Single RTX 3090: Qwen3.8 27B at 78-140 tok/s with the OpenJet megakernel vs llama.cpp
| GPU(s) | RTX 3090 Amazon · eBay (used) · Newegg |
|---|---|
| VRAM / unified memory (GB) | 24 |
| CPU | |
| Motherboard | |
| RAM | |
| PSU | |
| Other | |
| Reported total cost | |
| Power |
Measured speed (generation tokens/sec)
| Model | Quant | Engine | tok/s |
|---|---|---|---|
| Qwen3.8 27B | Q4_K_M (unsloth), greedy code prompt | OpenJet megakernel (MTP, 4 drafts) | 139.7 |
| Qwen3.8 27B | Q4_K_M (unsloth), greedy code prompt | llama.cpp (MTP, 2 drafts) | 72.5 |
| Qwen3.8 27B | Q4_K_M (unsloth), reasoning mode | OpenJet megakernel (MTP) | 78.0 |
| Qwen3.8 27B | Q4_K_M (unsloth), reasoning mode | llama.cpp (MTP, 2 drafts) | 50.9 |
| Qwen3.8 27B | Q4_K_M (unsloth), no speculation | llama.cpp | 40.4 |
| Qwen3.8 27B | Q4_K_M (unsloth), no speculation | OpenJet megakernel | 42.9 |
From the OpenJet megakernel README (r/LocalLLaMA post, Oct 2026). Single conversation, back-to-back on an idle GPU. The 139.7/72.5 figures are the best-case 'Python function' greedy prompt; the 4-prompt greedy mean is 106.7 vs 63.1 tok/s. Speedups come from speculative decoding (MTP); without it both engines run ~40-43 tok/s. Works only with specific Qwen3.8-27B Q4_K_M / UD-Q4_K_XL GGUFs.
Source: https://github.com/L-Forster/open-jet/tree/master/megakernel
Blank fields = not stated in the source. We never estimate build data; GPU street prices above are separately sourced and dated.
Rent before you buy: try this model on a cloud GPU for a few dollars first: RunPod
Quick answers: Can RTX 3090 run Qwen3.8 27B?