TinyLlama 1.1B Chat v1.0 — Hardware Requirements & GPU Compatibility
Chat
TinyLlama 1.1B Chat is a 1.1-billion parameter chat model built on the Llama 2 architecture and trained on approximately 3 trillion tokens, an unusually large dataset for a model of its size. The TinyLlama project demonstrated that small models can achieve strong performance when given sufficient training compute, making it a standout in the sub-2B parameter class. The Chat variant is fine-tuned for conversational use and runs on virtually any modern GPU, including entry-level cards with 4GB of VRAM or less. It is a practical choice for lightweight local inference, edge deployment, and experimentation where hardware resources are limited.
2.1M downloads
1.5K likesSpecifications
- Publisher
- TinyLlama
- Family
- Llama
- Parameters
- 1.1B
- Architecture
- LlamaForCausalLM
- Context Length
- 2,048 tokens
- Vocabulary Size
- 32,000
- Release Date
- 2024-03-17
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does TinyLlama 1.1B Chat v1.0 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 0.8 GB | — | 0.47 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 0.8 GB | — | 0.48 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 0.9 GB | — | 0.54 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 0.9 GB | — | 0.55 GB | 4-bit legacy quantization |
| Q3_K_L | 4.10 | 0.9 GB | — | 0.56 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 0.9 GB | — | 0.59 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 1.0 GB | — | 0.62 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 1.0 GB | — | 0.66 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_0 | 5.00 | 1.0 GB | — | 0.69 GB | 5-bit legacy quantization |
| Q5_K_S | 5.50 | 1.1 GB | — | 0.76 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 1.1 GB | — | 0.78 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.3 GB | — | 0.91 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 1.4 GB | — | 1.10 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run TinyLlama 1.1B Chat v1.0?
Q4_K_M · 1.0 GBTinyLlama 1.1B Chat v1.0 (Q4_K_M) requires 1.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ 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 Chat v1.0?
Q4_K_M · 1.0 GB33 devices with unified memory can run TinyLlama 1.1B Chat v1.0, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does TinyLlama 1.1B Chat v1.0 need?
TinyLlama 1.1B Chat v1.0 requires 1.0 GB of VRAM at Q4_K_M, or 1.4 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 1.1B × 4.8 bits ÷ 8 = 0.7 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M1.0 GB- What's the best quantization for TinyLlama 1.1B Chat v1.0?
For TinyLlama 1.1B Chat v1.0, Q4_K_M (1.0 GB) offers the best balance of quality and VRAM usage. Q5_0 (1.0 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 0.8 GB.
VRAM requirement by quantization
Q2_K0.8 GB~75%Q4_00.9 GB~85%Q4_K_S1.0 GB~88%Q4_K_M ★1.0 GB~89%Q5_K_S1.1 GB~92%Q8_01.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run TinyLlama 1.1B Chat v1.0 on a Mac?
TinyLlama 1.1B Chat v1.0 requires at least 0.8 GB at Q2_K, 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 Chat v1.0 locally?
Yes — TinyLlama 1.1B Chat v1.0 can run locally on consumer hardware. At Q4_K_M quantization it needs 1.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is TinyLlama 1.1B Chat v1.0?
At Q4_K_M, TinyLlama 1.1B Chat v1.0 can reach ~2886 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~649 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 ÷ 1.0 × 0.55 = ~2886 tok/s
Estimated speed at Q4_K_M (1.0 GB)
AMD Instinct MI300X~2886 tok/sNVIDIA GeForce RTX 4090~649 tok/sNVIDIA H100 SXM~2157 tok/sAMD Instinct MI250X~1784 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 Chat v1.0?
At Q4_K_M, the download is about 0.66 GB. The full-precision Q8_0 version is 1.10 GB. The smallest option (Q2_K) is 0.47 GB.