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In a reported test on an NVIDIA RTX PRO 6000 Blackwell workstation, vLLM led vLLM, SGLang and llama.cpp in aggregate BF16 throughput at concurrency 32. The same report found higher vLLM throughput with the Qwen3-8B FP8 checkpoint, though its results describe one setup and have not been independently reproduced here.
What the Blackwell comparison tested
ConatusAI’s 2026 article on DEV Community tested Qwen3-8B on an NVIDIA RTX PRO 6000 Blackwell workstation GPU with 96 GB of memory (sm_120). The compared stacks were vLLM 0.27.1, SGLang 0.5.9 and a CUDA build of llama.cpp. The author says the engines used identical prompts and sampling settings, greedy decoding, and matched output-token counts before timing. Aggregate figures were measured at concurrency 32. Read the reported test and methodology.
These are the article author’s results, not an independent replication. They answer a narrow question—how those builds performed on that workload—not which engine is universally fastest across Blackwell systems, prompts, or load levels.
BF16 results at concurrency 32
The report’s aggregate throughput, median time to first token (TTFT p50) and end-to-end 99th-percentile latency (p99) were:
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| Serving stack | Aggregate throughput | TTFT p50 | End-to-end p99 |
|---|---|---|---|
| vLLM 0.27.1 | 1,725 tok/s | 39 ms | 3.4 s |
| SGLang 0.5.9 | 1,327 tok/s | 42 ms | 5.0 s |
| llama.cpp CUDA build | 428 tok/s | 316 ms | 16.3 s |
All values in the table are figures reported by ConatusAI for the same BF16, concurrency-32 test. In that test, vLLM had the highest aggregate throughput and the lowest reported p99; its TTFT p50 was close to SGLang’s. The llama.cpp result was lower on aggregate throughput and higher on both latency measures. These figures should not be treated as predictions for single-user serving: the article says single-stream performance was similar enough that operational preference may matter more in a one-user setup.
What the FP8 pass adds
ConatusAI also reports an FP8 run on vLLM using the official Qwen3-8B-FP8 checkpoint. The article gives the following figures under its stated settings:
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| vLLM run | Single-stream throughput | Batch throughput | Latency p50 |
|---|---|---|---|
| BF16 | 86 tok/s | 1,725 tok/s | 0.74 s |
| FP8 | 130 tok/s | 2,597 tok/s | 0.49 s |
These are the author’s reported measurements, not an independently verified comparison. The article says it performed a fixed 20-prompt factual check and observed zero regressions. That small check is not evidence that FP8 preserves quality across tasks or prompt distributions.
The author also reports that getting FP8 to run required routing around a DeepGEMM assertion on sm_120 and falling back to a CUTLASS path. This describes that particular software stack and setup; it does not establish a universal Blackwell limitation.
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How to interpret the result for your workstation
Choose a serving stack against the way you will actually use the model. Concurrency 32 rewards aggregate serving capacity, while a single user may care more about responsiveness, straightforward setup and the behavior of a specific build. When reproducing the comparison or making your own decision, keep these variables aligned:
- GPU and memory: the reported figures are for an RTX PRO 6000 Blackwell with 96 GB, not every Blackwell workstation GPU.
- Workload: prompt lengths, generated-token counts, concurrency, sampling and stop behavior all affect throughput and latency.
- Software and kernels: record engine versions, CUDA build details and kernel backend; the FP8 path in the report required a specific fallback.
- Quality: validate FP8 on representative prompts and outputs for your application rather than relying on a 20-prompt check.
- Metrics: compare aggregate throughput alongside TTFT and end-to-end tail latency; a throughput leader is not automatically the best fit for every service.
Official support is not a benchmark match
Qwen’s deployment documentation identifies Qwen/Qwen3-8B-FP8 as a pre-quantized checkpoint and describes Qwen3 FP8 as block-wise quantization supported on NVIDIA GPUs with compute capability above 8.9. It also notes that tensor-parallel divisibility can fail, suggesting a lower tensor-parallel degree or expert parallelism as mitigations. Check the exact GPU, software build and flags before adopting a configuration. Qwen’s vLLM deployment guidance documents the setup.
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The Qwen3-8B-FP8 model card provides vLLM and SGLang serving instructions and lists llama.cpp among local-use applications that support Qwen3. Those are framework and model support statements, not matched performance results on the RTX PRO 6000 Blackwell.
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Other published results should not be blended into this comparison. Qwen’s Qwen3 speed benchmark describes SGLang tests on an NVIDIA H20 96GB using PyTorch 2.6.0+cu124, Transformers 4.51.3, SGLang 0.4.6.post1 and SGL-kernel 0.1.0. Its batch-size-1 tests use several input lengths and generate 2,048 tokens; the page also notes that SGLang memory use is not reported because it pre-allocates GPU memory. The different GPU, versions and workload make it unsuitable as direct confirmation of the workstation ranking. NVIDIA’s DGX Spark SGLang page lists Qwen3-8B FP8 and NVFP4 variants validated for that separate platform; that does not establish performance or compatibility for this exact workstation setup.
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Practical takeaway
For the reported concurrency-32 BF16 workload, vLLM is the throughput leader, with SGLang second and llama.cpp CUDA third. The reported FP8 pass improves the vLLM figures, but its small quality check and setup-specific kernel workaround call for validation on your own prompts and build. The RTX PRO 6000 Blackwell is the test card, not an established requirement for running Qwen3-8B.
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