Short answer: Qwen2.5-VL spans very different memory classes. The official BF16 instruct checkpoints total 7.509GB for 3B, 16.584GB for 7B, 68.283GB for 32B and 146.822GB for 72B. Cautious starting tiers are roughly 12GB, 24GB, 80GB/96GB and 192GB respectively, before increasing context, image/video load or concurrency.
Model size alone is not the whole workload. Qwen2.5-VL combines a language model with a dynamic-resolution vision encoder. Community GGUF packaging may separate the multimodal projector, while the official safetensor checkpoints represent integrated model repositories. Verify every required file and test the actual image path.
Qwen2.5-VL VRAM at a glance
| Official instruct model | BF16 shard total | FP16/BF16 KV per text token | Cautious starting tier |
|---|---|---|---|
| Qwen2.5-VL-3B | 7.509GB across 2 shards | 36KiB | 12GB |
| Qwen2.5-VL-7B | 16.584GB across 5 shards | 56KiB | 24GB |
| Qwen2.5-VL-32B | 68.283GB across 18 shards | 256KiB | 80GB bounded; 96GB safer |
| Qwen2.5-VL-72B | 146.822GB across 38 shards | 320KiB | 192GB |
Why cache does not scale directly with the model label
For FP16/BF16 keys and values, the one-sequence language-cache payload is 2 × layers × KV heads × head dimension × 2 bytes per allocated text token. Qwen’s official configurations produce 36KiB/token for 3B, 56KiB/token for 7B, 256KiB/token for 32B and 320KiB/token for 72B.
| Model | 8K text allocation | 32K | 128K architectural maximum |
|---|---|---|---|
| 3B | 0.281GiB | 1.125GiB | 4.5GiB |
| 7B | 0.438GiB | 1.75GiB | 7GiB |
| 32B | 2GiB | 8GiB | 32GiB |
| 72B | 2.5GiB | 10GiB | 40GiB |
3B: compact edge-oriented path
The official 3B checkpoint totals 7.509GB before cache and runtime state. That makes an 8GB label a file-size boundary rather than a dependable full-precision deployment. Start around 12GB for bounded BF16 experiments or use a verified quantized path on smaller hardware, keeping the vision component and image workload in the total. The exact Qwen2.5-VL 3B guide maps complete Q4/Q8 projector pairs across 4GB, 6GB, 8GB and 12GB.
7B: the practical single-GPU middle
The official checkpoint totals 16.584GB, so 16GB cannot hold the displayed files plus working memory. A 24GB accelerator is the clearer BF16 starting point. Quantized GGUF routes can target 8GB–16GB, but their separate projector must be counted. See the exact Qwen2.5-VL 7B VRAM guide for complete Q4/Q8 file pairs and 56KiB/token cache math.
32B: 80GB is bounded, 96GB is safer
The official 68.283GB BF16 checkpoint leaves limited headroom on an 80GB accelerator once cache, vision encoding and runtime allocations are included. At 32K text allocation, the arithmetic FP16/BF16 cache alone is 8GiB. Treat 80GB as a bounded workload target and 96GB as the safer single-pool planning tier; larger contexts or concurrency can require more. The exact Qwen2.5-VL 32B guide adds complete Q4/Q8 projector pairs and tier-by-tier working-room analysis.
72B: a 192GB-class official deployment
The official 146.822GB checkpoint plus a 10GiB 32K cache payload already approaches 160GB before vision and runtime allocations. A 192GB-class pool is the practical starting point for official BF16, with exact usable memory, software support, image/video load and concurrency still controlling fit. The exact Qwen2.5-VL 72B guide adds complete Q4/Q8 projector pairs, 48GB/80GB file-size traps and per-device validation.
Image and video memory is workload-dependent
- Dynamic resolution: images of different dimensions become different numbers of visual tokens.
- Image count: multiple images increase preprocessing and prompt state.
- Video: resolution, sampled frames, duration and frame rate affect the vision workload.
- Vision precision and packaging: a separate F16 projector has a different footprint from a lower-precision component.
- Concurrency: parallel requests multiply cache and intermediate-state pressure.
- Unified memory: the operating system and applications share the advertised pool.
A reproducible family comparison
- Record exact repository, revision, license, every required file and checksum.
- State whether the vision encoder/projector is integrated, separate or missing.
- Record model, projector and cache precision plus device placement.
- Fix image dimensions, image count, video settings, text allocation, output target and concurrency.
- Measure memory before load, after load, after vision encoding and at peak generation.
- Test image-grounded correctness; text-only output does not prove a working vision path.
- Capture preprocessing time, time to first token, throughput, fallback and out-of-memory errors.
Compare the broader multimodal VRAM hub, model-size map, VRAM calculator, unified-memory guide, workload test and benchmark submission.
Continue planning
Compare 8GB–16GB tiers, compare 48GB–96GB tiers, or browse all guides and tools.
Newer generation: compare the Qwen3-VL 8B VRAM guide, whose official 17.534GB BF16 checkpoint, 144KiB/token cache and native 256K architecture differ from Qwen2.5-VL.
Newer 32B generation: compare the Qwen3-VL 32B VRAM guide for official Q4/Q8 projector pairs, DeepStack vision fusion and native 256K architecture.
Newer generation: compare the Qwen3-VL 2B/4B/8B/32B VRAM hub for official complete Q4/Q8 deployment totals.
Sources and limits
- Qwen official 3B repository
- Qwen official 7B repository
- Qwen official 32B repository
- Qwen official 72B repository
- Qwen2.5-VL release article
- Qwen2.5-VL-32B release article
- HighVRAM methodology
Reviewed October 25, 2026. Checkpoint totals and architecture fields were read from the cited official repositories. HighVRAM has not independently benchmarked every model, runtime, quantization, image resolution or video workload. File size is not peak resident memory.