Mingxin

Evidence Library

Every key performance figure on this site comes from the signed / official test reports below (R1–R9). Each report’s scope, test platform and headline measurements are published free — click a report title to read them. The report PDF itself (full method, per-step raw data, charts, screenshots and signatures) is sold per report. The test code, load clients, orchestration scripts and raw data (R8) allow independent third-party reproduction of all conclusions. We label every number by provenance: measured / vendor / public / estimated.

Primary test platform (R1–R4): 8× AMD Instinct MI308X (192 GB HBM each, gfx942); 2× AMD EPYC 9654 (384 threads), ≈1.5 TB RAM; ROCm 7.2; vLLM 0.20.1+rocm721. Device under test: Mingxin FX100 all-flash NVMe-oF array.
Need the full chain of evidence? The complete library is $5 (7 reports, 14 files, Chinese + English) — cheaper than buying individually.See pricing
R12026-07-03

FX100 Comprehensive LLM Inference & Training Benchmark (8× AMD MI308X)

LMCache parallel-read patch, multi-GPU KV tiering scale-out, concurrent model loading, model-switch effective TPS, training checkpoint concurrent writes (Qwen2.5-32B/7B).

Original file: test/AISSD5000-综合性能测试报告-AMD-MI308X.pdf

R22026-07-05

FX100 KV-Cache Benchmark (480B, TP8 long-context, signed)

Qwen3-Coder-480B-FP8, single 8-GPU instance (TP=8 standard production topology), long-context cold recovery, three-way comparison across concurrency levels.

Original file: test/AISSD5000-KVCache性能测试报告(480B·TP8长上下文·正式版).pdf

R32026-07-06

FX100 KV-Cache Benchmark Summary (480B, TP4×2, all metrics, brand-unified edition)

Dual 4-GPU instances, nine load levels, full metrics (TTFT p50/p90/p99, TPOT, throughput, disk bandwidth); fs:// shared-pool cross-instance hot sharing verified; two independent runs within 5% deviation.

The hosted PDF is the brand-unified edition: the product is named FX100 throughout, the layout was rebuilt, and charts plus a numerical-verification appendix were added. All nine load levels match the signed original cell for cell — no measured value was added, changed, or recomputed. The signed original uses the legacy product name and is not hosted publicly to avoid confusion with the FX naming; it is available on request. Its MD5 (A66F5491F075F502BB0B2E2EFB0C101C) is printed inside this edition, so you can confirm you received the same file and then re-verify every number with fx_report/verify_r3.py.

Original file: test/AISSD5000-KVCache性能测试报告(480B·TP4x2·全指标汇总·正式版).pdf

R42026-07-06

FX100 KV-Cache Benchmark (480B, multi-instance, official, No.-006)

Official compilation of R2 + R3 issued by a third-party testing organization.

Original file: test/AISSD5000-KVCache性能测试报告(480B·多实例形态·正式版).pdf

R52026-07-03

FX100 KV-Cache Benchmark (14B, HBM efficiency, official, No.-004)

Seven-way comparison (GDS direct read / two re-compute HBM tiers / low-HBM direct read); three-step HBM-equivalence argument. Platform: MetaX N260 single GPU — methodology portable across platforms, numbers not mixed with MI308X.

Original file: test/AISSD5000-KVCache性能测试报告(14B·显存效益·正式版).pdf

R62026-07-07

ComfyUI + LTX-Video 2.3 Full Deployment & Adaptation Report (V2)

All 7 delivered models (3 VAE / fp8 text encoder / text projection / 2 LoRA) running end-to-end on MI308X / ROCm 7.2 with no dtype, operator, or kernel errors.

Original file: test/ComfyUI-LTX2.3-完整报告(全模型·部署适配使用·V2).pdf

R72026-07-07

ComfyUI + LTX-Video 2.3 Model Adaptation Report (AMD MI308X)

Per-model runs and adaptation notes for 3 VAE + Singularity LoRA.

Original file: test/ComfyUI-LTX2.3-模型适配报告(AMD-MI308X)(1).pdf

R82026-07

FX100 KV-Cache AMD Code Export + Raw Benchmark Data

LMCache parallel-read patch (git patch + full before/after), load clients, orchestration and forensic scripts, raw data — enables full third-party reproduction (contact us for access).

Original file: test/AISSD5000-KVCache-AMD代码导出包.zip; test/KVCache测试结果数据.rar

R92026-05-30

Mingxin FX100-HBMM vs NFS Baseline on Huawei Ascend 910B

Huawei Atlas 910B ×8 (Kunpeng-920): model-serving load (DeepSeek-32B/70B), training weight/checkpoint I/O (Qwen-7B), training-data acceleration (YOLOv8/COCO) — three groups against an NFS baseline (Ascend platform, labeled as such; contact us for access).

Original file: test/微算WS-HBMM5000与华为910B模型推理与训练性能测试.docx

Each paid report includes two files: the signed Chinese original and an English reference translation (AI-produced, with machine-verified number fidelity — every numeric value in the source is asserted to appear in the translation; QC logs are published in the open repository under dmkt/report_i18n/qc/). The signed Chinese originals remain the authoritative versions — figures, screenshots, signatures and stamps are only in the originals. One piece of research is never charged twice by language. R8 and R9 have no hosted file; email us for access.

Naming note: FX100 appears in historical report filenames as AISSD5000 (also WS5000 / GP5000) — all refer to the same product; this site uses the unified FX naming while report entries keep original filenames for verification. 中文版:/evidence