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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minuteIn a reported test on an NVIDIA RTX PRO 6000 Blackwell workstation with 96 GB of memory, vLLM led SGLang and CUDA-built llama.cpp on aggregate BF16 throughput at concurrency 32. The same report found higher throughput in a vLLM FP8 run. Those results describe one model, GPU, software stack and workload—not a universal ranking. For a single-user setup, the report says performance was similar enough that operational preference may matter more.
What the Blackwell comparison actually tested
ConatusAI’s 2026 DEV Community article tested Qwen3-8B on an RTX PRO 6000 Blackwell workstation GPU with 96 GB of memory (sm_120). It compared vLLM 0.27.1, SGLang 0.5.9 and a CUDA build of llama.cpp. The reported aggregate figures used concurrency 32.
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The author says the engines used identical prompts and sampling settings, greedy decoding and matched output-token counts before timing. The article reports raw CSVs and a reproduction script, but the results below remain the author’s measurements; they have not been independently reproduced here. Read the ConatusAI benchmark report.
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These are the aggregate throughput, median time to first token (TTFT p50) and end-to-end 99th-percentile latency reported for the test workload.
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- [Double-Flow-Through Design] The RTX PRO 6000 Blackwell features a double-flow-through cooling design, optimizing efficiency and airflow to sustain peak performance under 600W power loads. | [5th Gen Tensor Cores] Deliver up to 3X the performance of the previous generation and support for FP4 precision for faster AI model processing times with reduced memory usage, enabling local fine-tuning of LLMs and generative AI | [4th Gen Ray Tracing Cores] Double the ray-triangle intersection rate of the previous generation to create photoreal, physically accurate scenes and immersive 3D designs with RTX Mega Geometry, which enables up to 100X more ray-traced triangles.
- [PCIe Gen 5] Support for PCIe Gen 5 provides double the bandwidth of PCIe Gen 4, improving data-transfer speeds from CPU memory and unlocking faster performance for data-intensive tasks like AI, data science, and 3D modeling. | [GDDR7 Memory] With 96 GB of GPU memory and 1.8 TB ps bandwidth, it can tackle massive 3D and AI projects, fine-tune AI models locally, explore large-scale VR environments, and drive larger multi-app workflows.
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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 figures in the table are ConatusAI’s reported results for Qwen3-8B on the RTX PRO 6000 Blackwell workstation at concurrency 32, not general engine specifications. Under that particular BF16 workload, vLLM delivered the highest aggregate throughput and lowest reported tail latency. SGLang was closer to vLLM on TTFT p50 than on aggregate throughput; llama.cpp was lower on throughput and higher on both reported latency measures.
What changed in the vLLM FP8 run
ConatusAI also compared a vLLM BF16 run with the official Qwen3-8B-FP8 checkpoint using the same reported settings. The article reports the following:
| vLLM run | Single-stream throughput | Batch throughput | Latency p50 |
|---|---|---|---|
| BF16 | 86 tok/s | 1,725 tok/s | 0.74 s |
| Official FP8 checkpoint | 130 tok/s | 2,597 tok/s | 0.49 s |
These are the article author’s measurements, not independently verified results. The author also says a fixed factual check of 20 prompts showed zero regressions. That small check is not evidence of broad quality parity: it does not establish how FP8 behaves across other prompts, evaluation sets or real applications.
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The report says its FP8 run required routing around a DeepGEMM assertion on sm_120 and falling back to a CUTLASS path. Treat that as a detail of the described stack and setup, not as proof that FP8 currently fails on all Blackwell workstations or with every software build.
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Does this tell you which engine to use?
If your workload resembles the reported concurrency-32 test and throughput under load is the priority, the benchmark points to vLLM as the leading option among the three tested. It does not establish that vLLM will win with your prompt lengths, output lengths, batching, model-serving configuration or software versions. The article says single-stream performance was similar enough that the operator’s preference may matter more for a one-user setup.
- For a busy server: use aggregate throughput and tail latency as decision measures, then test with your own request mix and concurrency.
- For one user: compare interactive latency and operational fit rather than assuming the batch-throughput result predicts your experience.
- For FP8: check that the exact checkpoint, GPU, engine build and kernel backend work together, then evaluate output quality on representative tasks.
Qwen’s official materials document an FP8 checkpoint and serving paths, but framework support is not a guarantee that every combination of GPU, build and flags will run cleanly. The RTX PRO 6000 is the card used in this comparison, not a stated requirement for running Qwen3-8B.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How to judge whether another benchmark is comparable
A useful comparison needs more than engine names and a tokens-per-second figure. Check that the measured runs match on the conditions that can change throughput and latency:
- Exact GPU model and memory, engine versions, build options and kernel backend.
- Model checkpoint and precision, including whether quantization is pre-applied or handled by the runtime.
- Prompt lengths, generated output lengths, sampling and stop behavior, and whether generated token counts are matched.
- Concurrency and whether the result represents single-stream performance or aggregate throughput under load.
- Latency definitions and percentiles, such as TTFT p50 and end-to-end p99, rather than throughput alone.
Qwen’s published speed benchmark is useful context for methodology, but it is not a confirmation of this workstation ranking. Its listed SGLang evaluation used an NVIDIA H20 96GB, PyTorch 2.6.0+cu124, Transformers 4.51.3, SGLang 0.4.6.post1 and SGL-kernel 0.1.0. It tested batch size 1 at several input lengths while generating 2,048 tokens; it also notes that SGLang memory use was not reported because it pre-allocates GPU memory, and that FP8 performance in Transformers was not then optimal. Different hardware, versions and workloads make those numbers unsuitable for combining with the Blackwell results. See Qwen’s speed-benchmark methodology.
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What official FP8 support does—and does not—establish
Qwen’s vLLM deployment documentation identifies the pre-quantized checkpoint as Qwen/Qwen3-8B-FP8. It describes Qwen3 FP8 as block-wise quantization supported on NVIDIA GPUs with compute capability above 8.9. It also documents a tensor-parallel divisibility failure mode and suggests a lower tensor-parallel degree or expert parallelism as mitigations. Check compatibility and flags against the exact GPU and software build you plan to use; the documentation does not certify every workstation configuration. Read Qwen’s vLLM deployment notes.
The Qwen3-8B-FP8 model card describes fine-grained FP8 quantization with block size 128 and provides vLLM and SGLang serving instructions. It also lists llama.cpp among local-use applications that support Qwen3. These are support and usage statements, not matched performance measurements on the RTX PRO 6000 Blackwell. View the Qwen3-8B-FP8 model card.
NVIDIA lists Qwen3-8B FP8 and NVFP4 variants validated for DGX Spark with SGLang. DGX Spark is a separate platform; its validation does not establish the result or compatibility of the workstation stack described above. See NVIDIA’s DGX Spark SGLang page.
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