DialoGPT Small — Hardware Requirements & GPU Compatibility
Chat
DialoGPT Small is a 176M-parameter open language model from Microsoft. It supports a context window of up to 1,024 tokens. At Q4_K_M it needs about 0.12 GB of VRAM — see which GPUs and Macs can run it below.
57.5K downloads
146 likesSpecifications
- Publisher
- Microsoft
- Parameters
- 176M
- Architecture
- GPT2LMHeadModel
- Context Length
- 1,024 tokens
- Vocabulary Size
- 50,257
- Release Date
- 2024-02-29
- License
- MIT
Get Started
HuggingFace
How Much VRAM Does DialoGPT Small Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_S | 3.50 | 0.1 GB | — | 0.08 GB | 3-bit small quantization |
| Q2_K | 3.40 | 0.1 GB | — | 0.07 GB | 2-bit quantization with K-quant improvements |
| Q3_K_M | 3.90 | 0.1 GB | — | 0.09 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 0.1 GB | — | 0.11 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 0.1 GB | — | 0.13 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 0.2 GB | — | 0.14 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 0.2 GB | — | 0.18 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run DialoGPT Small?
Q4_K_M · 0.1 GBDialoGPT Small (Q4_K_M) requires 0.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 1+ 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 DialoGPT Small?
Q4_K_M · 0.1 GB33 devices with unified memory can run DialoGPT Small, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does DialoGPT Small need?
DialoGPT Small requires 0.1 GB of VRAM at Q4_K_M, or 0.2 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 176M × 4.8 bits ÷ 8 = 0.1 GB
VRAM usage by quantization
Q4_K_M0.1 GB- What's the best quantization for DialoGPT Small?
For DialoGPT Small, Q4_K_M (0.1 GB) offers the best balance of quality and VRAM usage. Q5_K_S (0.1 GB) provides better quality if you have the VRAM. The smallest option is IQ3_S at 0.1 GB.
VRAM requirement by quantization
IQ3_S0.1 GBQ2_K0.1 GBIQ4_XS0.1 GBQ4_K_M ★0.1 GBQ5_K_S0.1 GBQ8_00.2 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run DialoGPT Small on a Mac?
DialoGPT Small requires at least 0.1 GB at IQ3_S, 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 DialoGPT Small locally?
Yes — DialoGPT Small can run locally on consumer hardware. At Q4_K_M quantization it needs 0.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is DialoGPT Small?
At Q4_K_M, DialoGPT Small can reach ~24292 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~5460 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 ÷ 0.1 × 0.55 = ~24292 tok/s
Estimated speed at Q4_K_M (0.1 GB)
~24292 tok/s~5460 tok/s~18157 tok/s~15019 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of DialoGPT Small?
At Q4_K_M, the download is about 0.11 GB. The full-precision Q8_0 version is 0.18 GB. The smallest option (IQ3_S) is 0.07 GB.
- Which GPUs can run DialoGPT Small?
35 consumer GPUs can run DialoGPT Small at Q4_K_M (0.1 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 DialoGPT Small?
33 devices with unified memory can run DialoGPT Small at Q4_K_M (0.1 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.