What are LLMs Optimized for Inference Speed?
LLMs optimized for inference speed are specialized large language models designed to deliver rapid responses with minimal computational overhead. These models typically feature smaller parameter counts (7B-9B range), efficient architectures, and optimized serving capabilities that enable fast token generation and low latency. This technology allows developers to deploy powerful AI capabilities in resource-constrained environments, real-time applications, and high-throughput scenarios. They balance performance with efficiency, making advanced language understanding accessible for applications requiring quick responses, from chatbots to production APIs, without the computational cost of larger models.
Qwen/Qwen2.5-VL-7B-Instruct
Qwen2.5-VL-7B-Instruct is a 7 billion parameter vision-language model from the Qwen series, equipped with powerful visual comprehension capabilities and optimized for inference efficiency. It can analyze text, charts, and layouts within images, understand long videos, and capture events. The model features an improved visual encoder with dynamic resolution and frame rate training, making it exceptionally fast for multimodal tasks while maintaining strong reasoning capabilities and supporting multi-format object localization with structured outputs.
Qwen/Qwen2.5-VL-7B-Instruct: Lightning-Fast Multimodal Understanding
Qwen2.5-VL-7B-Instruct is a 7 billion parameter vision-language model from the Qwen series, equipped with powerful visual comprehension capabilities and optimized for inference efficiency. It can analyze text, charts, and layouts within images, understand long videos, and capture events. It is capable of reasoning, manipulating tools, supporting multi-format object localization, and generating structured outputs. The model has been optimized for dynamic resolution and frame rate training in video understanding, and has improved the efficiency of the visual encoder. With a 33K context length and highly competitive pricing at $0.05/M tokens on SiliconFlow, it delivers exceptional speed-to-performance ratio for multimodal applications.
Pros
- Compact 7B parameters enable fast inference speeds.
- Optimized visual encoder for efficient processing.
- Excellent cost-efficiency at $0.05/M tokens on SiliconFlow.
Cons
- Smaller model size may limit complex reasoning depth.
- Vision-language focus may not suit pure text tasks.
Why We Love It
- It delivers blazing-fast multimodal inference with an optimized visual encoder, making it the perfect choice for real-time vision-language applications on a budget.
meta-llama/Meta-Llama-3.1-8B-Instruct
Meta-Llama-3.1-8B-Instruct is an 8 billion parameter multilingual large language model optimized for dialogue and inference speed. This instruction-tuned variant outperforms many open-source and closed chat models on industry benchmarks while maintaining exceptional efficiency. Trained on over 15 trillion tokens with supervised fine-tuning and RLHF, it supports text and code generation across multiple languages with a 33K context window, making it ideal for high-throughput production environments requiring fast response times.
meta-llama/Meta-Llama-3.1-8B-Instruct: Industry-Leading Speed and Multilingual Excellence
Meta Llama 3.1-8B-Instruct is a multilingual large language model developed by Meta, featuring an instruction-tuned 8B parameter architecture optimized for dialogue use cases. This model outperforms many available open-source and closed chat models on common industry benchmarks while delivering exceptional inference speed. The model was trained on over 15 trillion tokens of publicly available data, using techniques like supervised fine-tuning and reinforcement learning with human feedback to enhance helpfulness and safety. Llama 3.1 supports text and code generation with a 33K context length and a knowledge cutoff of December 2023. At $0.06/M tokens on SiliconFlow, it offers outstanding value for production deployments requiring rapid response times.
Pros
- Exceptional inference speed with 8B parameters.
- Outperforms many larger models on benchmarks.
- Multilingual support across diverse languages.
Cons
- Knowledge cutoff limited to December 2023.
- May require fine-tuning for specialized domains.
Why We Love It
- It strikes the perfect balance between speed, quality, and multilingual capability, making it a top choice for high-performance production chatbots and APIs.
THUDM/GLM-4-9B-0414
GLM-4-9B-0414 is a lightweight 9 billion parameter model in the GLM series, offering excellent inference speed while maintaining powerful capabilities. Despite its smaller scale, it demonstrates excellent performance in code generation, web design, SVG graphics generation, and search-based writing tasks. The model supports function calling to extend its capabilities and achieves an optimal balance between efficiency and effectiveness in resource-constrained scenarios, making it ideal for rapid deployment where speed is critical.
THUDM/GLM-4-9B-0414: Compact Power with Blazing Speed
GLM-4-9B-0414 is a small-sized model in the GLM series with 9 billion parameters. This model inherits the technical characteristics of the GLM-4-32B series but offers a more lightweight deployment option optimized for inference speed. Despite its smaller scale, GLM-4-9B-0414 still demonstrates excellent capabilities in code generation, web design, SVG graphics generation, and search-based writing tasks. The model also supports function calling features, allowing it to invoke external tools to extend its range of capabilities. The model shows a good balance between efficiency and effectiveness in resource-constrained scenarios, providing a powerful option for users who need to deploy AI models under limited computational resources. With a 33K context length and priced at $0.086/M tokens on SiliconFlow, it delivers competitive performance in benchmark tests while maintaining rapid inference speeds.
Pros
- Fast inference with only 9B parameters.
- Excellent code generation and technical tasks.
- Function calling support for tool integration.
Cons
- Slightly higher cost than some alternatives.
- May not match larger models in complex reasoning.
Why We Love It
- It delivers enterprise-grade capabilities in a compact, speed-optimized package, perfect for developers needing rapid inference in technical and creative applications.
LLM Speed Comparison
In this table, we compare 2025's fastest LLMs, each optimized for different speed-critical use cases. For multimodal applications, Qwen2.5-VL-7B-Instruct offers the most efficient vision-language processing. For multilingual dialogue at scale, Meta-Llama-3.1-8B-Instruct provides industry-leading speed with broad language support. For technical tasks and code generation, GLM-4-9B-0414 delivers rapid inference with function calling capabilities. This side-by-side view helps you choose the right speed-optimized model for your specific deployment requirements.
| Number | Model | Developer | Subtype | Pricing (SiliconFlow) | Core Strength |
|---|---|---|---|---|---|
| 1 | Qwen/Qwen2.5-VL-7B-Instruct | Qwen | Vision-Language | $0.05/M Tokens | Fastest multimodal inference |
| 2 | meta-llama/Meta-Llama-3.1-8B-Instruct | meta-llama | Multilingual Chat | $0.06/M Tokens | Top-tier speed & benchmarks |
| 3 | THUDM/GLM-4-9B-0414 | THUDM | Lightweight Chat | $0.086/M Tokens | Rapid code generation |
Frequently Asked Questions
Our top three picks for fastest inference in 2025 are Qwen/Qwen2.5-VL-7B-Instruct, meta-llama/Meta-Llama-3.1-8B-Instruct, and THUDM/GLM-4-9B-0414. Each of these models stood out for their exceptional speed, efficiency, and ability to deliver rapid responses while maintaining high-quality outputs in their respective domains.
Our analysis shows Qwen/Qwen2.5-VL-7B-Instruct offers the best cost-efficiency at $0.05/M tokens on SiliconFlow, making it ideal for high-volume multimodal applications. Meta-Llama-3.1-8B-Instruct at $0.06/M tokens provides exceptional value for multilingual chat deployments. For technical tasks requiring function calling, GLM-4-9B-0414 at $0.086/M tokens delivers strong performance while maintaining rapid inference speeds.