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freetoken-qwen36-dual3090-benchmarks / README.md
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metadata 
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 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
  • TP support work: PR #104 (+ stacked branch by CraigStone-Dev)
  • Tracking issues: #29 (Qwen3.5/3.6 MoE TP=1 gate), #15 (multi-GPU roadmap)
  • Benchmarks and fix validation on this hardware: 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 

@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}
}