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

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Assessment

Difficulty
2/5
Estimated time
1-3 hours
Newbie friendliness
62/100
Issue type
Documentation
Clarity
Mostly clear
Activity status
Active
Tech stack
cpp, huggingface, python
Domain
machine-learning, testing

Research direction

Start with tools/strata_bench.py, tools/ab_engine.py, and tools/needle_bench.py, then review the referenced results.json, engine-log-excerpt.txt, and needle b.txt. Reproduce the benchmark with the documented hardware, model, server command, and suite settings; completion means reporting the timing and correctness results with their ranges and limitations.

Written by the indexing model from the issue text.

Description

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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Getting set up

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First steps

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