Short answer: Qwen3-VL 30B-A3B is not a 3B memory model. The smallest complete official Q4 path is 19.269GB after adding the vision projector. Use 24GB as a bounded Q4 tier, 32GB as the practical Q4 tier, 48GB for Q8, and 80GB for the official 62.142GB BF16 checkpoint.
A3B describes active routed parameters per token, not stored weights. All expert weights still need storage or device/system-memory placement. The official GGUF path also requires a separate multimodal projector.
Qwen3-VL 30B-A3B files and practical tiers
| Official deployment path | Displayed files | Combined file size | Planning tier |
|---|---|---|---|
| Q4_K_M + Q8 projector | 18.557GB + 0.712GB | 19.269GB | 24GB bounded; 32GB practical |
| Q4_K_M + F16 projector | 18.557GB + 1.083GB | 19.640GB | 24GB bounded; 32GB practical |
| Q8_0 + Q8 projector | 32.484GB + 0.712GB | 33.196GB | 48GB practical; 64GB safer |
| Q8_0 + F16 projector | 32.484GB + 1.083GB | 33.567GB | 48GB practical; 64GB safer |
| Official BF16 safetensors | Checkpoint index totaling 62.142GB | 62.142GB | 64GB too tight; 80GB practical |
Why A3B does not mean 3B storage
The official configuration declares 128 experts and routes eight experts per token. Sparse routing changes which expert computation is active for a token; it does not remove the other experts from the checkpoint. That is why the official Q4_K_M language file is 18.557GB and the BF16 checkpoint is 62.142GB.
Active-parameter labels can be relevant to compute behavior, but they are not a substitute for artifact size, cache, projector, runtime buffers or device placement. Fit, speed, visual accuracy and output quality remain separate questions.
KV cache: 96KiB per allocated text token
Qwen’s official configuration lists 48 language layers, four KV heads and a 128-element head dimension. With FP16/BF16 keys and values, one sequence uses 2 × 48 × 4 × 128 × 2 = 98,304 bytes, or 96KiB per allocated text token.
| Allocated text tokens | FP16/BF16 KV payload |
|---|---|
| 8K | 0.75GiB |
| 32K | 3GiB |
| 64K | 6GiB |
| 128K | 12GiB |
| 256K architectural maximum | 24GiB |
Native 256K context is an architectural maximum, not a guarantee that the allocation fits beside the model. The illustrative cache payload alone is 24GiB at 256K.
Complete vision workloads need more than weights
- Projector: include the official 0.712GB Q8_0 or 1.083GB F16 projector.
- Images and video: dimensions, count, sampled frames, duration and timestamp processing affect allocations.
- DeepStack: the architecture fuses features from vision layers 8, 16 and 24.
- Concurrency: parallel requests multiply cache and other request-specific state.
- Expert/runtime support: verify MoE kernels, quantization support and device placement in the exact runtime version.
- Offload: splitting weights across accelerator and system memory changes residency and can change speed.
Qwen3-VL 30B-A3B versus dense 32B
The official complete Q4 paths are 19.269GB for Qwen3-VL 30B-A3B and 20.535GB for dense Qwen3-VL 32B with Q8_0 projectors. Their cache geometries differ more sharply: 96KiB per token for the MoE model versus 256KiB for dense 32B. This does not prove that one is universally faster, better or more accurate; hardware, kernels, routing, precision and workload matter.
Compare the dense Qwen3-VL family hub, dense Qwen3-VL 32B guide, text-only Qwen3-30B-A3B guide and MoE VRAM explainer.
Reproducible Qwen3-VL 30B-A3B fit test
- Record exact repository, revision, Instruct/Thinking variant, language file, projector and checksums.
- Record runtime/version, MoE support and whether image/video inputs are actually active.
- Record weight, projector and cache precision plus every device placement or offload.
- Fix image dimensions/count, video sampling, text allocation, output target and concurrency.
- Measure accelerator and system memory before load, after load, after vision encoding and at peak generation.
- Use image-grounded and video-grounded prompts; text output alone does not prove modality support.
- Capture preprocessing time, time to first token, throughput, expert/runtime fallback and errors.
Use the 27B/32B sizing guide, 24GB–48GB capacity comparison, VRAM calculator, workload test and benchmark submission.
Continue planning
Browse all guides and tools. The active-parameter label is not a hardware-memory tier.
Flagship MoE sibling: the Qwen3-VL 235B-A22B guide uses exact official split-file sums and multi-device safeguards from 160GB through 640GB.
MoE family comparison: the Qwen3-VL 30B-A3B vs 235B-A22B hub compares complete official artifacts, active-versus-stored parameters and cache geometry.
Sources and limits
- Qwen official Qwen3-VL 30B-A3B repository
- Qwen official configuration
- Qwen official BF16 index
- Qwen official GGUF repository
- Qwen3-VL official code repository
- HighVRAM methodology
Reviewed October 25, 2026. Artifact sizes and architecture fields were read from official Qwen repositories. HighVRAM has not independently benchmarked every runtime, MoE kernel, image/video workload, variant or quantization. File size is not peak resident memory.
Newer hybrid MoE comparison: Qwen3.5-35B-A3B uses 35B total/3B active parameters, a 3:1 linear/full-attention stack and separate first-party GPTQ evidence.