.5 4B Coder at BF16 (9.8 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwopus3.5 4B Coder?
27 devices with unified memory can run Qwopus3.5 4B Coder at BF16 (9.8 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.