Alibaba’s Qwen AI Releases Compact Dense Qwen3-VL 4B/8B (Instruct & Thinking) With FP8 Checkpoints

Do you actually need a giant VLM when dense Qwen3-VL 4B/8B (Instruct/Thinking) with FP8 runs in low VRAM yet retains 256K→1M context and the full capability surface? Alibaba’s Qwen team has expanded its multimodal lineup with dense Qwen3-VL models at 4B and 8B scales, each shipping in two task profiles—Instruct and Thinking—plus FP8-quantized checkpoints for low-VRAM deployment. The drop arrives as a smaller, edge-friendly complement to the previously released 30B (MoE) and 235B (MoE) tiers and keeps the same capability surface: image/video understanding, OCR, spatial grounding, and GUI/agent control.

What’s in the release?

SKUs and variants: The new additions comprise four dense models—Qwen3-VL-4B and Qwen3-VL-8B, each in Instruct and Thinking editions—alongside FP8 versions of the 4B/8B Instruct and Thinking checkpoints. The official announcement explicitly frames these as “compact, dense” models with lower VRAM usage and full Qwen3-VL capabilities retained.

Context length and capability surface: The model cards list native 256K context with expandability to 1M, and document the full feature set: long-document and video comprehension, 32-language OCR, 2D/3D spatial grounding, visual coding, and agentic GUI control on desktop and mobile. These attributes carry over to the new 4B/8B SKUs.

Architecture notes: Qwen3-VL highlights three core updates: Interleaved-MRoPE for robust positional encoding over time/width/height (long-horizon video), DeepStack for fusing multi-level ViT features and sharpening image–text alignment, and Text–Timestamp Alignment beyond T-RoPE for event localization in video. These design details appear in the new cards as well, signaling architectural continuity across sizes.

Project timeline: The Qwen3-VL GitHub “News” section records the publication of Qwen3-VL-4B (Instruct/Thinking) and Qwen3-VL-8B (Instruct/Thinking) on Oct 15, 2025, following earlier releases of the 30B MoE tier and organization-wide FP8 availability.

FP8: deployment-relevant details

Numerics and parity claim: The FP8 repositories state fine-grained FP8 quantization with block size 128, with performance metrics nearly identical to the original BF16 checkpoints. For teams evaluating precision trade-offs on multimodal stacks (vision encoders, cross-modal fusion, long-context attention), having vendor-produced FP8 weights reduces re-quantization and re-validation burden.

Tooling status: The 4B-Instruct-FP8 card notes that Transformers does not yet load these FP8 weights directly, and recommends vLLM or SGLang for serving; the card includes working launch snippets. Separately, the vLLM recipes guide recommends FP8 checkpoints for H100 memory efficiency. Together, these point to immediate, supported paths for low-VRAM inference.

Key Takeaways

• Qwen released dense Qwen3-VL 4B and 8B models, each in Instruct and Thinking variants, with FP8 checkpoints.
• FP8 uses fine-grained FP8 (block size 128) with near-BF16 metrics; Transformers loading is not yet supported—use vLLM/SGLang.
• Capability surface is preserved: 256K→1M context, 32-language OCR, spatial grounding, video reasoning, and GUI/agent control.
• Model Card-reported sizes: Qwen3-VL-4B ≈ 4.83B params; Qwen3-VL-8B-Instruct ≈ 8.77B params.

Qwen’s decision to ship dense Qwen3-VL 4B/8B in both Instruct and Thinking forms with FP8 checkpoints is the practical part of the story: lower-VRAM, deployment-ready weights (fine-grained FP8, block size 128) and explicit serving guidance (vLLM/SGLang) makes it easily deployable. The capability surface—256K context expandable to 1M, 32-language OCR, spatial grounding, video understanding, and agent control—remains intact at these smaller scales, which matters more than leaderboard rhetoric for teams targeting single-GPU or edge budgets.

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