Qwen3.5 0.8B L — Hardware Requirements & GPU Compatibility
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Qwen3.5 0.8B L is a 776M-parameter open language model from trymirai in the Qwen 3.5 family. At BF16 it needs about 1.71 GB of VRAM — see which GPUs and Macs can run it below.
17 downloads
2 likesSpecifications
- Publisher
- trymirai
- Family
- Qwen 3.5
- Parameters
- 776M
- Release Date
- 2026-06-04
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3.5 0.8B L Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 1.7 GB | — | 1.55 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 Qwen3.5 0.8B L?
BF16 · 1.7 GBQwen3.5 0.8B L (BF16) requires 1.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 3+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3.5 0.8B L?
BF16 · 1.7 GB59 devices with unified memory can run Qwen3.5 0.8B L, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3.5 0.8B L need?
Qwen3.5 0.8B L requires 1.7 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 776M × 16 bits ÷ 8 = 1.6 GB
KV Cache + Overhead ≈ 0.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF161.7 GB- Can I run Qwen3.5 0.8B L on a Mac?
Qwen3.5 0.8B L requires at least 1.7 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 Qwen3.5 0.8B L locally?
Yes — Qwen3.5 0.8B L can run locally on consumer hardware. At BF16 quantization it needs 1.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 0.8B L?
At BF16, Qwen3.5 0.8B L can reach ~2807 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~383 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 ÷ 1.7 × 0.65 = ~3041 tok/s
Estimated speed at BF16 (1.7 GB)
~3041 tok/s~383 tok/s~3041 tok/s~2807 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3.5 0.8B L?
At BF16, the download is about 1.55 GB.
- Which GPUs can run Qwen3.5 0.8B L?
52 consumer GPUs can run Qwen3.5 0.8B L at BF16 (1.7 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.5 0.8B L?
59 devices with unified memory can run Qwen3.5 0.8B L at BF16 (1.7 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.