TinyLlama 1.1B Intermediate Step 1431k 3T — Hardware Requirements & GPU Compatibility
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TinyLlama 1.1B Intermediate Step 1431k 3T is a 1.1B-parameter open language model from TinyLlama in the Llama family. It supports a context window of up to 2,048 tokens. At BF16 it needs about 2.55 GB of VRAM — see which GPUs and Macs can run it below.
40.6K downloads
186 likesSpecifications
- Publisher
- TinyLlama
- Family
- Llama
- Parameters
- 1.1B
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2024-09-27
- License
- Apache 2.0
Get Started
How Much VRAM Does TinyLlama 1.1B Intermediate Step 1431k 3T Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 2.5 GB | — | 2.20 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run TinyLlama 1.1B Intermediate Step 1431k 3T?
BF16 · 2.5 GBTinyLlama 1.1B Intermediate Step 1431k 3T (BF16) requires 2.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run TinyLlama 1.1B Intermediate Step 1431k 3T?
BF16 · 2.5 GB33 devices with unified memory can run TinyLlama 1.1B Intermediate Step 1431k 3T, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does TinyLlama 1.1B Intermediate Step 1431k 3T need?
TinyLlama 1.1B Intermediate Step 1431k 3T requires 2.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 1.1B × 16 bits ÷ 8 = 2.2 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF162.5 GB- Can I run TinyLlama 1.1B Intermediate Step 1431k 3T on a Mac?
TinyLlama 1.1B Intermediate Step 1431k 3T requires at least 2.5 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 TinyLlama 1.1B Intermediate Step 1431k 3T locally?
Yes — TinyLlama 1.1B Intermediate Step 1431k 3T can run locally on consumer hardware. At BF16 quantization it needs 2.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is TinyLlama 1.1B Intermediate Step 1431k 3T?
At BF16, TinyLlama 1.1B Intermediate Step 1431k 3T can reach ~1143 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~257 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 2.5 × 0.55 = ~1143 tok/s
Estimated speed at BF16 (2.5 GB)
~1143 tok/s~257 tok/s~854 tok/s~707 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of TinyLlama 1.1B Intermediate Step 1431k 3T?
At BF16, the download is about 2.20 GB.
- Which GPUs can run TinyLlama 1.1B Intermediate Step 1431k 3T?
35 consumer GPUs can run TinyLlama 1.1B Intermediate Step 1431k 3T at BF16 (2.5 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run TinyLlama 1.1B Intermediate Step 1431k 3T?
33 devices with unified memory can run TinyLlama 1.1B Intermediate Step 1431k 3T at BF16 (2.5 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.