Fix: PyTorch 2.10.0, CUDA 12.8 (was incorrectly CUDA 12.4)

README.md

@@ -208,7 +208,7 @@ Some runs produce request failures, typically at high concurrency where the engi
 
 ### Hardware
 
-All benchmarks collected on CUDA 12.4.
+All benchmarks collected on PyTorch 2.10.0, CUDA 12.8.
 
 | GPU | VRAM | HBM bandwidth | Peak half-precision TFLOPS |
 |-----|------|---------------|---------------------------|
@@ -286,7 +286,7 @@ ds = load_dataset("agent-perf-bench/AgentPerfBench", "trace_replay")
 - Metrics: TTFT, TPOT, ITL, E2EL, request throughput, token throughput.
 - Summary statistics: mean, median, p90, p99.
 - Collection period: March 2026 onwards.
-- CUDA 12.4 on all machines. All models served in BF16 (gpt-oss: mxfp4 projection weights).
+- PyTorch 2.10.0, CUDA 12.8 on all machines. All models served in BF16 (gpt-oss: mxfp4 projection weights).
 
 ## Future releases
 
@@ -302,7 +302,7 @@ ds = load_dataset("agent-perf-bench/AgentPerfBench", "trace_replay")
 
 ## Limitations
 
-- Results are specific to tested hardware and software versions (vLLM 0.19.0, SGLang 0.5.9, CUDA 12.4).
+- Results are specific to tested hardware and software versions (vLLM 0.19.0, SGLang 0.5.9, PyTorch 2.10.0, CUDA 12.8).
 - Distributional profiles approximate but do not replicate production traffic patterns.
 - No consumer GPUs beyond RTX 3090; no non-NVIDIA accelerators.
 - Closed-loop concurrency only; no open-loop (Poisson) arrivals.