Qwen3.5 0.8B Base — Hardware Requirements & GPU Compatibility
Vision
Qwen3.5-0.8B-Base is Alibaba's smallest base checkpoint in the Qwen3.5 family, at 0.87 billion parameters, a native vision-language foundation model that fuses image and text tokens during pretraining rather than bolting a vision encoder onto a text-only model. Like the rest of the -Base line, it ships as pretrained-only weights for fine-tuning and research, not direct interaction, though its control tokens support efficient LoRA-style adaptation with the official chat template. Its hybrid architecture pairs Gated DeltaNet linear attention with periodic full attention layers. It runs easily on a single modest consumer GPU, even unquantized. Context length is 262,144 tokens natively, extensible up to 1,010,000 tokens. It is released under the Apache 2.0 license, and was published in February 2026 as the smallest of five Qwen3.5-Base sizes, from 0.8B dense up to a 35B mixture-of-experts model.
310.2K downloads
109 likesSpecifications
- Publisher
- Alibaba
- Family
- Qwen 3.5
- Parameters
- 873M
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-02-28
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3.5 0.8B Base Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.7 GB | 7.1 GB | 0.37 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.7 GB | 7.1 GB | 0.38 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.8 GB | 7.2 GB | 0.43 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.8 GB | 7.2 GB | 0.44 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 0.9 GB | 7.3 GB | 0.52 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 1.0 GB | 7.4 GB | 0.62 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.1 GB | 7.5 GB | 0.72 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.2 GB | 7.6 GB | 0.87 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3.5 0.8B Base?
Q4_K_M · 0.9 GBQwen3.5 0.8B Base (Q4_K_M) requires 0.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 262K context window can add up to 6.4 GB, bringing total usage to 7.3 GB. 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 Base?
Q4_K_M · 0.9 GB59 devices with unified memory can run Qwen3.5 0.8B Base, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomWhere to Download Qwen3.5 0.8B Base
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 Qwen3.5 0.8B Base need?
Qwen3.5 0.8B Base requires 0.9 GB of VRAM at Q4_K_M, or 2.1 GB at BF16. Full 262K context adds up to 6.4 GB (7.3 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 873M × 4.8 bits ÷ 8 = 0.5 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 6.8 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M0.9 GBQ4_K_M + full context7.3 GB- What's the best quantization for Qwen3.5 0.8B Base?
For Qwen3.5 0.8B Base, Q4_K_M (0.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 0.6 GB.
VRAM requirement by quantization
IQ2_XXS0.6 GBIQ3_XS0.7 GBQ4_00.8 GBIQ4_NL0.8 GBQ4_K_M ★0.9 GBBF162.1 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 0.8B Base on a Mac?
Qwen3.5 0.8B Base requires at least 0.6 GB at IQ2_XXS, 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 Base locally?
Yes — Qwen3.5 0.8B Base can run locally on consumer hardware. At Q4_K_M quantization it needs 0.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 0.8B Base?
At Q4_K_M, Qwen3.5 0.8B Base can reach ~5517 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~753 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 ÷ 0.9 × 0.65 = ~5977 tok/s
Estimated speed at Q4_K_M (0.9 GB)
~5977 tok/s~753 tok/s~5977 tok/s~5517 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 Base?
At Q4_K_M, the download is about 0.52 GB. The full-precision BF16 version is 1.75 GB. The smallest option (IQ2_XXS) is 0.24 GB.
- Which GPUs can run Qwen3.5 0.8B Base?
52 consumer GPUs can run Qwen3.5 0.8B Base at Q4_K_M (0.9 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 Base?
59 devices with unified memory can run Qwen3.5 0.8B Base at Q4_K_M (0.9 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.