ebca0c8921
Closes task #230. Measured PFlash NIAH compression at 16K-260K source contexts on 1× 24 GB / 3090 single-card. Result: PFlash works flawlessly up to 131K source. Compresses 131,068 tokens to 6,524 (5%) in 10.8s with NIAH key + answer both retained. Vanilla llama.cpp pp131072 takes ~257s per Luce's published numbers, so PFlash alone is ~24× faster at this context. End-to-end TTFT (PFlash + target prefill on 6.5K) would be ~12-13s vs ~257s = ~20×. Above 131K, drafter ephemeral forward-pass tensors (K_curr/V_curr/Q_last at full sequence length) exceed 24 GB. K-cache quantization (--pflash-k-type q8_0) doesn't help — the failing allocs are forward-pass not cache, confirmed by separate bench at 200K/260K with identical OOM at the same layer numbers. @weicj's PR #78 claim of 24K → 262K dual-GPU phase split is neither refuted nor reproduced. Their setup was 2× 22 GB Ti with target also loaded co-resident on one card; the "24K" was target+drafter combined. Our 131K is drafter-alone on 24 GB. Reproducing 262K specifically would require investigation of their drafter config (chunk_size, lookahead, BSA window) — drafter activation footprint at 200K+ is the binding constraint regardless of GPU count. Practical recommendation for 24 GB / 3090 single-card users: PFlash is shippable for source contexts ≤ 131K. The ~24× TTFT speedup is genuine and quality holds. Above 131K, fall back to vanilla llama.cpp prefill or wait for upstream drafter optimizations. Adds: - BENCHMARKS.md "PFlash long-context compression on 1× 3090" subsection with full per-context table + drafter ceiling explanation - results/lucebox-pflash-niah-20260504-150321/ (BF16 K cache run) - results/lucebox-pflash-niah-q8k-20260504-150600/ (q8_0 K cache run) This closes our active investigation of the Luce surface — three benches done (DFlash same-card 73.97 mean, K8V4 same-card 74.68 mean, PFlash compression ceiling 131K). Recommendation surface narrows to: PFlash at ≤131K is the one piece of Luce that beats vLLM dual.yml on TTFT for that workload class. Co-Authored-By: Claude Opus 4.7 (1M context) <noreply@anthropic.com>
21 lines
1.9 KiB
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21 lines
1.9 KiB
Plaintext
ggml_cuda_init: found 1 CUDA devices (Total VRAM: 24126 MiB):
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Device 0: NVIDIA GeForce RTX 3090, compute capability 8.6, VMM: yes, VRAM: 24126 MiB
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[drafter] loaded Qwen3-0.6B BF16: n_layer=28 n_head=16 n_kv=8 n_embd=1024 n_ff=3072 head_dim=128 vocab=151936
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[pflash-daemon] ready load=0.521s vocab=151936
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[pflash-daemon] compress start tokens=131068 keep=0.050 lookahead=2 chunk=32 pool=13 path=/opt/ai/github/club-3090/results/lucebox-pflash-niah-q8k-20260504-150600/niah_ctx131068/prompt_counted.bin
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[qwen3-0.6b-fp] layer 1/28 done (A=0.193s FP=0.152s B=0.086s)
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[qwen3-0.6b-fp] layer 28/28 done (A=1.584s FP=4.234s B=2.342s)
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[qwen3-0.6b-fp] forward 8.17s (S=131068, A=1.58s FP=4.23s B=2.34s) tail-score 2.34s total 10.52s
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[drafter] forward+score in 10.64s S=131068
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[drafter] score_and_compress total 10.64s S=131068 kept=6524 (204/4096 chunks)
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[pflash-daemon] compress done 10.645s in=131068 out=6524 ratio=0.0498
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[pflash-daemon] compress start tokens=199996 keep=0.050 lookahead=2 chunk=32 pool=13 path=/opt/ai/github/club-3090/results/lucebox-pflash-niah-q8k-20260504-150600/niah_ctx199996/prompt_counted.bin
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ggml_backend_cuda_buffer_type_alloc_buffer: allocating 390.62 MiB on device 0: cudaMalloc failed: out of memory
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alloc_tensor_range: failed to allocate CUDA0 buffer of size 409591808
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[pflash-daemon] compress failed: forward_qwen3_0p6b: K_curr/V_curr/Q_last alloc failed at layer 25
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[pflash-daemon] compress start tokens=259996 keep=0.050 lookahead=2 chunk=32 pool=13 path=/opt/ai/github/club-3090/results/lucebox-pflash-niah-q8k-20260504-150600/niah_ctx259996/prompt_counted.bin
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ggml_backend_cuda_buffer_type_alloc_buffer: allocating 507.80 MiB on device 0: cudaMalloc failed: out of memory
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alloc_tensor_range: failed to allocate CUDA0 buffer of size 532471808
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[pflash-daemon] compress failed: forward_qwen3_0p6b: K_curr/V_curr/Q_last alloc failed at layer 18
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[pflash-daemon] stopped
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