VLM2Vec Full — Hardware Requirements & GPU Compatibility
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VLM2Vec Full is a 4.1B-parameter open language model from TIGER-Lab. It supports a context window of up to 131,072 tokens. At BF16 it needs about 9.40 GB of VRAM — see which GPUs and Macs can run it below.
175.2K downloads
29 likesSpecifications
- Publisher
- TIGER-Lab
- Parameters
- 4.1B
- Architecture
- Phi3VForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 32,064
- Release Date
- 2024-10-08
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does VLM2Vec Full Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 9.4 GB | 60.1 GB | 8.29 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 VLM2Vec Full?
BF16 · 9.4 GBVLM2Vec Full (BF16) requires 9.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 13+ GB is recommended. Using the full 131K context window can add up to 50.7 GB, bringing total usage to 60.1 GB. 40 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run VLM2Vec Full?
BF16 · 9.4 GB49 devices with unified memory can run VLM2Vec Full, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPad Pro M5 13" (16 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does VLM2Vec Full need?
VLM2Vec Full requires 9.4 GB of VRAM at BF16. Full 131K context adds up to 50.7 GB (60.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4.1B × 16 bits ÷ 8 = 8.3 GB
KV Cache + Overhead ≈ 1.1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 51.8 GB (at full 131K context)
VRAM usage by quantization
BF169.4 GBBF16 + full context60.1 GB- Can I run VLM2Vec Full on a Mac?
VLM2Vec Full requires at least 9.4 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 VLM2Vec Full locally?
Yes — VLM2Vec Full can run locally on consumer hardware. At BF16 quantization it needs 9.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is VLM2Vec Full?
At BF16, VLM2Vec Full can reach ~511 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~70 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 ÷ 9.4 × 0.65 = ~553 tok/s
Estimated speed at BF16 (9.4 GB)
~553 tok/s~70 tok/s~553 tok/s~511 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of VLM2Vec Full?
At BF16, the download is about 8.29 GB.
- Which GPUs can run VLM2Vec Full?
40 consumer GPUs can run VLM2Vec Full at BF16 (9.4 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 26 GPUs have plenty of headroom for comfortable inference.
- Which devices can run VLM2Vec Full?
52 devices with unified memory can run VLM2Vec Full at BF16 (9.4 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.