TinyDolphin 2.8 1.1B — Hardware Requirements & GPU Compatibility
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TinyDolphin 2.8 1.1B is a 1.1B-parameter open language model from QuixiAI in the Phi family. It supports a context window of up to 4,096 tokens. At Q4_K_M it needs about 1.01 GB of VRAM — see which GPUs and Macs can run it below.
21.3K downloads
63 likesSpecifications
- Publisher
- QuixiAI
- Family
- Phi
- Parameters
- 1.1B
- Architecture
- LlamaForCausalLM
- Context Length
- 4,096 tokens
- Vocabulary Size
- 32,002
- Release Date
- 2024-01-21
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does TinyDolphin 2.8 1.1B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 0.8 GB | 0.9 GB | 0.47 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 0.9 GB | 0.9 GB | 0.54 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 1.0 GB | 1.1 GB | 0.66 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 1.1 GB | 1.2 GB | 0.78 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 1.3 GB | 1.3 GB | 0.91 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 1.4 GB | 1.5 GB | 1.10 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 2.5 GB | 2.6 GB | 2.20 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 TinyDolphin 2.8 1.1B?
Q4_K_M · 1.0 GBTinyDolphin 2.8 1.1B (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 TinyDolphin 2.8 1.1B?
Q4_K_M · 1.0 GB33 devices with unified memory can run TinyDolphin 2.8 1.1B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does TinyDolphin 2.8 1.1B need?
TinyDolphin 2.8 1.1B requires 1.0 GB of VRAM at Q4_K_M, or 2.5 GB at BF16.
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)
KV Cache + Overhead ≈ 0.4 GB (at full 4K context)
VRAM usage by quantization
Q4_K_M1.0 GBQ4_K_M + full context1.1 GB- What's the best quantization for TinyDolphin 2.8 1.1B?
For TinyDolphin 2.8 1.1B, Q4_K_M (1.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (1.1 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 GBQ4_K_M ★1.0 GBQ5_K_M1.1 GBQ6_K1.3 GBQ8_01.4 GBBF162.5 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run TinyDolphin 2.8 1.1B on a Mac?
TinyDolphin 2.8 1.1B 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 TinyDolphin 2.8 1.1B locally?
Yes — TinyDolphin 2.8 1.1B 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 TinyDolphin 2.8 1.1B?
At Q4_K_M, TinyDolphin 2.8 1.1B 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)
~2886 tok/s~649 tok/s~2157 tok/s~1784 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of TinyDolphin 2.8 1.1B?
At Q4_K_M, the download is about 0.66 GB. The full-precision BF16 version is 2.20 GB. The smallest option (Q2_K) is 0.47 GB.
- Which GPUs can run TinyDolphin 2.8 1.1B?
35 consumer GPUs can run TinyDolphin 2.8 1.1B at Q4_K_M (1.0 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 TinyDolphin 2.8 1.1B?
33 devices with unified memory can run TinyDolphin 2.8 1.1B at Q4_K_M (1.0 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.