Summary
Fahd Mirza walks through a complete hands-on reproduction of DeepSeek’s DeepSpark speculative decoding results on a single NVIDIA RTX A6000 (48 GB VRAM), using the newly open-sourced DeepSpeed toolkit that DeepSeek released alongside its full model code, training scripts, and evaluation tools.
DeepSpark is the inference acceleration technique behind the up-to-85% speed increase reported on DeepSeek’s V4 models. It works by pairing a tiny 1-billion-parameter drafter model with a large target model (Qwen3 34B in this video). The drafter speculates several tokens ahead using a memory-retention mechanism that prevents early guesses from degrading, and a scheduler that checks only the most promising candidates — making verification cheap while keeping outputs identical to a standard forward pass.
Mirza installs the repo, patches a missing prettytable dependency the DeepSeek team omitted from their requirements file, and runs the eval script trimmed to two benchmarks (GSM8K structured math and MT-bench open chat) at 20 samples each. The results are striking: an accept length of 6.0 on GSM8K and 3.65 on MT-bench — matching DeepSeek’s published figures of 6.11 and 3.64 almost exactly. He explains why chat tasks score lower (harder to predict, shorter accepted sequences) and what the accept-length number means in practice: six accepted tokens per pass translates directly to roughly six times fewer expensive forward passes through the large model. Total VRAM during inference stays under 12 GB.
📺 Source: Fahd Mirza · Published June 29, 2026
🏷️ Format: Hands On Build
