Vlt5 Base Keywords — Hardware Requirements & GPU Compatibility
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Vlt5 Base Keywords is a 275M-parameter open language model from Voicelab. At BF16 it needs about 0.61 GB of VRAM — see which GPUs and Macs can run it below.
364.1K downloads
55 likesSpecifications
- Publisher
- Voicelab
- Parameters
- 275M
- Architecture
- T5ForConditionalGeneration
- Vocabulary Size
- 50,048
- Release Date
- 2022-09-27
- License
- CC BY 4.0
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HuggingFace
How Much VRAM Does Vlt5 Base Keywords Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 0.6 GB | — | 0.55 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 Vlt5 Base Keywords?
BF16 · 0.6 GBVlt5 Base Keywords (BF16) requires 0.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Vlt5 Base Keywords?
BF16 · 0.6 GB59 devices with unified memory can run Vlt5 Base Keywords, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does Vlt5 Base Keywords need?
Vlt5 Base Keywords requires 0.6 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 275M × 16 bits ÷ 8 = 0.6 GB
VRAM usage by quantization
BF160.6 GB- Can I run Vlt5 Base Keywords on a Mac?
Vlt5 Base Keywords requires at least 0.6 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 Vlt5 Base Keywords locally?
Yes — Vlt5 Base Keywords can run locally on consumer hardware. At BF16 quantization it needs 0.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Vlt5 Base Keywords?
At BF16, Vlt5 Base Keywords can reach ~7869 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~1074 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 ÷ 0.6 × 0.65 = ~8525 tok/s
Estimated speed at BF16 (0.6 GB)
~8525 tok/s~1074 tok/s~8525 tok/s~7869 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Vlt5 Base Keywords?
At BF16, the download is about 0.55 GB.
- Which GPUs can run Vlt5 Base Keywords?
52 consumer GPUs can run Vlt5 Base Keywords at BF16 (0.6 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 Vlt5 Base Keywords?
59 devices with unified memory can run Vlt5 Base Keywords at BF16 (0.6 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.