Qwen3.6 35B-A3B
Paddock vs vLLM vs SGLang vs llama.cpp · Text Generation · RTX PRO 6000 Blackwell Max-Q (96 GB) · 2026-07-20
Hybrid Gated-DeltaNet + gated-attention MoE with a built-in MTP head for speculative decoding and a native vision encoder.
Up to 1.6x
vs vLLM
Faster in 4 of 5 tests
Up to 2x
vs SGLang
Faster in 3 of 5 tests
Up to 4.4x
vs llama.cpp
Faster in 5 of 5 tests
We measure throughput end to end and take the best timed round after a warmup. Each model is tested several ways, from one client sending large requests to many clients hitting the server at once. Every input is unique, so caching can't inflate anyone's numbers.
Measured Throughput
Each engine ran the same tests, in tokens/s. Longer bars are better; the number next to a competitor is how many times faster Paddock was. Every engine here uses speculative decoding: a small model guesses the next few words and the main model checks them in one go, so it produces several words at a time. Paddock does this by default, so the comparison is fair. These numbers rise and fall with the prompt and the load, and usually beat running without it. The dropdown shows the same engines with it off.
Qwen3.6-35B-A3B
Show the same engines without speculative decoding
Speculative decoding is off by default in these engines - it needs a separate helper model and extra setup. This is how they run out of the box. Paddock always uses it, so its bar is unchanged and shown for scale.
Qwen3.6-35B-A3B
Time to First Token
How long a client waits for the first token (median, best round), in milliseconds. Lower is better. The multiplier is how many times lower Paddock's latency is than that engine's on the same test.
| Test | Paddock | vLLM | SGLang | llama.cpp |
|---|---|---|---|---|
| 1 client · 1k-token prompts · 256 tokens out | 83 ms | 88 ms1.1x | 85 ms1.0x | 365 ms4.4x |
| 4 clients · 256-token prompts · 1k tokens out | 65 ms | 171 ms2.6x | 157 ms2.4x | 395 ms6.0x |
| 8 clients · 1k-token prompts · 256 tokens out | 254 ms | 248 ms1.0x | 237 ms0.9x | 819 ms3.2x |
| 8 clients · 4k-token prompts · 128 tokens out | 477 ms | 789 ms1.7x | 677 ms1.4x | 2,178 ms4.6x |
| 32 clients · 1k-token prompts · 256 tokens out | 364 ms | 586 ms1.6x | 619 ms1.7x | 1,541 ms4.2x |
Throughput Under Load
The same results drawn as lines, from a single client on the left to the most concurrent test on the right. The shaded area is the gap between Paddock and the strongest competitor at each point, and the small numbers are Paddock's speedup there.
Qwen3.6-35B-A3B
Test Environment
Hardware
- GPU
- RTX PRO 6000 Blackwell Max-Q, 96 GB
- Driver
- 595.71.05
- CUDA
- 13.0
- OS
- Ubuntu 24.04, Linux 6.8.0-117
Engine Versions
- Paddock
- pre-release build
- vLLM
- 0.25.1
- SGLang
- 0.5.15.post1
- llama.cpp (speculative)
- b10069
Each engine ran the best setup it supports on this hardware, with everything on the GPU.
How We Tested
- Every engine ran on the same machine and GPU, never at the same time.
- The same test program sent identical requests to each engine.
- Every input was unique, so nothing could be served from a cache.
- Each number is the best timed round, after a warmup.
- We measure end to end: what an application connecting over HTTP actually gets.
- Where the engines differ in ways that could affect the comparison, we note it alongside the results.
Check our numbers
Paddock ships a benchmark harness, so you can run the same comparison on your own hardware.