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.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~649 tok/s · 4K)Macs from 8 GB (Q4_K_M · ~71 tok/s)
Apache 2.0
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run TinyDolphin 2.8 1.1B
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
vllm serve QuixiAI/TinyDolphin-2.8-1.1b --max-model-len 4096vLLM repo with its own quantisation
Download: Weights on Hugging Face
Ways to runOther ways
each opens step-by-stepRun in cloud
Runs on RTX 3080 Ti (12 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~587 tok/s
- generation speed
- Cost per 1M output tokens
- $0.058
- per 1M output tokens
How Much VRAM Does TinyDolphin 2.8 1.1B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 0.8 GB | — | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~809 tok/s | |
| 0.9 GB | — | 3-bit medium — 3-bit medium quantization | Fits: 4K context · ~745 tok/s | |
| 1.0 GB | — | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 4K context · ~649 tok/s | |
| 1.1 GB | — | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 4K context · ~580 tok/s | |
| 1.3 GB | — | 6-bit, very good — 6-bit quantization, very good quality | Fits: 4K context · ~524 tok/s | |
| 1.4 GB | — | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 4K context · ~452 tok/s | |
| 2.5 GB | — | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~257 tok/s |
Selected Q4_K_M
4-bit medium quantization1.1GBat 4K context
- Weights
- 0.7 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 22.9 GB free · ~649 tok/s
≥50 tok/s needs ≥78 GB/s · RTX 5090 ~1153, RTX 3090 Ti ~649
Which Hardware 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. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 163 devices with unified memory can run TinyDolphin 2.8 1.1B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB).
≤ 11 GB
≤ 11 GB tier →fits · 6.9 GB free · up to 4K ctx
16 GPUs · 1 Mac class · 2 device families
RTX 3080~489Simulate output on RTX 3080RTX 2080 Ti~396Simulate output on RTX 2080 TiRTX 3070 Ti~392Simulate output on RTX 3070 TiM3*8 GB~71Simulate output on iMac 24" M3 (8 GB)Orin Nano~66Simulate output on NVIDIA Jetson Orin Nano 8GB (Super)iPhone~47Simulate output on iPhone 1720 GB
fits · 18.9 GB free · up to 4K ctx
1 GPU
RX 7900 XT~475Simulate output on RX 7900 XT32–40 GB
fits · 30.9 GB free · up to 4K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~1153Simulate output on RTX 5090M5 Max36 GB~319Simulate output on Mac Studio M5 Max (36 GB)M532 GB~107Simulate output on MacBook Air 13" M5 (32 GB)Orin~132Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~87Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to TinyDolphin 2.8 1.1B
Phi family · 9 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| MiniCPM5 1BOpenBMB | — | 1.3 GB | ~662 | same size |
| MinerU2.5 Pro 2605 1.2Bopendatalab | — | 1.1 GB | ~642 | same size |
| MinerU2.5 Pro 2604 1.2Bopendatalab | — | 1.1 GB | ~642 | same size |
| InternVL3 5 1BOpenGVLab | — | 2.2 GB | ~936 | same size |
| TinyLlama 1.1B Chat v1.0TinyLlama | — | 1 GB | ~649 | same size |
| HRM Text 1Bsapientinc | — | 3.1 GB | ~228 | same size |
Specifications
- 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
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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
KV Cache + Overhead ≈ 0.4 GB (at full 4K context)
VRAM usage by quantization
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
★ Recommended — best balance of quality and VRAM usage.
Can I run TinyDolphin 2.8 1.1B on a Mac?
Yes — MacBook Air 13" M3 (8 GB) and 145 other Macs can run TinyDolphin 2.8 1.1B. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 1.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run TinyDolphin 2.8 1.1B locally?
Yes — TinyDolphin 2.8 1.1B can run locally on consumer hardware. 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 ~4753 tok/s on AMD Instinct MI350X. 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: NVIDIA B200 → 8000 ÷ 1.01 × 0.65 = ~5149 tok/s
Estimated speed at Q4_K_M (1.0 GB)
Real-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?
52 consumer GPUs can run TinyDolphin 2.8 1.1B at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 52 GPUs have plenty of headroom for comfortable inference.
Which devices can run TinyDolphin 2.8 1.1B?
163 devices with unified memory can run TinyDolphin 2.8 1.1B at Q4_K_M (1.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (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.