docs: add dataset card with findings, methodology, and reproduction notes

README.md

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+---
+license: apache-2.0
+tags:
+  - benchmark
+  - llm-inference
+  - mixture-of-experts
+  - tensor-parallelism
+  - consumer-gpu
+  - freetoken
+  - qwen
+pretty_name: FreeToken Qwen3.6-35B-A3B Dual RTX 3090 Benchmarks
+---
+
+# FreeToken Qwen3.6-35B-A3B — Dual RTX 3090 Consumer Benchmarks
+
+Measured inference performance of **Qwen3.6-35B-A3B** (FP8 / NVFP4 / BF16 checkpoints) served by
+[FreeToken](https://github.com/FlashML-org/FreeToken) on a **consumer dual-GPU workstation**
+(2x RTX 3090 24 GB, 62 GB RAM), across quantization formats, tensor-parallel configurations,
+MoE backends, RAM bandwidth regimes, and three engine revisions — including validation of two
+upstream fixes that landed during testing.
+
+This is, to our knowledge, the **first published set of consumer dual-GPU numbers for this stack**.
+All figures were measured on real hardware; methodology and provenance below.
+
+## Hardware / software environment
+
+| Component | Value |
+|---|---|
+| GPU | 2x NVIDIA RTX 3090 24 GB (Ampere, sm_86), PCIe |
+| CPU | Intel Core i9-10980XE, 18c/36t (X299 platform) |
+| RAM | 62 GB DDR4, tested at 2000 MT/s (JEDEC) and 3600 MT/s (XMP) |
+| OS | Ubuntu 24.04 |
+| Driver / CUDA | 580.173.02 / CUDA 13.0 (official toolkit — see reproduction notes) |
+| NCCL | 2.31.2 (`libnccl2` + `libnccl-dev`) |
+| Engine | FreeToken v0.1.2 (PyPI) + `freetoken_kernel_cache` 0.1.2+cu130; later PR #104 head `3c8b281` + stacked branch (`f1ff8e2`, then `c23ec78`) |
+| Models | `nvidia/Qwen3.6-35B-A3B-FP8` (35 GB), `nvidia/Qwen3.6-35B-A3B-NVFP4` (22 GB), BF16 (~70 GB, OOM on this host) |
+
+## Headline findings
+
+### 1. Config ladder (decode throughput, tok/s)
+
+| Config | Engine revision | Decode tok/s | Verdict |
+|---|---|---|---|
+| FP8, TP=1, offload | v0.1.2 | **93.9** | Speed champion on 2x 3090 |
+| NVFP4, TP=1, hybrid | v0.1.2 | 61.0 → **~79** (RAM 3600 MT/s) | CPU/RAM-bound |
+| NVFP4, TP=2, hybrid | PR #104 @ `f1ff8e2` | 2.2 | Broken: CPU executor core collision |
+| NVFP4, TP=2, hybrid + `--moe-cpu-threads 8` | PR #104 @ `f1ff8e2` | 9.5 | Workaround (4.3x) |
+| NVFP4, TP=2, offload | PR #104 @ `f1ff8e2` | 3.1 | PCIe + host all-reduce serialization |
+| **NVFP4, TP=2, hybrid** | **PR #104 @ `c23ec78`** | **32.7 wall / 59.3 engine-instantaneous** | Affinity fix verified: **14.9x vs broken** |
+| FP8, TP=2 | PR #104 @ `f1ff8e2` | — | OOM at load (62 GB RAM < ~35 GB banks + runtime) |
+| BF16, TP=2 | PR #104 | — | OOM (67 GB model > 62 GB RAM) |
+
+### 2. KV-cache capacity is the real TP=2 prize
+
+| TP | Usable KV tokens |
+|---|---|
+| 1 | 262,144 |
+| 2 | **524,288** (2.4x claim confirmed) |
+
+### 3. RAM bandwidth scaling (2000 → 3600 MT/s)
+
+| Metric | 2000 MT/s | 3600 MT/s | Change |
+|---|---|---|---|
+| CPU STREAM read | 54.1 GB/s | 80.8 GB/s | +49% |
+| PCIe H2D | 8.6 GB/s | 8.5 GB/s | flat |
+| CPU-MoE NVFP4 | 28.1 GB/s | 46.7 GB/s | +66% |
+| CPU-MoE BF16 | 41.5 GB/s | 71.3 GB/s | +72% |
+| Serving: NVFP4 hybrid decode | 60.1–63.8 tok/s | 76.0–80.9 tok/s | **+29–31%** |
+| Serving: FP8 offload decode | ~90–106 tok/s | ~86–106 tok/s | **flat** (PCIe-bound) |
+
+**Lesson:** RAM overclocking only pays off on CPU-compute (hybrid) MoE paths; pure offload is PCIe-bound.
+
+### 4. Engine findings reported upstream (PR #104)
+
+1. **CPU executor core collision (fixed in `c23ec78`)**: both TP ranks' hybrid executors pinned to the same
+   physical cores. Per-rank disjoint core partitioning recovered 13.5–14.9x across three independent machines
+   (this dataset's host + two author machines).
+2. **Offload + `--disable-pynccl` serialization**: in offload mode, per-token PCIe expert streams and the
+   host-staged all-reduce contend on the same bus. Machine-dependent: hybrid wins on bare metal, offload can
+   win in VMs.
+3. **Fused MoE gate** (`freetoken/engine/engine.py`, `applied_nv`/`applied_fp` guards): `--moe-backend fused`
+   refuses NVFP4/FP8 ("bf16-only for now"). At TP=2 the sharded NVFP4 experts (~11 GB/rank) would fit in
+   24 GB of VRAM — lifting this gate would likely beat every row in this dataset.
+
+Also verified: the `lm_head` vocab-sharding fix (`f1ff8e2`) — both FP8 and NVFP4 now load past the previous
+crash point at TP=2.
+
+## Data
+
+`benchmarks.jsonl` — one row per measurement. Schema:
+
+| Column | Type | Meaning |
+|---|---|---|
+| `date` | string | Measurement date (UTC) |
+| `engine` | string | FreeToken version / commit |
+| `model` | string | Checkpoint served |
+| `quant` | string | `fp8` / `nvfp4` / `bf16` |
+| `tp_size` | int | Tensor-parallel size |
+| `moe_backend` | string | `offload` / `hybrid` / n/a |
+| `attention_backend` | string | e.g. `hybrid_linear`, `triton` |
+| `extra_flags` | string | Non-default serve flags |
+| `ram_mts` | int | DDR4 transfer rate during run |
+| `category` | string | `serving` / `bandwidth` / `capacity` / `load_test` |
+| `metric` | string | `decode_tok_s`, `ttft_s`, `bandwidth_gbs`, `kv_usable_tokens`, `load_status` |
+| `target` | string | For bandwidth rows: the subsystem measured |
+| `value` | number/string | Measured value (or status string) |
+| `value_max` | number | Upper bound when the run produced a range |
+| `unit` | string | Unit of `value` |
+| `notes` | string | Context, caveats, cross-references |
+
+## Methodology
+
+- Serving numbers: OpenAI-compatible endpoint (`127.0.0.1`, local), scripted client, representative prompt
+  batches (up to 399 output tokens); decode throughput = generated tokens / decode-phase wall time unless
+  noted (`engine-instantaneous` = the server's own logged per-batch gen throughput).
+- "Warm" = after first request; "cold" includes FlashInfer JIT compilation.
+- Bandwidth rows: `ft bench bw` calibration output.
+- Every TP=2 row was run on an otherwise idle host; a contaminated first offload measurement (a stale server
+  holding both GPUs) was discarded and re-run clean — the discarded number is not in this dataset.
+
+## Reproduction notes (the parts the docs understate)
+
+1. **Do not use pip-installed CUDA packages with FreeToken.** FlashInfer JIT-compiles kernels lazily
+   (including at first inference), so a partial toolkit fails *after* the server reports ready. Install the
+   official toolkit: `cuda-nvcc-13-0 cuda-cudart-dev-13-0 libcurand-dev-13-0`, `CUDA_HOME=/usr/local/cuda-13.0`.
+2. Multi-GPU needs NCCL dev libs: `sudo apt-get install libnccl2 libnccl-dev` (upstream docs PR #90).
+3. After killing `ft serve`, orphaned multiprocessing workers can hold VRAM and port 1920;
+   `pkill -9 -f "multiprocessing.spawn"` cleans them.
+4. DDR4-3600 stability on X299 quad-channel may need VCCSA/VCCIO raised (1.2 V / 1.15 V worked here) —
+   vdimm alone at 1.35–1.41 V was not sufficient.
+5. `bench_decode_moe.py` (upstream) hangs spawning its own server in this environment; direct `ft serve` +
+   scripted client was used instead.
+
+## Provenance
+
+- Engine: [FlashML-org/FreeToken](https://github.com/FlashML-org/FreeToken)
+- TP support work: [PR #104](https://github.com/FlashML-org/FreeToken/pull/104) (+ stacked branch by CraigStone-Dev)
+- Tracking issues: [#29](https://github.com/FlashML-org/FreeToken/issues/29) (Qwen3.5/3.6 MoE TP=1 gate), [#15](https://github.com/FlashML-org/FreeToken/issues/15) (multi-GPU roadmap)
+- Benchmarks and fix validation on this hardware: [Alogotron](https://huggingface.co/Alogotron)
+
+## Limitations
+
+- Single hardware sample (Ampere consumer); NVFP4 carries a software-dequant penalty here — Blackwell cards
+  should shift the hybrid/offload balance.
+- Decode figures are single-client, moderate batch; no concurrency sweep.
+- BF16 rows are load-test only (host RAM bound).
+
+## Citation
+
+```bibtex
+@misc{freetoken_qwen36_dual3090_benchmarks,
+  title  = {FreeToken Qwen3.6-35B-A3B — Dual RTX 3090 Consumer Benchmarks},
+  author = {Alogotron},
+  year   = {2026},
+  url    = {https://huggingface.co/datasets/Alogotron/freetoken-qwen36-dual3090-benchmarks}
+}
+```