Quick Verdict: Speculative Decoding vs Continuous Batching
Speculative decoding reduces per-request latency by using a small draft model to predict multiple tokens, then verifying them in a single forward pass of the large model. Continuous batching increases throughput by dynamically adding and removing requests from the running batch without waiting for the longest sequence to complete. They solve different problems and combine well. On dedicated GPU hosting, continuous batching (enabled by default in vLLM) should always be active, while speculative decoding is added when single-request latency must be minimised.
How Each Technique Works
Continuous batching groups multiple inference requests into a single GPU operation. Unlike static batching, which waits for all sequences in a batch to finish before processing new ones, continuous batching inserts new requests as soon as any sequence completes. This keeps the GPU saturated and eliminates idle time. vLLM implements this as its default scheduling strategy. See the vLLM production guide for configuration.
Speculative decoding uses a small model (1-7B parameters) to generate N candidate tokens quickly, then the large target model verifies all N tokens in a single forward pass. If 4 of 5 candidates are correct, the model effectively generates 5 tokens in the time of 1 forward pass. Acceptance rates typically range 60-85% depending on draft model quality.
Performance Impact
| Metric | No Optimisation | Continuous Batching | Speculative Decoding | Both Combined |
|---|---|---|---|---|
| Single Request Latency | Baseline | No change | 40-60% faster | 40-60% faster |
| Throughput (tok/s, 32 users) | Baseline | 3-5x higher | 1.5-2x higher | 4-8x higher |
| GPU Utilisation | 30-50% | 80-95% | 50-70% | 85-95% |
| Additional VRAM Required | None | None | 2-8 GB (draft model) | 2-8 GB |
| Implementation Complexity | None | Built into vLLM | Requires draft model selection | Moderate |
When Each Technique Helps
Continuous batching is most impactful under concurrent load. A single user sees no benefit. At 10 concurrent users, throughput can triple compared to sequential processing. At 50 users, the difference is 5x or more. Every production LLM deployment should use continuous batching. See token speed benchmarks for concurrency scaling data.
Speculative decoding shines for single-user or low-concurrency scenarios where per-request latency matters. An interactive chatbot serving one user at a time benefits enormously. At high concurrency, the draft model consumes GPU compute that could serve more requests, reducing the net benefit. Choose your deployment style based on GPU capabilities and traffic patterns.
Combining Both Techniques
vLLM supports both simultaneously. Continuous batching handles request scheduling while speculative decoding accelerates individual requests within the batch. The draft model adds 2-8GB of VRAM overhead, so ensure your GPU has headroom. On multi-GPU clusters, one GPU can run the draft model while others handle the target model for optimal resource allocation. Review engine comparisons for implementation differences.
Recommendation
Always enable continuous batching (it is on by default in vLLM). Add speculative decoding when your application is latency-sensitive and you have VRAM headroom for the draft model. Test acceptance rates with your specific model pair before deploying to production. Deploy on GigaGPU dedicated servers with private AI hosting for optimised inference. Explore the benchmarks section for performance data across configurations.
