Strata 0.2.9

# Strata 0.2.9: Unsloth's 1-bit DeepSeek sets as a choice, much faster prompts on Strix Halo, faster parallel requests on Linux NVIDIA cards, fixes from your reports

The app is 0.2.9. Control and every engine package are 0.1.55.

The numbers first

• Strix Halo on Windows, Qwen3.8 IQ3_S: prompts 1.8 times as fast, decode the same. Against the 0.2.8 engine on the same PC, interleaved, nothing set: an 8K prompt goes from 791 to 1,424 tokens per second and a 32K prompt from 778 to 1,460. With Unsloth's UD-IQ4_XS, 8K goes from 794 to 1,243. Decode went from 57.4 to 58.9 tokens per second (about 3% more, inside normal spread), and the greedy answers were the same.
• Strix Halo on Windows, DeepSeek 2.25, engine level against 0.2.8: a 200-token prompt reads at 24.9 tokens per second against 15.0 (x1.66), a 4K prompt at 183.7 against 148.8 (x1.24), a 16K prompt at 469.9 against 298.0 (x1.58). With the drafter, decode went from 10.89 to 12.01 tokens per second, about 10% more but inside run-to-run noise; without the drafter it is unchanged (10.30 and 10.24).
• Parallel requests on Linux with NVIDIA cards: 1.45 times the 0.2.8 speed at 8 requests at once. One RTX PRO 5000, Qwen3.8 IQ3_S, 1,000-token prompts, 128 tokens out, three paired blocks, range 1.451 to 1.459. One request alone is 5.3% faster, and its greedy answers matched in 12 of 12 prompts. With --parallel or --batch of 2 or more, the server now sets the nine qualified settings itself.
• Prompts about 2% to 6% faster on Linux, by default. On Linux NVIDIA cards the Gated DeltaNet prompt kernels now run on the tensor cores: 4.4% faster at 4K and 5.5% to 5.7% at longer prompts on an RTX PRO 5000, 2.6% at 16K and 3.2% at 32K on an RTX A4000. On Linux with Radeon cards that are all gfx12 (such as the R9700) the chunked kernel now starts at 64 tokens: 3.5% at 4K, 4.7% at 16K and 6.1% at 32K, and 1.7% to 3.2% at 1K to 3K. Decode is unchanged. The KL check, the 61-task suite (55 against 54 to 56) and a 30-minute soak all passed. Windows, Mac and Intel are not changed.
• Two Intel Arc Pro B65 cards: prompts 4.6% to 15.8% faster. Prompts of 1K, 4K and 16K tokens: 4.6%, 14.6% and 15.8% faster. Eight requests at once: 53.8 tokens per second, against 42.1 before. Single-request decode through the batch server is 2.7% slower. The 42.1 is the old default of one group.
• DGX Spark, DeepSeek 2.25: a 442-token prompt reads at 80 to 82 tokens per second against 40 to 42 on the 0.2.8 engine, and a 1,953-token prompt at 208 against 144, on an otherwise quiet box. Decode is the same within noise.
• DeepSeek on several AMD cards now uses all of them by default. Four Radeon AI PRO R9700 cards, ISTA-DAS 2.25, strata launch with no --gpu: before this release it ran on one card, with 2,371 of 15,360 experts on the card, 6 experts per token and decode at 7.8 and 25.7 tokens per second on two short answers. It now fills all four, with 12,147 of 15,360 experts on the cards, 5 experts per token (the launcher's choice on four cards) and decode at 57.7 to 103.7 across six chat and tool answers on the release files.
• Unsloth's 1-bit DeepSeek sets, UD-IQ1_S and UD-IQ1_M, are now a choice in setup and `strata launch`. Downloads are 241 GB and 258 GB, about 111 GB less if you already have ISTA's Engram file. The default set is unchanged. We measured both next to the ISTA-DAS 2.25 set, one run each, on four Radeon AI PRO R9700 cards (UD-IQ1_M on two RTX A6000), with 6 experts per token. The reference is Unsloth's own Q8_K_XL file.

| | UD-IQ1_S | UD-IQ1_M | ISTA-DAS 2.25 | |---|---|---|---| | KL against the reference (4,995 positions) | 0.114 | 0.087 | 0.110 | | Top-1 agreement | 87.2% | 88.8% | 87.4% | | Perplexity against the reference | +4.8% | +3.0% | +8.9% | | 61-task suite (35 on 14K to 30K-token documents) | 53 of 61 | 53 of 61 | 56 of 61 |

By family in that suite (UD-IQ1_S, UD-IQ1_M, ISTA-DAS 2.25): retrieval 11, 11, 12 of 12; extraction 11, 13, 13 of 13; unanswerable 9, 9, 9 of 9; summary 8, 6, 7 of 8; code 6, 5, 6 of 6; JSON 3, 3, 3 of 3; agent loops 5, 6, 6 of 10. All 10 agent loops finished on all three sets, with no invalid tool calls and no tool errors. Speed: UD-IQ1_S on four R9700 cards, decode 48 to 88 tokens per second and a 4K prompt at 1,204 to 1,296; UD-IQ1_M on two A6000 cards, decode 41 to 82 and a 4K prompt at 304 cold and 415 warm. These are single runs on our own test texts. UD-IQ1_M's suite ran on the two A6000 cards, the other two on four R9700 cards, and the reference file is Unsloth's own.

What is new

• Parallel requests and the Gated DeltaNet kernels are on by default, on Linux. Parallel: Linux with NVIDIA cards, with --parallel or --batch of 2 or more. Gated DeltaNet kernels: Linux with NVIDIA cards (Qwen3.8) and Linux with Radeon cards that are all gfx12. Any environment variable or config value you set still wins. Windows, Mac, Intel and DeepSeek are not changed.
• Intel Arc B-series. Prompt attention now runs on the int8 path, on by default for Battlemage cards. Set STRATA_PROMPT_ATTN_ESIMD=0 to turn it off. Two B-series cards with 8 or more slots now run 2 pipelined groups by default; set --batch-groups 1 or STRATA_BATCH_GROUPS_AUTO=0 to turn that off. A single request through the batch server is 2.7% slower than before. The A750 keeps the old path, and we did not run it.
• Strix Halo on Windows (gfx1151). The prompt experts use the F16 matrix-core kernels by default (STRATA_PF_F16), a Q8_0 down layer of the Unsloth files takes the same kernel, and STRATA_SELECT_WMMA (the block scorer on matrix cores) is on by default. The block scorer is not bit-exact: about 10 of 2,051 selected cells flip at the cutoff for prompts past about 5K tokens. Turn any of them off with NAME=0, for example STRATA_PF_F16=0. On DeepSeek, the shards are no longer held open after the load by default (DS4_NO_VIEW=0 turns that off), and the CPU lane waits for the next token more steadily. Part of the prompt work follows ideas from asf0's libstrix (MIT).
• DeepSeek GGUF layouts. Strata now accepts the four-shard set that ISTA re-published on 26 October and ISTA's six-shard layout, both for "Use a local GGUF" in the app and for --model-path. A cut or missing shard is still refused, and the message names the missing part. Thanks to lu123bing for the GGUF reader fix (PR #4348) and to StackArc3 for the six-shard layout (PR #4632).
• Unsloth's 1-bit sets, where they are offered. Windows and Linux (NVIDIA and AMD), the DGX Spark, and the Mac. On a Mac, UD-IQ1_S ran on an M5 Max (decode 15.4 to 18.5 tokens per second, a 3K prompt at 230 cold and 314 warm, a 14K prompt at 519); UD-IQ1_M is offered there but was not run. On the DGX Spark, UD-IQ1_S decodes 15 to 26 tokens per second on code and reads a 3,950-token prompt at 377 to 397; UD-IQ1_M decodes 7.6 to 13 and reads at 330 to 345. The Engram table comes from the ISTA file you already have, if you have one. Setup keeps its own plain note about these 1-bit sets.

Fixed, from your reports

• Chat:
• A long answer no longer slows the page down. Each streamed token used to redraw the whole chat. Now the text is applied about ten times a second, and only the streaming message redraws. For a 40,000-token answer, main-thread time fell from 15.7 to 9.0 seconds, and peak heap from 114 MB to 62 to 75 MB. We measured it in the app on a synthetic long answer, not on the reporter's own setup.
• A client that hangs up right after data: [DONE] no longer gets "Request cancelled." (#4690).
• Tool calls and the server:
• Calls shown inside a code fence or an indented block are no longer run as tool calls. A fence closes only with the same length it opened with (#4702, #4703).
• A tool the conversation already used counts as declared, so a later call to it comes back as a tool call, not as raw text (#4663).
• Qwen3.6: the warm-up no long