OTel LLM E4B IT — Hardware Requirements & GPU Compatibility
Chat
OTel LLM E4B IT is a 4B-parameter open language model from farbodtavakkoli. It supports a context window of up to 131,072 tokens. At BF16 it needs about 8.52 GB of VRAM — see which GPUs and Macs can run it below.
1.2M downloadsSpecifications
- Publisher
- farbodtavakkoli
- Parameters
- 4B
- Architecture
- Gemma4ForConditionalGeneration
- Context Length
- 131,072 tokens
- Vocabulary Size
- 262,144
- Release Date
- 2026-06-17
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does OTel LLM E4B IT Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 8.5 GB | 22.4 GB | 8.00 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 OTel LLM E4B IT?
BF16 · 8.5 GBOTel LLM E4B IT (BF16) requires 8.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 12+ GB is recommended. Using the full 131K context window can add up to 13.9 GB, bringing total usage to 22.4 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 OTel LLM E4B IT?
BF16 · 8.5 GB49 devices with unified memory can run OTel LLM E4B IT, 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 OTel LLM E4B IT need?
OTel LLM E4B IT requires 8.5 GB of VRAM at BF16. Full 131K context adds up to 13.9 GB (22.4 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 4B × 16 bits ÷ 8 = 8 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 14.4 GB (at full 131K context)
VRAM usage by quantization
BF168.5 GBBF16 + full context22.4 GB- Can I run OTel LLM E4B IT on a Mac?
OTel LLM E4B IT requires at least 8.5 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 OTel LLM E4B IT locally?
Yes — OTel LLM E4B IT can run locally on consumer hardware. At BF16 quantization it needs 8.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OTel LLM E4B IT?
At BF16, OTel LLM E4B IT can reach ~563 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~77 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 ÷ 8.5 × 0.65 = ~610 tok/s
Estimated speed at BF16 (8.5 GB)
~610 tok/s~77 tok/s~610 tok/s~563 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OTel LLM E4B IT?
At BF16, the download is about 8.00 GB.
- Which GPUs can run OTel LLM E4B IT?
40 consumer GPUs can run OTel LLM E4B IT at BF16 (8.5 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 OTel LLM E4B IT?
52 devices with unified memory can run OTel LLM E4B IT at BF16 (8.5 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.