AI SYSTEMS & PERFORMANCE ENGINEER
I build and optimize the parts of AI systems where execution cost accumulates: inference paths, GPU kernels, retrieval, and runtime internals. My work connects a focused code change to a clearly scoped, measured result.
Batch-time speedup versus prefix recomputation.
NVIDIA A40 · 16K · C=8.
Best tested kernel configuration.
M4 Max · proposed upstream patch.
Against a vectorized NumPy path.
Single thread · one orbital.
Inference paths, cache lifecycles, memory traffic, and workload-specific performance.
Compiled numerical routines, GPU dispatch, lexical ranking, and filtered search.
Four workload-specific results across persistent inference caching, GPU dispatch, concurrent model serving, and compiled scientific computation.
NVIDIA / SERVING
Published benchmarkbatch wall-time speedup
A40 · Qwen2.5-7B · BF16 · 16K prefix · concurrency 8
23.66 s to 5.40 s versus forced prefix recomputation. Same-process native-hot caching was faster at 2.42 s. Three repetitions per cell.
Built the external cache connector and its benchmark / lifecycle validation.
github.com/thyn-ai/telys-vllmGPU / QUANTIZED MATMUL
Open upstream PRlower latency in the best tested cell
M4 Max · 4-bit weights · BF16 activations · M=2048
Best of five tested configurations; reported range 5.6–38.1% lower latency. A kernel result, not an end-to-end model speedup. Submitted PR.
Authored the proposed tile-dispatch optimization and regression coverage.
github.com/ml-explore/mlx · PR 4826NVIDIA H100 / SERVING
Archived resultaggregate output throughput
H100 80GB · Gemma 3 27B · FP16 · 550 in / 256 out
One H100 at concurrency 50. Recorded in the August 26, 2026 documentation snapshot; not a newly rerun comparison.
Built and benchmarked the serving runtime described in the source documentation.
docs.algenta.ai · NVIDIA servingMOJO / SCIENTIFIC COMPUTE
Published benchmarkspeedup versus the vectorized baseline
M4 Max · single thread · benzene / 6-31G* · 50³ grid
689 ms to 9.5 ms versus the vectorized pyquante2 / NumPy path. A single-orbital grid, not a complete chemistry simulation.
Built the native Gaussian-grid evaluation path and Python integration.
github.com/thyn-ai/mojo-kernelsEvidence snapshot: November 9, 2026. Results are author-published, not independently certified or newly rerun for this brochure. “Open upstream PR” describes the reviewed snapshot; submission does not imply upstream adoption.
Compiled search, bounded memory, physical partitioning, and resident execution. Each result keeps its measurement boundary.
warm-query speedup in the selected cell
M4 Max · 100K docs · 16-code-unit patterns · warm
1,239.508 ms to 32.003 ms versus Fuse.js 7.1.0. Accelerated path uses 16 threads; warm-query measurement.
github.com/thyn-ai/mojo-kernelswarm-query speedup across the tested grid
M4 Max · 1K–100K docs · 5–20 query terms
Versus the best measured bm25s / Numba variant across nine cells. Numerical parity is against rank_bm25, not bm25s.
github.com/thyn-ai/mojo-kernelsat concurrency 8; recall reported as 1.0000
Cohere-100K · 768 dimensions · cosine · 1% filter
Concurrency 8; reported recall 1.0000. The separate 1.5 ms serial p99 is not a concurrency-8 latency. Hardware unspecified in PR summary.
github.com/zilliztech/VectorDBBench · PR 824documented warm finance.npv execution
One representative operation · warm resident runtime
One documented warm finance.npv call; not an agent workflow or a matched benchmark campaign. First-time provisioning excluded.
github.com/thyn-ai/sqai-mcp · documentationpeak RSS in the selected scored test
Repeated-token phrase stress case · scored search
Selected repeated-token phrase stress test. Earlier implementation crossed a 256 MiB watchdog; its exact peak was not measured. Submitted PR.
github.com/quickwit-oss/tantivy · PR 3248
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