Reference

Poros — Official Validation Results (NVIDIA B300 SXM6)

Official, measured benchmark numbers for the architecture families gated on the B300 (qwen2, qwen3). Every cell is measured — no number is interpolated or estimated. The same-attention-path A100 matrix is included below.

Configuration

Field	Value
Hardware	NVIDIA B300 SXM6 (268 GB VRAM)
Precision	NF4 (the only bitwise-parity path)
Steps × passes	200 steps × 3 passes (warmup discarded; median ± std across passes)
Sequence length	512
Batch / grad-accum	1 / 1 (so 1 step = 1 optimizer step for every method)
LoRA	rank 16, alpha 32
Block size (poros)	4
Dataset	`synthetic` (deterministic, license-free random tokens)
Methods	poros (blockwise residency), resident (full QLoRA), unsloth
Software stacks	torch 2.10.0+cu128 / transformers 5.5.0 and torch 2.12.0+cu130 / transformers 5.10.1, bitsandbytes 0.49.2

Scope. The bitwise-parity guarantee (0.00e+00) holds on the deterministic, math-only SDPA path — that is what Poros ships and what the poros cells below run. The resident/unsloth baselines use their default flash attention, so the slowdown column includes a math-vs-flash gap on top of the streaming cost; same-path references are ~1.6× at 32B on an RTX PRO 6000 (all methods math-only) and 2.28–2.68× on the A100 (all methods fast path, table below). Bitwise parity is a separate gate asserting torch.equal(resident, blockwise) — exact, not allclose.

Results — B300

Model	Arch	Stack	Bitwise parity	poros s/step (median±std)	resident s/step	unsloth s/step	Slowdown vs resident	vs unsloth	poros VRAM	resident VRAM	unsloth VRAM	VRAM cut vs resident	vs unsloth
Qwen3-32B	qwen3	torch 2.10.0+cu128	PASS (0.00e+00)	1.0585±0.0044	0.2945	0.4048	3.59×	2.61×	11.12 GB	37.92 GB	20.49 GB	70.7%	45.8%
Qwen3-32B	qwen3	torch 2.12.0+cu130	PASS (0.00e+00)	1.0483±0.0096	0.2960	—	3.54×	—	11.11 GB	37.89 GB	—	70.7%	—
Qwen2.5-32B	qwen2	torch 2.10.0+cu128	PASS (0.00e+00)	1.0012±0.0082	0.2755	0.4051	3.63×	2.47×	11.04 GB	36.53 GB	20.54 GB	69.8%	46.2%
Qwen2.5-32B	qwen2	torch 2.12.0+cu130	PASS (0.00e+00)	0.9900±0.0049	0.2731	—	3.62×	—	11.04 GB	36.54 GB	—	69.8%	—
Qwen2.5-72B	qwen2	torch 2.10.0+cu128	PASS (0.00e+00)	1.8680±0.0087	0.4489	0.5917	4.16×	3.16×	18.16 GB	67.63 GB	42.46 GB	73.1%	57.2%
Qwen2.5-72B	qwen2	torch 2.12.0+cu130	PASS (0.00e+00)	1.8635±0.0101	0.4410	—	4.23×	—	18.16 GB	67.63 GB	—	73.1%	—

Raw per-cell artifacts (full provenance: torch/cuda/transformers/bitsandbytes versions, GPU UUID, per-pass timings): validation/official_b300_consolidated.jsonl.

Results — A100 80GB PCIe (same attention path for all methods)

All three methods on the same default fast path and the synthetic dataset, so the slowdown column is the pure cost of blockwise residency. Full configuration and notes: official-a100-results.md.

Model	Arch	poros s/step (median±std)	resident s/step	unsloth s/step	Slowdown vs resident	vs unsloth	poros VRAM	resident VRAM	unsloth VRAM	VRAM cut vs resident	vs unsloth
Qwen2.5-32B	qwen2	2.0489±0.0091	0.7662	0.9086	2.67×	2.25×	10.95 GB	34.72 GB	20.82 GB	68.4%	47.4%
Qwen3-32B	qwen3	2.1832±0.0040	0.8157	0.9287	2.68×	2.35×	11.03 GB	36.00 GB	20.77 GB	69.4%	46.9%
Gemma3-27B	gemma3	1.8933±0.0048	0.7835	0.9365	2.42×	2.02×	9.95 GB	33.17 GB	22.06 GB	70.0%	54.9%
Gemma4-31B	gemma4	2.2537±0.0055	0.9889	1.0255	2.28×	2.20×	10.65 GB	38.75 GB	20.57 GB	72.5%	48.2%

Takeaways

Bitwise parity holds at scale. torch.equal(resident, blockwise) is exact (0.00e+00) at every scale gated on this box — Qwen2.5 through 72B and Qwen3 at 32B. Gemma3/Gemma4 are gated on the RTX 5090, not here.
~70–73% VRAM cut vs resident, ~46–57% vs unsloth. A 72B fine-tunes in 18 GB instead of 68 GB resident / 42 GB unsloth.
No PyTorch-version regression. poros speed is identical on torch 2.10 vs 2.12 (Qwen2.5-72B: 1.868 vs 1.864 s/step; Qwen3-32B: 1.059 vs 1.048).
The slowdown ratio is hardware-dependent. The B300's resident baseline is extraordinarily fast (~0.27 s/step at 32B), so a fixed streaming cost shows up as a large ratio (~3.5–4.2×). Same-path runs measure 2.28–2.68× on the A100 and ~1.6× at 32B on an RTX PRO 6000.

Reproduce

# poros + resident (needs bf16 base weights on disk)
poros bench matrix configs/bench/official_b300_qwen25_72b_pr.yaml -o out/
# unsloth (pulls its own prequantized 4-bit repo)
poros bench matrix configs/bench/official_b300_qwen25_72b_un.yaml -o out/
# bitwise parity gate
POROS_GPU_TEST_MODEL=Qwen/Qwen2.5-72B pytest -m gpu tests/test_gpu_parity.py