This gallery proves it can. Every benchmark, every model, and every 3D scene was generated entirely on-premise by a single 64GB machine the size of a game console. The question isn't whether local AI is viable — it's how fast and how reliable it can be when you tune it properly.
For the tech-curious: We tested 21 AI models for speed, quality, and deep-context retrieval. We generated 49 working 3D/WebGL apps from scratch. We pushed context windows to 120,000 tokens. All on integrated graphics.
Top 35B Q6 MoE/MTP lanes were re-tested with the proven stack: TurboQuant turbo4/turbo3, MTP speculative decoding, ubatch=1024, flash attention, performance governor, swappiness=10, THP=always, adaptive timeouts, and separated llama-server timings.
The corrected ladder records authoritative server-reported input tokens instead of relying on client estimates. architect-35b-q6 passed every class through 120K, reaching 117,814 observed prompt tokens. The result reinforces the current TurboQuant + MTP production profile; it does not justify a global Q8 promotion.
Q8 DFlash drafters were tested against Qwen3.6-27B, Qwen3.6-35B-A3B, and Gemma4-26B-A4B using a separate llama.cpp DFlash Vulkan build. Prefill, decode, wall decode, and draft acceptance remain separated. Decision: do not promote DFlash to production MiniV yet.
Each model received 5 visual prompts: Three.js Particle Galaxy, FPS Raycasting Engine, 3D Flight Simulation, Wave Ocean Shader, and Breakout Canvas Game. All deliverables were runtime-gated with headless browser verification. SHA256 integrity manifest included.
21 models benchmarked with Bench v2.2 — separated prefill/decode/MTP metrics. 11 models achieved perfect score. Deep context retrieval solved: 310s→92s via ubatch optimization. OS-level optimizations: CPU governor performance, ubatch-size tuning, and Transparent Huge Pages (THP) enabled.
Agentic/structured tasks with adaptive timeouts by context size. Bench v2.2 separates prefill/decode metrics from llama-server API timings object. Thinking tokens isolated via reasoning_content field.
Framework-spec tasks: executable contracts, BMAD/story breakdown, spec review, and execution planning. MTP acceptance rate tracked for multi-token prediction models.
Long-context retrieval scoring (deep_context_score) with timing measurement (deep_context_time_s). OS-level optimizations: CPU governor performance, ubatch-size tuning, THP enabled.
| # | Model | Class | Pass | Decode | Prefill | MTP | Deep ctx | Status |
|---|---|---|---|---|---|---|---|---|
| 1 | miniv-qwopus36-35b-a3b-q6-mtp |
LOCAL_CHAMPION | 4/4 | 71.2 | 320.3 | 90% | 92s · 1.00 | MODEL_DONE |
| 2 | miniv-qwen36-35b-a3b-q6 |
FRAMEWORK_READY | 4/4 | 63.8 | 332.7 | 91% | 99s · 1.00 | MODEL_DONE |
| 3 | miniv-qwopus36-35b-a3b-q8-mtp |
FRAMEWORK_READY | 4/4 | 62.5 | 361.8 | 92% | 93s · 1.00 | MODEL_DONE |
| 4 | miniv-qwen36-35b-a3b-q8 |
FRAMEWORK_READY | 4/4 | 54.4 | 338.6 | 89% | 105s · 1.00 | MODEL_DONE |
| 5 | miniv-ornith1-35b-q6 |
FRAMEWORK_READY | 4/4 | 52.8 | 328.2 | — | 97s · 1.00 | MODEL_DONE |
| 6 | miniv-ornith1-35b-q6-100k |
FRAMEWORK_READY | 4/4 | 52.6 | 328.1 | — | 97s · 1.00 | MODEL_DONE |
| 7 | miniv-nex-n2-mini-q6 |
FRAMEWORK_READY | 4/4 | 49.5 | 336.1 | — | 88s · 1.00 | MODEL_DONE |
| 8 | miniv-qwen3-coder-next-q4 |
SMOKE_PROMOTED | 3/4 | 50.0 | 228.7 | — | 106s · 1.00 | MODEL_DONE |
| 9 | miniv-qwopus35-9b-coder-q6-mtp |
SMOKE_PROMOTED | 3/4 | 48.1 | 439.7 | 89% | 110s · 1.00 | MODEL_DONE |
| 10 | miniv-agentworld-35b-a3b-q6 |
FRAMEWORK_READY | 4/4 | 46.5 | 343.6 | — | 120s · 0.67 | MODEL_DONE |
| 11 | miniv-ornith1-35b-q8 |
SMOKE_PROMOTED | 3/4 | 45.3 | 372.5 | — | 103s · 1.00 | MODEL_DONE |
| 12 | miniv-gemma4-26b-a4b-q6 |
FRAMEWORK_READY | 4/4 | 39.7 | 404.8 | — | 84s · 0.67 | MODEL_DONE |
| 13 | miniv-gemma4-26b-a4b-q8 |
FRAMEWORK_READY | 4/4 | 36.7 | 421.9 | — | 83s · 0.67 | MODEL_DONE |
| 14 | miniv-agentworld-35b-a3b-q8 |
FRAMEWORK_READY | 4/4 | 32.6 | 329.3 | — | 114s · 0.67 | MODEL_DONE |
| 15 | miniv-ornith1-9b-q8 |
SMOKE_PROMOTED | 3/4 | 22.6 | 489.7 | — | 94s · 0.67 | MODEL_DONE |
| 16 | miniv-qwopus36-27b-v2-q6-mtp |
FRAMEWORK_READY | 4/4 | 19.1 | 127.2 | 94% | 301s · 1.00 | MODEL_DONE |
| 17 | miniv-qwopus36-27b-coder-q6-mtp |
FRAMEWORK_READY | 4/4 | 19.0 | 128.6 | 93% | 310s · 1.00 | MODEL_DONE |
| 18 | miniv-gemma4-12b-q6 |
FRAMEWORK_READY | 4/4 | 16.6 | 279.5 | — | 166s · 0.67 | MODEL_DONE |
| 19 | miniv-qwopus36-27b-coder-q8-mtp |
FRAMEWORK_READY | 4/4 | 14.7 | 135.7 | 93% | 293s · 1.00 | MODEL_DONE |
| 20 | miniv-gemma4-12b-q8 |
FRAMEWORK_READY | 4/4 | 13.2 | 286.2 | — | 159s · 0.67 | MODEL_DONE |
| 21 | miniv-qwopus36-27b-v2-q8 |
FRAMEWORK_READY | 4/4 | 7.4 | 139.0 | — | 300s · 1.00 | MODEL_DONE |
Jackrong/Kyle Hessling Space: Qwopus3.6-27B v1-preview Q4_K_M on RTX 5090, 16-prompt suite: 5 agentic, 5 web-design, 6 canvas/WebGL. Published copy reports 62.3 tok/s average, 87.4k generated tokens, 23.4 min runtime.
We reuse the comparison vocabulary — agentic reasoning, production UI, and canvas/WebGL — while showing N30's stricter local multi-pass trust gates with Bench v2.2 metrics.