TLive Omni 4B — Hardware Requirements & GPU Compatibility
ChatVision
TLive Omni 4B is a 5.8B-parameter open language model from TaoLiveAIGC. It supports a context window of up to 262,144 tokens. At BF16 it needs about 12.11 GB of VRAM — see which GPUs and Macs can run it below.
1.8K downloads
17 likesSpecifications
- Publisher
- TaoLiveAIGC
- Parameters
- 5.8B
- Architecture
- TLiveOmniForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-08-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does TLive Omni 4B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 12.1 GB | 33.4 GB | 11.65 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 TLive Omni 4B?
BF16 · 12.1 GBTLive Omni 4B (BF16) requires 12.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 16+ GB is recommended. Using the full 262K context window can add up to 21.3 GB, bringing total usage to 33.4 GB. 26 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run TLive Omni 4B?
BF16 · 12.1 GB48 devices with unified memory can run TLive Omni 4B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomFrequently Asked Questions
- How much VRAM does TLive Omni 4B need?
TLive Omni 4B requires 12.1 GB of VRAM at BF16. Full 262K context adds up to 21.3 GB (33.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 5.8B × 16 bits ÷ 8 = 11.6 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 21.8 GB (at full 262K context)
VRAM usage by quantization
BF1612.1 GBBF16 + full context33.4 GB- Can I run TLive Omni 4B on a Mac?
TLive Omni 4B requires at least 12.1 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 TLive Omni 4B locally?
Yes — TLive Omni 4B can run locally on consumer hardware. At BF16 quantization it needs 12.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is TLive Omni 4B?
At BF16, TLive Omni 4B can reach ~396 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~54 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 ÷ 12.1 × 0.65 = ~429 tok/s
Estimated speed at BF16 (12.1 GB)
~429 tok/s~54 tok/s~429 tok/s~396 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of TLive Omni 4B?
At BF16, the download is about 11.65 GB.
- Which GPUs can run TLive Omni 4B?
26 consumer GPUs can run TLive Omni 4B at BF16 (12.1 GB). Top options include AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, AMD Radeon RX 6800. 8 GPUs have plenty of headroom for comfortable inference.
- Which devices can run TLive Omni 4B?
49 devices with unified memory can run TLive Omni 4B at BF16 (12.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.