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… configs are written atomically (Niko1221#459) A fresh copy of Strata set itself up like the newest strata-*.json of another Strata folder and crashed with JSONDecodeError when that file was empty or cut off. previous_config now takes the newest one that parses as a JSON object; each one that does not gets a one-line warning naming it, and with none the copy is set up as a fresh install. Every place setup writes a run config now goes through write_config (a .tmp file, then os.replace), so a setup stopped half-way no longer leaves an empty config behind. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…s falls back to the wheels (Niko1221#446) rocm_root took /opt/rocm when bin/hipcc and lib/libhipblas.so* were there and the version was 7.0 or newer. A runtime-only ROCm 7 (no -dev packages) has both, and the engine's cmake configure then failed on the missing hip-lang package. The system ROCm now also needs lib/cmake/hip-lang/hip-lang-config.cmake (lib, lib64 or the multiarch lib folder, where CMake looks) and include/hip/hip_runtime.h; without them setup warns, naming what is missing, and takes the TheRock wheels path, as it already does for a ROCm older than 7.0. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
… a failed tuning says why (Niko1221#447) tools/calibrate.py read any "gpu" list as a layer split and added --layer-split auto, so a one-card config with a helper card for the expert tier ("gpu": [0] + --expert-cache-device1) was refused by the engine before it read a weight, a "0,2" string never got its split, and split_skip_if_fits was ignored. The tuner now builds the arguments with serve.server.engine_args, the same as a normal start. When the tuning fails, setup prints the engine's own last error line from the log (only lines written during the tuning), and says the failure again where it is not lost: before --calibrate starts the model, and in the "All set." summary of an install that tuned. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…etup recommends the big card alone (Niko1221#448) - engine (layer splits only): when a stage's cache cannot fund the chunk CUDA0 alone would choose, a WARNING names it (CUDA index, name, cache slots, the slots the chunk borrows) and points at the helper cache (--expert-cache-device1, docs/SECOND_GPU.md). The chunk choice itself is unchanged: fits() is split into fits_one() with the same tests; one GPU never reaches the new log. - setup: a pair whose second card is under 12 GB beside a first card of 12 GB or more is still offered, but the first card alone is recommended (the default with --yes and Enter; offer_together asks "n" by default), with a note. A named --gpus pair is kept. Equal pairs (2x 8/16/24 GB) are unchanged. - The automatic drop of the small card from an auto split is not done: stages load their weights before the boundary search and the exact prompt-buffer bytes need the sessions made after it. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…the totals (Niko1221#457) The server already read drafts_offered / drafts_accepted from the engine's DONE line but only returned them in a response's timings (draft_n / draft_n_accepted). Each /metrics request entry now carries both (null when the engine did not report them) and the totals keep their running sums since the server started, so a dashboard such as llm-visuals can show the acceptance. Additive: no existing field changes. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…he --family/--model to use (Niko1221#444) With Unsloth's UD-IQ3_XXS or UD-Q2_K_XL in the folder, setup either said only "has no IQ3_XXS model file" (--family unsloth --model IQ3_XXS) or took the UD-IQ3_XXS file for the GSQ-RCO IQ3_XXS by the size in its name. Now: - a chosen shard whose name is a quantization setup has no size for (UD-IQ3_XXS, UD-Q2_K_XL, a K-quant) stops with "<file> is UD-IQ3_XXS, a GGUF Strata cannot run" and what Strata runs: ISTA-DASLab's GSQ-RCO files (and Swift 1.5's and the Coder's) and Unsloth's UD-Q4_K_XL only; - a folder without the chosen file but with other GGUFs lists the ones Strata cannot use and the --family/--model of the ones it can (an empty folder keeps check_shards' "missing"); - a size the family does not have names the families that do (--family qwen --model IQ3_XXS), and with --gguf-dir also what Strata runs. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…e unpruned sizes for that (Niko1221#438) README and docs/MODELS.md said only "Weaker outside coding". Niko1221#438 measured Chinese answers wrong or looping on the Coder where English was fine; both now say plainly that this includes Chinese and other CJK text and point to the sizes that keep every expert (Q2_0, IQ2_XS, IQ3_S). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…iko1221#461), the low-RAM resident mode on Windows 32 GB PCs (Niko1221#467), fixes Niko1221#460 Niko1221#459 Niko1221#446 Niko1221#447 Niko1221#457 Niko1221#448 Niko1221#444; setup requires it Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
The fill from the GGUF (--stream-experts) read each expert's three slices, copied the blob and waited for the copy before the next read: ~12,000-18,500 experts with neither the SSD nor PCIe kept busy. Now the slots are admitted in profile order first (same placement), reads go in file order by up to 8 threads into page-locked batches of 64, and a batch's copies overlap the next batch's reads. STRATA_FILL_SERIAL=1 keeps the serial fill. Tested on the B70: cold page cache, Coder fill 23.4 GiB 76.2 s -> 18.8 s; launch to first token Coder 82 s -> 26 s, IQ2_XS 120 s -> 41 s; outputs identical to the serial fill. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Rb4SRxFuLpffQAAXBf4HxA
… read (Niko1221#471) The engine's `strata serve: prompt ...` line now says "= 0 reused + 12288 of 98179 read in ... (cancelled)" with the rate from the tokens read, and DONE gets a 15th field: the prompt tokens actually read. The server's /metrics history and totals count reused + read for a cancelled read (prompt_total keeps the whole prompt); an older engine's DONE line (no such field) is read as before. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
Engine: when the MTP draft head's subset does not fit, it prints what the head needs, the VRAM free, and the smaller shipped subsets (--draft-vocab cyrillic / en, their MiB from the head's real row size) with how to pick one (START-HERE.bat / ./setup.sh --draft-vocab en, kept in the model's config). Text only, on the failure path. Server: a start that ended that way carries the engine's hint (or the same advice for an older engine) in its "the engine exited before it was ready" error. Setup: a card under 14 GB with the default subset gets a note recommending --draft-vocab en; nothing is changed, an explicit or saved choice gets no note, and the golden configs are unchanged. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…o1221#475) In a git clone they run `git pull --ff-only` (a zip copy is told to download the new zip), then `setup.py --update`: what a plain start does to an install before it starts the model - the Python packages, the ready-made engine when MIN_ENGINE is newer (a compiled one when its source changed), each model's config upgrades and its draft subset, the Windows AMD runtime - and nothing else: no question, no model files, no start. Both scripts run from one parsed block, since the pull can rewrite the running script. Documented in docs/INSTALL.md (Updating) and the README. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…profile-save, Niko1221#477) `--expert-profile-save P` (--serve) writes the adaptive tier's learning as a profile in tools/make_profile.py's format, byte for byte (read_expert_profile reads it back): every pair ranked - the experts resident in VRAM now first, then the routing counted since the start (the decayed usage summed at each adapt step, before its decay), then the profile the engine started from. On QUIT / stdin EOF and every --expert-profile-save-every minutes (default 10, 0 = exit only) between requests, through <P>.tmp and a rename. Without the flag nothing is counted or written: `heat` stays empty and every added loop is over it. Server: "expert_profile_save" (and "expert_profile_save_every") in the config pass the flags, and the next start begins from the saved file instead of the config's --expert-profile when its header matches that profile's layers x experts. tests/core/expert_profile_save_test.cpp (CPU only) checks the order and the bytes. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…, the draft-head hint (Niko1221#474), UPDATE.bat (Niko1221#475), --expert-profile-save (Niko1221#477); setup requires it Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…pt path, grouped on the GPU (opt-in STRATA_PF_FUSED=1) Strata's own kernels (src/prefill/moe_fused.cu): the layer's input rounded to int8 once per token (a scale per 32 values), the (token, k) pairs grouped by expert on the GPU (counts, offsets, a 64-row tile list - no host sync), then per batch of up to 128 experts one gate/up launch (mma.sync m16n8k32 s8, the 2-bit codes unpacked in registers, Q2_0's offset folded into the activation blocks' sums, SwiGLU and H's int8 rounding in the epilogue) and one down launch into the per-slot rows the existing combine reads. Experts are read where they are (cache slot or streamed ring slot, a pointer per expert): no gather. Used only on the streamed walk (chunks from stream_all_min()) of the canonical Q2_0 layout on sm_80+ CUDA, with STRATA_PF_FUSED=1; it reuses the MMQ buffers (Xq, H, GU). Everything else - the default, native/IQ packs, small chunks, sm_75, HIP - runs the MMQ path unchanged (the old block is only wrapped in the else branch). tests/cuda/prefill_fused_moe_test (exit 77 below sm_80), RTX 5070: vs a double-precision reference, MMQ rel RMS 1.144e-2 (worst row 4.15e-2), fused 1.144e-2 (4.15e-2); fused vs MMQ 9.0e-5. One layer at 2048 tokens x top 10 over 512 experts: MMQ path 11.12 ms, fused 3.13 ms. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…e layer's experts) by default The fused path launches on batches of a layer's streamed experts, so with the 384-slot ring a layer's last batch waited for slots its own first batch frees. With STRATA_PF_FUSED=1 (and the canonical Q2_0 layout) the pinned-RAM ring is 512 slots; without it nothing changes (384 / 96 as before; STRATA_PREFILL_RING still overrides). RTX 5070, Q2_0 (cfg-q2rel), greedy, 3 alternating pairs, prompt tok/s: 4K 1,275 -> 1,516 (+18.9%), 32K 2,176 -> 2,422 (+11.3%); at 384 slots the fused path gave +4-7% / +3%. 1024 slots: 4K +24-27%, but the 32K chunk falls from 8192 to 6144 (+6%). The default path's output is identical to 0.1.35's on both prompts. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
… hold only what the fused kernels need
In a layout whose chunks run the fused experts (STRATA_PF_FUSED=1, the Q2_0 pack, a streamed chunk) GU holds the
grouping tables, H the int8 H rows and Xq the per-token int8 activations, and Hq nothing - instead of MMQ's FP32 GU / H
and per-slot q8_1 rows: ~100 KB a token less in the MoE set, which then no longer sets the shared region's size (the
GDN set does). A last chunk below stream_all_min() still runs MMQ in the same buffers, so each keeps MMQ's size for
stream_all_min() - 1 tokens. The ring may grow to 1024 slots with the fused experts (STRATA_PREFILL_RING; the default
stays 512). Without STRATA_PF_FUSED nothing changes: the same buffers, ring cap (512) and events.
The fused walk now counts the experts it reads from the cache in experts_resident (it stayed 0): every resident expert
of the layer, routed or not - at a streamed chunk's size nearly all are.
RTX 5070, Q2_0 (cfg-q2rel), --prefill auto, 2 alternating pairs, prompt tok/s (borrowed slots):
4K: MMQ 1,305 (1621), fused 1,517 (1749), fused + P3 1,512 (1534), + 1024-slot ring 1,605 (2046)
32K: MMQ 2,177 (3071), fused 2,453 (3199), fused + P3 2,485 (2641), + 1024-slot ring 2,660 (3153)
128K (--max-context 131072 --kv-resident 32768): MMQ 2,108 (chunk 8192), fused 2,140 (6144), fused + P3 2,346
(8192), + 1024-slot ring 2,392 (6144)
A 16K chunk still does not fit the 12 GB card: at 8192 tokens the loan is 2,641 of 3,690 slots, ~2.1K of them per
8K tokens.
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…ault (two layers' experts) With P3's smaller buffers the 1024-slot ring fits beside an 8192-token chunk at 32K (borrowed 3,153 of 3,690 slots, under the 90% cap). RTX 5070, Q2_0 (cfg-q2rel), --prefill auto, prompt tok/s at 512 / 1024 slots (2 alternating pairs): 4K 1,512 / 1,605, 32K 2,485 / 2,660, 128K (KV streaming) 2,346 / 2,392 - there the chunk falls from 8192 to 6144 and is still faster. Against MMQ (one more pair on this build): 4K 1,299 -> 1,609 (+23.9%), 32K 2,187 -> 2,656 (+21.5%), 128K 2,128 -> 2,454 (+15.3%). The ring does not change the numbers (the same output as at 512 slots). Without STRATA_PF_FUSED nothing changes (384 / 96 slots, cap 512). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…y argmax, the same ids and tokens - qsa_block_topk: calls of up to 16 queries (decode windows) run on a cluster of 8 CTAs per query (block_topk_cluster_kernel): keys in shared memory, digit histograms pushed through distributed shared memory, one cluster barrier per radix pass, a parallel digit search, cluster-wide offsets for the ordered emit. The threshold and the cut at it are the one-CTA kernels' (same tie rule, NaN keys 0). STRATA_QSA_CLUSTER=0: off. RTX 5070 per call: 21.9 -> 15.6 us at 32K, 58 -> 18 at 128K, 200 -> 22 at 262K. - sample_tokens greedy without penalties: a cluster of 8 CTAs per row (sampler_greedy_cluster_kernel), four loads in flight per thread, results merged in CTA 0 (lowest index on ties, NaN never taken, 0 when none). STRATA_ARGMAX_MULTI=0: off. 39.6 -> 5.9 us per call at 248,320 logits. - Both launch through cudaLaunchKernelEx with the cluster attribute (captured in the decode graphs) only where they run: CUDA, compute capability 9+ (STRATA_EMULATE_CC respected), the kernel's own PTX/SASS at sm_90+ (an older build's JIT copy is a trap), room for one cluster - checked once per device. HIP and sm_75/86/89 keep the one-CTA kernels. - decode_cluster_parity (ctest; skips below sm_90): bitwise vs the one-CTA dispatch and the reference kernel over 434 top-k cases (contexts 7..262,144, every tail weight, 1-16 queries, ties / NaN / +-inf / -0 / denormal / all NaN) and 216 argmax cases, plus a CUDA graph capturing both (first calls inside the capture); --bench timings. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
…gate/up IQ2_XXS IQ2_XS IQ2_S IQ3_XXS IQ3_S IQ4_XS, down Q2_0 IQ4_NL; opt-in STRATA_PF_FUSED=1) src/prefill/moe_fused_iq.cu: the Q2_0 path's design (moe_fused.cu: the grouping on the GPU, a pointer per expert - cache slot or ring slot, no gather -, SwiGLU and H's int8 rounding in the gate/up epilogue) for the raw GGUF blobs of the native packs. The load stage decodes the i-quant blocks to int8 in shared memory (llama.cpp's codebooks and sign tables, held in shared memory; a 32-value sub-block per thread per 64 values of K, read one stage ahead with 16-bit loads and two 256-value blocks ahead into L2), with each sub-block's scale (per 16 values for IQ2_XS / IQ2_S, which multiply at m16n8k16). The activations are int8 per 32 values in natural order (quantize_act_native); fragments by ldmatrix; the magic bias 1.5 * 2^23 enters as the mma's C, so a dot becomes a float with one add. A work item is 256 weight rows x 64 routed rows, or 128 x 128 when the experts have 56-112 rows each (STRATA_PF_FUSED_TILE=64|128). Wiring (prefill.cpp): a native layer takes these kernels when both its formats are covered, else MMQ (IQ1_M keeps the FP16 path); the 512-slot ring of the fused path now also for a native pack with such layers. sm_75, HIP and the default (no variable) are unchanged: on IQ2_XS, IQ3_XXS and IQ3_S the default output is 0.1.35's (256/256 greedy tokens, 4K and 32K). tests/cuda/prefill_fused_iq_test (exit 77 below sm_80), RTX 5070, 256 tokens x 64 experts against a double-precision reference from ggml's to_float, rel RMS / worst row - MMQ and fused equal to four digits: IQ2_S/Q2_0 1.489e-2 / 2.93e-2 (fused vs MMQ 2.6e-4), IQ2_XXS/Q2_0 1.464e-2 / 2.96e-2 (1.9e-4), IQ2_XS/IQ4_NL 1.465e-2 / 2.41e-2 (3.5e-5), IQ3_XXS/IQ4_NL 1.458e-2 / 2.73e-2 (1.6e-4), IQ3_S/IQ4_NL 1.470e-2 / 2.65e-2 (6.5e-5), IQ4_XS/Q2_0 1.446e-2 / 2.72e-2 (4.4e-5); both tile shapes. One layer (512 experts, top 10), ms, MMQ path -> fused at 2048 / 3584 / 8192 tokens: IQ2_S 10.5 -> 5.2, 12.5 -> 9.3, 21.4 -> 17.5; IQ2_XXS 8.8 -> 4.2, 10.7 -> 7.6, 17.9 -> 13.6; IQ3_S 10.6 -> 5.6, 12.3 -> 8.8, 19.8 -> 18.4; IQ3_XXS 10.2 -> 5.1, 11.9 -> 8.3, 19.1 -> 16.5. MMQ's products alone (no gathers) at 3584 / 8192: IQ2_S 8.9 / 17.9, IQ3_S 8.1 / 15.6: the kernels are about even with MMQ's, the gain is the path around them. (The test syncs the device after its uploads: a cudaMemcpy from pageable memory on the legacy stream can still be queued on Windows while a non-blocking stream runs - the grouping then read zeroed ids, every pair on expert 0. prefill_fused_moe_test's timing part has the same pattern.) End to end, RTX 5070, speed_135.py's settings, greedy, A (MMQ) / B (STRATA_PF_FUSED=1) alternating, 2 pairs, prompt tok/s: IQ2_XS 4K 1,228 -> 1,378 (+12%; a cold first A run of 590 left out), 32K 2,107 -> 2,170 (+3.0%); IQ3_XXS 4K 1,001 -> 1,020 (+1.8%), 32K 1,745 -> 1,739 (-0.4%); IQ3_S 4K 927 -> 925 (-0.2%), 32K 1,382 -> 1,362 (-1.4%). The IQ3 packs' prompts here wait on the host copies of their unpinned experts (IQ3_XXS 4K: 5,062 of 22,573 streamed, ~3.0 s of 3.4 s), so faster kernels change little. Each arm repeats itself (256/256; IQ3_XXS 32K B and IQ3_S 32K A split at 162 / 223, the decode's timing); A vs B greedy agreement, identical prefix of 256: IQ2_XS 204 / 88, IQ3_XXS 182 / 89, IQ3_S 31 / 256. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Rb4SRxFuLpffQAAXBf4HxA
…(STRATA_PF_FUSED=0 off), the native IQ packs' fused kernels opt-in (=1), cluster decode kernels; the fused test waits for its uploads The ring rule: 1024 slots for the Q2_0 pack's fused experts (P3), 512 for native packs with fused layers. Quality (teacher-forced vs the FP16 path, Q2_0): fused as close as MMQ at 8K, closer at 32K (phaseA-tests s18-tf). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
device, expert_source, native_head, prefill gemm, prefill.cpp, generate.cpp merged from dpct of aeb35be and d9ab843 with sycl/tools/merge_upstream.py (the 3-way merge restricted to files upstream changed, dpct kernel-name hashes canonicalized to the port's). Version 0.1.35. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01Rb4SRxFuLpffQAAXBf4HxA
…ls) and their switches Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_0154bwiad8gY7wRehCXy27pN
The PCIe probe sets the share of the missed experts the GPU reads over the link (--pcie-frac): the native default 0.55 from 20 GB/s up, less on a slower link (Niko1221#44). On Windows HIP it timed its copies with events that do not bracket them there: an RX 6800 read between 3,348 and 25,999 GB/s in 24 engine starts, so every link kept the x16 share. A standalone copy of the probe on that card: its events measure 0.13 ms for the 1 GiB, the host clock 43 ms (25.1 GB/s). On Windows HIP the probe now times the same copies on the host, between two synchronizes (1 GiB, tens of milliseconds): the same RX 6800 on PCIe 4.0 x16 reads 27.2 GB/s in every run, which keeps 0.55 there. On a PCIe 4.0 x8 or 3.0 x16 link the share now shrinks as Niko1221#44 intended. CUDA and Linux keep the events. Claude-Session: https://claude.ai/code/session_015Ld5YDzuKW1hgMVwwgRZDP Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
…DGET)
On Windows, hipMemGetInfo (ROCclr's PAL backend) reports the card's size
minus this process's own allocations; what the desktop and other
programs hold on the card is not subtracted. On a card that also drives
the desktop the engine counts the ~930 MiB those hold as free, and
--expert-cache auto fills the card past what Windows lets the process
keep in VRAM: WDDM moves part of it out to system RAM. On an RX 6800
(HIP SDK 7.2) with setup's configuration: a 9.83 GiB cache, the card at
16132 of its 16368 MiB, 929 MiB more of the engine in system RAM than
with this change, 30.1-31.0 tok/s decode. On a 0.1.30 build,
--vram-reserve-mib 1500 left the card as full (16037 MiB), since the
reserve is taken from the same figure.
Windows gives every process a video memory budget that does account
for the others (DXGI QueryVideoMemoryInfo). On that card it is 799 MiB
below the card's size while the process is small and about 1.8 GiB
below once it holds 11 GiB; allocations past it still succeed.
cudaMemGetInfo, which the HIP shim maps to hipMemGetInfo, now lowers
hipMemGetInfo's free figure by what the budget withholds (the card's
size minus the budget), on Windows only. What is sized from it then
stays within the budget: --expert-cache auto sizes the cache from it,
and the existing check after the slots are written shrinks it ("only
8 MiB free once the slots are written ... shrinking"): an 8.05 GiB
cache, the card at 15230 MiB, 40.6-41.5 tok/s.
Lowering HIP's own figure, instead of taking the budget minus DXGI's
usage, keeps memory the HIP runtime has freed but still caches counted
as free, as HIP counts it.
dxgi.dll is loaded at run time, so nothing new is linked; the adapter
is found by the device's LUID. Without dxgi.dll, the adapter or a
budget below the card's size, hipMemGetInfo's figure stands. The first
query logs the budget once. STRATA_WDDM_BUDGET=0 turns it off. CUDA
and Linux builds are unchanged.
Claude-Session: https://claude.ai/code/session_015Ld5YDzuKW1hgMVwwgRZDP
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
…S.md with the measured quality Changes default output on long prompts of native packs against 0.1.39 (expected; gate notes in the S40 report). Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
…8, Linux 525); the wheel pins match the 12.9.1 toolkit Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
… keep 0.1.39's ring - a prompt that fits 0.1.39's auto chunk keeps 0.1.39's ring (Prefill::set_ring_budget's small_max): 4K prompts were 1-17% slower with the smaller ring (Coder -15%, IQ3_S -5%, IQ3_XXS -2%; 5 interleaved pairs each); - ring slots are given up for a bigger chunk only where 0.1.39's rule held the chunk under 6144 (ring_cap_for); from 6144 on the ring keeps its 0.1.39 size (the Coder 6144/384 -> 7936/199 was -12% at 32K); - the unpinned arm's cap stays 96 (the byte budget made it ~49 on IQ3_S); - the scan sets its own ring budget instead of set_ring_override, so a layer split's 96-slot override (Niko1221#340) is no longer clobbered by the scan. RTX 5070 vs STRATA_RING_BYTES=0: 4K -0.7..+1.1% (bit-identical); 32K IQ3_XXS +18.5% (cache 1500) / +9.4%, Coder +3.1%, IQ3_S +0.2%. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJJwUv9g3cD2ztX6KWS8rk
# Conflicts: # setup.py
# Conflicts: # src/core/expert_source.cpp # src/kernels/cuda/iq_kernels.cu
# Conflicts: # src/core/verify.cpp # src/program/generate.cpp
…l); UD-Q4_K_XL stays experimental - "experimental" is per model now: MODELS["UD-Q4_K_XL"] keeps it, the unsloth family and UD-IQ4_XS drop it. The first menu lists the family without [experimental], with a one-line about (UD-IQ4_XS: a 94 GB download; under ~80 GB of RAM part of its experts are read from the SSD). Its size menu lists UD-IQ4_XS first (the default) and UD-Q4_K_XL second; an experimental size always sorts last. --model names are unchanged. - UD-IQ4_XS: no EXPERIMENTAL warning, --check verdict without it. Images are asked (off by default), per model: the engine's image path has no restriction for this pack, which uses the original model's image encoder; not yet run with images. UD-Q4_K_XL keeps images off. - MCP: the fallback tables mirror setup's (per-model experimental/vision), sizes in setup's order, per-size "experimental" and "images", the vision check per model. - Docs: README and its six translations, MODELS, UNSLOTH_Q4, AI_SETUP, MCP_SERVER. - Tests: the menus, the default, --model by name, images, --check, the pack/tokenizer paths; the golden configs are unchanged. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJJwUv9g3cD2ztX6KWS8rk
…e-mib 1000 if a request stalls With the image encoder on the GPU, the 700 MiB reserve can leave ~200 MiB free on a 12 GB card (UD-IQ4_XS end-to-end check on an RTX 5070). Setup prints a one-line recommendation; the written config is unchanged. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_01QJJwUv9g3cD2ztX6KWS8rk
… IQ3_S, 5K to 164K prompt tokens An appendix condenses the A/B work done on this box since 0.1.35: the version ladder (cluster kernels, the 0.1.38 prompt rewrite), the env levers kept here (PF_FUSED + tile128 + GR_DOWN_MAX4, +6.9/+8.1/+2.6 on 0.1.39 as a trio) and the measured losers (ADAPT_NOWAIT, IQ_PREFETCH, dma/direct pcie-mode, stage-overlap, predictive prefetch). Model hashes verified against the HF revision's LFS OIDs.
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The 4-token accumulator variant of the fused GR down read keeps the same tile, block size, accumulation order and variant as the kFusedGrMaxT one, so its bits are the same; #832 measured +2.8-3.5% decode with it on an RTX 5070. Unset, the engine now turns it on for compute capability 12.0 only (decided once per device), and off on every other card and on the HIP build, where it was not measured. STRATA_GR_DOWN_MAX4=0 or =1 still decides explicitly. Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
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Thanks for this. STRATA_GR_DOWN_MAX4 stays opt-in in 0.1.40: on our RTX 5070 (sm_120) it gave no measurable gain, so we did not turn it on for any card. STRATA_GR_DOWN_MAX4=1 still enables it. If you measured a gain on another card, please send the numbers. |
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Yes — measured gain on this card (RTX 5070 Ti 16 GB, Ryzen 7 9800X3D, Win11, driver 617.14, engine 0.1.39 release zip, IQ3_S pack, ctx 262144, Q4_0 KV): STRATA_GR_DOWN_MAX4=1 alone: decode +2.8% on short prompts (5K band, ~75 tok/s base) and +3.5% after 130K+ prompts (ABBA couplets +3.6/+4.4), by 8-boot ABBA with STRATA_PF_FUSED=1, TILE=128 and --pcie-frac 0.35 held constant in both arms. With all three levers on vs all off (8-leg ABBA, 2026-10-04): prefill @130k +6.9%, decode after 130K +8.1%, short decode +2.6%, every couplet positive. So on the 5070 Ti (also sm_120) it is a small but replicated decode win, not noise. Full table is in the report appendix; happy to paste raw engine log lines for the on/off arms if useful. |
Niko1221
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The 4-token accumulator variant of the fused GR down read keeps the same tile, block size, accumulation order and variant as the kFusedGrMaxT one, so its bits are the same; #832 measured +2.8-3.5% decode with it on an RTX 5070. Unset, the engine now turns it on for compute capability 12.0 only (decided once per device), and off on every other card and on the HIP build, where it was not measured. STRATA_GR_DOWN_MAX4=0 or =1 still decides explicitly.
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Heads-up: today we cleaned up this repository's commit history, and GitHub closed this PR automatically when GitHub cannot reopen it, because its branch is built on the old history. If you want to keep it going: start a new branch from the new Sorry for the noise, and thank you for the contribution. |
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Correcting our earlier number — Method (your shape): N interleaved boot pairs — the flag Positive control (the important part). The same harness, same box, A/B on
So the harness detects a real ~2.3% effect cleanly and reproducibly. Any lever whose ratios scatter both sides of 1.0 is therefore noise, not a small win.
Medians hover at 1.0, the per-pair ratios scatter about ±10% and flip sign between quants and sizes (IQ3_S 99K values range 0.80–1.12), and nothing is consistent in direction. This agrees with your revert ( Where our +2.8% came from. Our original bench battery prefixed every probe run with a unique random marker to defeat the prompt cache. That defeats the cache, but it also makes the two arms generate different text, so they get different draft-acceptance and different decode speeds — a second, unwanted variable that our historical ABBA averaged over but did not cancel. Making the prompt identical in both arms (while still varying it across pairs) removes it. Our original method could not have distinguished +3% from 0% reliably; this one can, as the control shows. Caveat, stated plainly: this method's resolution is ~2–3% on this box. A real effect below ~1% is not excluded — we're saying no reproducible effect at the ≥2% level, not that none exists. We built a Q2_0 pack to run this ( Two follow-ups we're doing regardless: (1) our bench report's appendix carried the Thanks for the pointer — the paired design is stricter and we'll use it going forward. |
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Relanded the report onto the new |
anon761
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…1221#832) The 4-token accumulator variant of the fused GR down read keeps the same tile, block size, accumulation order and variant as the kFusedGrMaxT one, so its bits are the same; Niko1221#832 measured +2.8-3.5% decode with it on an RTX 5070. Unset, the engine now turns it on for compute capability 12.0 only (decided once per device), and off on every other card and on the HIP build, where it was not measured. STRATA_GR_DOWN_MAX4=0 or =1 still decides explicitly.
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Thanks for the report. It is in 0.1.40.2 as bench/results/2026-10-04-community-rtx-5070ti-windows, with your authorship kept. Raw dumps and logs were trimmed (see TRIMMED.md in the folder); the README, summaries and configs are unchanged. The row is in bench/results/COMMUNITY.md. |
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…1221#832) The 4-token accumulator variant of the fused GR down read keeps the same tile, block size, accumulation order and variant as the kFusedGrMaxT one, so its bits are the same; Niko1221#832 measured +2.8-3.5% decode with it on an RTX 5070. Unset, the engine now turns it on for compute capability 12.0 only (decided once per device), and off on every other card and on the HIP build, where it was not measured. STRATA_GR_DOWN_MAX4=0 or =1 still decides explicitly.
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Follow-up measurement note: original Qwen3.8-Flash-Next GSQ RCO IQ3_S on a Windows 11 RTX 5090 (32 GB), Strata v0.1.43 engine SHA-256 223289044f377c97e982cdacddb2e17b5726f63a0439765e3fb3e2bcfdf78656. The saved same-host 4K engine-decode medians were 153.8 tok/s on v0.1.42 and 157.9 tok/s on v0.1.43; the current binary hash matches the latter. Matched-harness 4K tests, three runs each, temperature 0, reasoning off, max_tokens 256:
The best tested arm is a substantial partial improvement (+78.3% over the batch1/no-reserve arm) but does not recover the historical result: 147.8 is 6.4% below 157.9 and below the ±5% lower bound of 150.005 tok/s. Fixed PCIe fraction 0.0 was 31.1% slower than auto; turning route-tail-skip off was 5.8% slower than the default-on arm. No persistent configuration or source change was made. Only loopback probes were used; the production 0.0.0.0/no-key config was not started. Caveat: this harness draws new prompt text from the same saved 4K corpus per run, so prompts were not exactly paired across arms. Historical successful-run environment snapshots were not present in the saved artifacts. Full per-run prompt/output counts, TTFT, API rate, raw matrices and limitations are recorded in the task KEEP artifacts; no claim of full recovery is made. |
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Kanban tracking for this follow-up: task t_f5898ce6, worker run ID 224. Measurement and reporting are complete; the card is still |
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bench/results/2026-10-04-community-rtx-5070ti-windows/: a community benchmark report for a single RTX 5070 Ti (16 GB) with 64 GB RAM on native Windows 11, engine 0.1.39 release build, IQ3_S native pack, 262K context.model-provenance.json); engine digest verified against the release asset at install.battery.ps1).The README also carries an appendix condensing the A/B work done on this box since 0.1.35: the version ladder (the sm_90+ cluster decode kernels, the 0.1.38 prompt-path rewrite), the env levers kept here (
STRATA_PF_FUSED=1,TILE=128,GR_DOWN_MAX4=1— +6.9/+8.1/+2.6% as a trio on 0.1.39, 8-leg ABBA), the calibrated--pcie-frac 0.35and prefill capauto:32768, and the measured losers (ADAPT_NOWAIT, IQ_PREFETCH=8192, dma/direct pcie-mode, a stage-overlap patch, router-lookahead prefetch) with their numbers, so nobody re-spends an evening on them. The equal-chunk prefill result measured here (+4.3% at 22.6K tokens) is noted and cross-linked to #693, where the idea lives.