Speculative Decoding: Using LLMs Efficiently¶
Speculative decoding makes large language models (LLMs) work more efficiently.
Large language models are transforming how we write code, but running them efficiently remains a challenge. Even with powerful hardware, code completion can feel sluggish, breaking our concentration just when we need it most. The bottleneck isn't necessarily computational power - it's how efficiently we use it. This is where speculative decoding comes in.
How Speculative Decoding Works: A Practical Approach¶
Think of speculative decoding like having two developers working together: a junior dev who quickly drafts code, and a senior dev who reviews and corrects it. Instead of the senior dev writing everything from scratch, they can review multiple lines at once, saving significant time.
Two models works in tandem. A smaller, faster model (like Qwen 2.5 Coder 1.5B or 3B) quickly generates potential completions, while a larger, more capable model (like Qwen 2.5 Coder 14B) verifies these completions. This approach makes better use of modern hardware in two key ways: while the large model is loading its weights from memory, the smaller model is already generating suggestions, and the large model can verify multiple tokens in parallel rather than generating them one at a time.
The result - code completion that's 2-4 times faster, with the same quality you'd get from the larger model alone.
The Technical Details¶
The memory bottleneck in large language models (LLMs) arises due to their autoregressive nature and substantial parameter counts. LLMs generate text one token at a time, and each token's generation requires accessing all the model's parameters. This process is memory-bound because the speed is limited by how quickly the model can read its parameters from memory (DRAM).
Speculative decoding mitigates this bottleneck by using a smaller, faster draft model to predict multiple tokens, which are then verified in parallel by the larger target model. This approach is efficient because:
Reduced Memory Access: Instead of generating each token sequentially with the target model, Speculative decoding allows the target model to process multiple draft tokens in a single pass. This reduces the number of times the target model's parameters need to be read from memory, alleviating the memory bottleneck.
Parallel Processing: The target model can evaluate multiple draft tokens in parallel. The target model processes all speculative tokens in one model pass (as a batch), which means that the parameters of the model are read only once per pass. This leverages the parallelizability of the transformer network.
Compute and Bandwidth Balance: Speculative decoding trades off compute for memory bandwidth. LLMs are often bandwidth-limited, meaning their performance is constrained by memory access speed rather than computational power. Speculative decoding uses the available compute to reduce the bandwidth requirements.
The efficiency of Speculative decoding depends on several factors:
Draft Model Size: The draft model needs to be significantly smaller than the target model so that it can generate draft tokens quickly.
Draft Length: The number of draft tokens generated in each step needs to be optimised. Longer draft lengths could lead to greater speedups but also increase the likelihood of rejection.
Acceptance Rate: The draft model needs to be well-aligned with the target model so that a sufficient number of draft tokens are accepted.
Setting Up Faster Code Completion in VS Code¶
Through the llama-vscode extension together with llama.cpp HTTP server Speculative decoding can be brought directly to your development environment. It offers intelligent suggestion handling, with the ability to accept suggestions using Tab, accept the first line with Shift+Tab, or take the next word with Ctrl/Cmd+Right. The extension manages context intelligently, considering code from open files and recently edited text, while remaining resource-efficient through clever context reuse.
Here's a configuration that leverages speculative decoding with the Qwen Coder models:
llama-server.exe -md qwen2.5-coder-3b-q8_0.gguf -m qwen2.5-coder-14b-q8_0.gguf --port 8012 -c 2048 --n-gpu-layers 99 -fa -ub 1024 -b 1024 -dt 0.1 --ctx-size 0 --cache-reuse 256
Key configuration options:
-md: Specifies the draft model (3B version)-m: Specifies the main model (14B version)
Real-World Benefits for Developers¶
The performance improvements from speculative decoding are significant form developers interacting with AI coding assistants. With response times cut by 2-4x, suggestions appear as you type, maintaining your coding flow rather than interrupting it. This responsiveness comes with an added benefit: everything runs locally on your development machine, enhancing privacy while reducing cloud costs.
Developers no longer need to choose between running a smaller model for speed or a larger model for quality - they can have both. Even on mid-range development machines, developers can run sophisticated code completion locally. This local processing not only improves privacy and security but also reduces costs compared to cloud-based solutions.
Getting started is fairly straightforward: install the llama-vscode extension, download llama.cpp HTTP server (llama-server.exe), download the Qwen Coder models (1.5B or 3B for the draft model and 7B or 14B versions) for the main model depending on your available memory. Use the configuration provided above to start the server, and VS Code will connect to the local server.
Looking Ahead¶
Speculative decoding is more than just an optimization - it's a view into the future of AI-assisted development. As models continue to grow in size and capability, techniques like this become increasingly important for making them practical in real-world development environments as this addresses the memory-bandwidth bottleneck inherent in LLMs.
As generative AI adoption grows, and the demands on computing infrastructure increase, having some of your AI processing on-device is becoming more important.
What's Next?¶
Speculative decoding is already showing practical benefits in several areas. In code completion, as demonstrated with the VS Code implementation, it's making local processing more responsive and practical. The technique is being successfully applied to general text generation tasks, significantly reducing latency. We're also seeing implementations emerge for chat completions, particularly for running chat models locally. Any task requiring long-form content generation benefits from the improved token generation speed. We'll see more adoption with phone on-device AI processing becoming increasingly important for privacy, security, and personalisation.
Speculative decoding is being introduced to other LLM products, such as LM Studio in their latest beta version. The AI Overviews in Google search are using Speculative decoding to speed up the generation of the overview.
The key insight isn't just about making things faster - it's about making better use of the computing resources we already have. As more applications move towards local processing for privacy and cost reasons, techniques like speculative decoding become increasingly valuable.
Conclusion¶
Speculative decoding represents a significant improvement in how we can leverage AI for development. While the technical details are fascinating, the real impact is practical: developers can access high-quality code completion without sacrificing speed or privacy.
The implications are far-reaching. You don't have to choose between model quality and response time anymore. Local processing is becoming increasingly viable for AI-assisted development, and better hardware utilization means more developers can access advanced AI tools. Perhaps most importantly, this approach shows how thoughtful engineering can solve apparent trade-offs in AI deployment. Instead of waiting for even more powerful hardware or accepting compromises in model size, we can make better use of what we already have.
For teams considering AI-assisted development, speculative decoding offers a practical path forward: high-quality suggestions with the speed and privacy of local processing. As we continue to explore ways to make AI more accessible and useful for development, techniques like this will be crucial in bridging the gap between capability and usability.
References¶
- Speculative Decoding Paper
- Google: Looking back at speculative decoding
- llama-vscode
- llama.cpp HTTP server
- Qwen2.5-Coder-14B-Q8_0-GGUF model
- Qwen2.5-Coder-7B-Q8_0-GGUF model
- Qwen2.5-Coder-3B-Q8_0-GGUF model
- Qwen2.5-Coder-1.5B-Q8_0-GGUF model
Chris Thomas is an AI consultant helping organizations validate and implement practical AI solutions.