Community benchmark NVFP4 - RTX 5060 Ti 16GB - Ryzen 5600 - 96GB RAM + needle_bench (Qwen3.8-Flash-Next-NVFP4-GPTQ-Strata)

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Valutazione

Difficoltà
2/5
Tempo stimato
1-3 ore
Idoneità per principianti
62/100
Tipo di issue
Documentazione
Chiarezza
Abbastanza chiara
Stato di attività
Attiva
Stack tecnologico
cpp, huggingface, python
Ambito
machine-learning, testing

Direzione di ricerca

Inizia con tools/strata_bench.py, tools/ab_engine.py e tools/needle_bench.py, quindi esamina i file referenziati results.json, engine-log-excerpt.txt e needle b.txt. Riproduci il benchmark con l'hardware, il modello, il comando del server e le impostazioni della suite documentati; il completamento consiste nel riportare i risultati di temporizzazione e correttezza con i relativi intervalli e limitazioni.

Scritto dal modello di indicizzazione a partire dal testo della issue.

Descrizione

Community benchmark on NVIDIA GeForce RTX 5060 Ti

Measured on 2026-10-17 with tools/strata_bench.py (suite 1, 3 runs per test, medians with ranges). Speeds are the engine's own timings; first-token times are measured by the client over HTTP. Speed only: no correctness check was run.

Hardware and software

  • Mobo B450 => PCIe 3.0
  • GPU and VRAM: "second" GPU NVIDIA GeForce RTX 5060 Ti, 16 GiB VRAM
  • CPU: AMD Ryzen 5 5600 6-Core Processor (6 cores, 12 threads); installed RAM: 95.9 GiB; storage: not measured
  • OS: Windows 10, GPU driver: 616.92; CUDA: 13.13
  • Strata: project 0.1.45, commit 477ac13c; engine 0.1.45
  • Background workloads and power limits: Clean system, "GPU drivers", Tiny wall, MSI monitor.

Model and configuration

  • Model and quantization: NVFP4 GPTQ (qwen-nvfp4-gptq "96GB" Maximilian228/Qwen3.8-Flash-Next-NVFP4-GPTQ-Strata)
  • Context: 524288 tokens; parallel slots: 1
  • Settings: the install's own configuration, unchanged; thinking off for the suite; temperature 0

(Google FL 8 free, suggestions)

  • Orca tokenizer reuse
  • reduced --expert-cache 2048 (JAN +filesystem, Understory, Exa)
  • "uncensored": true ( sergqwer/strata-nvfp4)
  • Turn on swap ssd "for stability"
strata.exe --pack pack --native qwen-nvfp4-gptq-dense.gguf --ple-gguf ple-fp8.gguf --expert-profile expert-profile-nvfp4.bin --expert-profile-save expert-profile-nvfp4.bin --expert-cache 2048 --prefill auto --adapt-async 1 --spec 4 --spec-min-p 0.5 --mtp rt --max-context 524288 --kv q4_0 --kv-resident 32768

Environment of the config: STRATA_PREFILL_CPU_SHARE=0, STRATA_ARENA_PIN_GIB=16, STRATA_PIN_BY_NEED=1

Method

Run by python tools/strata_bench.py --long against the HTTP API of a Strata server (started by the benchmark from the install configuration); model loading is not timed. One warm-up chat and a 1K prompt are discarded first. Every prompt starts with a unique tag, so the conversation cache cannot help the fresh-prompt tests.

  • short chat: the three questions of tools/ab_engine.py, up to 100 new tokens, 3 runs
  • 4K / 16K prompts: generated word lists sized by a calibration request, 24 new tokens, 3 fresh prompts each
  • agent session: system prompt and four tools, then 5 turns that each add about 1500 words of a project file and continue from the model's own earlier answers (the way agent clients resend a conversation), 64 new tokens per turn, 3 sessions; the cache numbers come from the answer's usage (cached_tokens) and timings (prompt_n)
  • concurrency (when run): N requests sent at the same moment, total tokens over the wall time of the burst, latency over all requests

Results GPU @2150 /83% Power uv, CPU @1.5GHz

Configuration Actual prompt tokens Reused tokens Generated tokens Runs Prompt tok/s median and range Decode tok/s median and range TTFT seconds median and range
short chat 46 (43-51) 0 100 (100-100) 9 not measured 15.6 (13.9-17.1) 2.43 (2.12-2.73)
4K prompt, fresh 4019 (4019-4019) 0 (0-0) 24 cap 3 342.0 (340.1-342.5) not measured 11.84 (11.82-11.91)
16K prompt, fresh 16058 (16058-16058) 0 (0-0) 24 cap 3 571.3 (570.2-572.1) not measured 28.21 (28.16-28.25)
32K prompt, fresh 32049 (32049-32049) 0 (0-0) 24 cap 3 578.0 (577.2-578.8) not measured 55.59 (55.55-55.69)
agent session, follow-up turns 14583 (14553-14596) at the last turn see below up to 64 3 see below 15.4 (10.3-18.6) 10.62 (10.34-10.87)

Ranges are the lowest and highest of the runs; Runs counts requests that returned a token.

Agent session (conversation cache)

Cache hit over the follow-up turns: 68.7 % (68.6-68.8); earlier-turn tokens read again per session: 3 (3-74) (earlier turns that the cache did not hold; the 13719 (13717-13720) new tokens of the files are read once); first turn (nothing cached) first token: 9.06 s (8.99-9.14).

Turn (run 1) Prompt tokens Reused Read Prompt tok/s First token (s) Decode tok/s
0 609 0 609 68.3 8.988 8.8
1 3741 621 3120 293.6 10.704 12.2
2 5274 3804 1470 143.2 10.344 14.1
3 8707 5338 3369 312.5 10.866 16.1
4 11627 8765 2862 271.0 10.671 15.4
5 14553 11654 2899 276.2 10.598 12.1

Per-request data: results.json. Engine log lines from the start: engine-log-excerpt.txt (when this run started the server).

Results GPU Default, CPU 3.4GHz

Configuration Actual prompt tokens Reused tokens Generated tokens Runs Prompt tok/s median and range Decode tok/s median and range TTFT seconds median and range
short chat 46 (43-51) 0 100 (100-100) 9 not measured 26.1 (24.3-30.9) 1.27 (1.10-1.39)
4K prompt, fresh 4019 (4019-4019) 0 (0-0) 24 cap 3 363.2 (361.1-363.4) not measured 11.13 (11.11-11.19)
16K prompt, fresh 16058 (16058-16058) 0 (0-0) 24 cap 3 627.1 (625.5-628.8) not measured 25.66 (25.60-25.73)
32K prompt, fresh 32049 (32049-32049) 0 (0-0) 24 cap 3 635.1 (634.6-635.5) not measured 50.56 (50.51-50.60)
agent session, follow-up turns 14586 (14577-14606) at the last turn see below up to 64 3 see below 26.4 (21.8-33.2) 10.15 (9.94-10.24)

Ranges are the lowest and highest of the runs; Runs counts requests that returned a token.

Agent session (conversation cache)

Cache hit over the follow-up turns: 68.8 % (68.8-68.8); earlier-turn tokens read again per session: 3 (3-4) (earlier turns that the cache did not hold; the 13719 (13718-13719) new tokens of the files are read once); first turn (nothing cached) first token: 8.47 s (8.42-8.55).

Turn (run 1) Prompt tokens Reused Read Prompt tok/s First token (s) Decode tok/s
0 609 0 609 72.8 8.418 21.2
1 3761 638 3123 309.6 10.147 21.8
2 5294 3824 1470 148.3 9.959 26.1
3 8727 5358 3369 330.7 10.244 30.9
4 11634 8772 2862 284.5 10.115 28.9
5 14577 11678 2899 287.1 10.152 26.2

python tools/needle_bench.py (only for test correctness, "eco mode")

Context Depth Status Prompt Tokens Time Gen. Speed Expert Cache Hit
32k 10% FOUND 32,792 53 s 17.4 tok/s 25.9%
32k 50% FOUND 32,792 52 s 20.8 tok/s 27.2%
32k 90% FOUND 32,793 52 s 15.9 tok/s 33.9%
128k 10% FOUND 127,535 199 s 12.7 tok/s 41.8%
128k 50% FOUND 127,533 200 s 11.1 tok/s 46.9%
128k 90% FOUND 127,533 176 s 14.2 tok/s 57.1%
256k 10% FOUND 261,213 392 s 13.3 tok/s 58.1%
256k 50% FOUND 261,213 392 s 16.0 tok/s 64.7%
256k 90% FOUND 261,214 392 s 20.0 tok/s 66.1%
520k 10% FOUND 517,676 863 s 19.2 tok/s 68.7%
520k 50% FOUND 517,676 863 s 18.1 tok/s 64.9%
520k 90% FOUND 517,677 862 s 18.4 tok/s 68.1%

Correctness and limitations

Not measured: this is a speed suite. The conversation uses the model's own replies, so token counts differ a little between runs. Machine details come from the server, nvidia-smi/rocm-smi and the OS; background load and power limits are not recorded. Total time of the suite: 656 s.

MSI Monitor Eco + Normal

Image
Technical Summary: NVFP4 Integration & Constraints on Consumer Rigs (Strata v0.1.45)
  • Expert Cache Reduction (--expert-cache 2048):
    Active MCP servers (filesystem, Understory, Exa) and system prompts inject heavy token payloads (~4k+ tokens per turn) containing JSON schemas. This spikes memory pressure during prefill and MTP verification (verify: instantiate: out of memory). Lowering the cache from +- 3000 to 2048 reclaims ~1.6 GB VRAM headroom.
  • Orca Tokenizer Reuse:
    The Qwen3.8-Flash-Next architecture shares an identical BPE vocabulary structure (152k tokens). Reusing asset files (vocab.json, tokenizer.json, merges.txt) from the Orca IQ3_XXS pack satisfies server.py validation without redundant file duplication.
  • Windows WDDM Commit Accounting vs Physical RAM:
    The model (63.3 GiB) + 524k KV cache (3.38 GiB) fit entirely within 96 GB physical DDR4 RAM, leaving ~16 GB free. However, the CUDA runtime and WDDM demand a high virtual memory commitment threshold (Commit Charge). A static pagefile acts purely as a virtual "accounting receipt" satisfying Commit Charge, while pages remain hardware-locked in physical RAM via SeLockMemoryPrivilege and VirtualLock (ensuring zero SSD I/O thrashing).

README ECO GPU + CPU Undervolt.md
results ECO GPU + CPU Undervolt.json

README default.md
results default.json

needle b.txt

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