sync: auto-sync from DESKTOP-0O8A1RL at 2026-05-16 16:26:04
Author: Mike Swanson Machine: DESKTOP-0O8A1RL Timestamp: 2026-05-16 16:26:04
This commit is contained in:
108
tmp_bench_8b.py
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108
tmp_bench_8b.py
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import urllib.request, json, time
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MODELS = ["qwen3:8b", "qwen3:14b", "qwen3.6:latest"]
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TESTS = [
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("prose", "List 10 common Windows troubleshooting steps an IT technician uses daily. One sentence each, numbered.", 300),
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("classification", "Classify this ticket into ONE of: Hardware, Software, Network, Security, User-Error. Reply with category and one sentence only.\n\nTicket: Client says Outlook keeps asking for password every morning. Started after Windows Update last Tuesday.", 80),
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("json", 'Extract from this ticket and return ONLY valid JSON with keys: client_name, issue_summary, urgency (low/medium/high).\n\nTicket: Hi, this is Janet from Cascades Dental. Our front desk PC shows a blue screen every time we open Dentrix. Patients arriving at 9am, need this fixed ASAP.', 150),
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("summary", "Summarize in exactly 2 bullet points, under 30 words total:\n\nIncident: GuruRMM watchdog failed to restart service after auto-update due to SCM access denied error and a stuck suppression timer. Service was offline 25 minutes. Fixed by adding sc.exe fallback and clearing suppression on failure.", 100),
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]
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def ask(model, prompt, max_tokens):
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payload = {"model": model, "prompt": prompt, "stream": False,
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"options": {"num_predict": max_tokens}}
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start = time.time()
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req = urllib.request.Request("http://localhost:11434/api/generate",
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data=json.dumps(payload).encode(), headers={"Content-Type": "application/json"})
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r = json.loads(urllib.request.urlopen(req, timeout=300).read())
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elapsed = time.time() - start
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gen_tok = r.get("eval_count", 0)
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gen_ns = r.get("eval_duration", 1)
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tps = gen_tok / (gen_ns / 1e9) if gen_ns else 0
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raw = r["response"].strip()
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if "<think>" in raw and "</think>" in raw:
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visible = raw[raw.index("</think>")+8:].strip()
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else:
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visible = raw
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return visible, elapsed, gen_tok, tps
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def get_ps():
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try:
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r = urllib.request.urlopen("http://localhost:11434/api/ps", timeout=5)
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d = json.loads(r.read())
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models = d.get("models", [])
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if models:
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m = models[0]
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vram = m.get("size_vram", 0) // 1024 // 1024
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total = m.get("size", 0) // 1024 // 1024
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pct = int(vram / total * 100) if total else 0
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return vram, total, pct
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except:
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pass
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return 0, 0, 0
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def unload(model):
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try:
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payload = {"model": model, "keep_alive": 0}
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urllib.request.urlopen(urllib.request.Request(
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"http://localhost:11434/api/generate",
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data=json.dumps(payload).encode(),
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headers={"Content-Type": "application/json"}), timeout=10)
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except:
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pass
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time.sleep(2)
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results = {} # model -> {test_name -> (tps, elapsed, visible)}
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for model in MODELS:
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print(f"\n{'='*60}")
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print(f"MODEL: {model}")
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print('='*60)
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model_results = {}
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# Warmup load
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print(" Loading...", end="", flush=True)
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ask(model, "hello", 5)
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vram, total, pct = get_ps()
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print(f" {vram} MB / {total} MB in VRAM ({pct}%)")
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for test_name, prompt, max_tok in TESTS:
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visible, elapsed, gen_tok, tps = ask(model, prompt, max_tok)
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model_results[test_name] = (tps, elapsed, gen_tok, visible)
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hit_limit = " [HIT LIMIT]" if gen_tok >= max_tok else ""
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print(f" [{test_name:14}] {tps:5.1f} tok/s {elapsed:5.1f}s {gen_tok} tok{hit_limit}")
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if test_name == "json":
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clean = visible.strip().strip("```json").strip("```").strip()
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try:
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json.loads(clean)
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print(f" JSON: [OK]")
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except:
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print(f" JSON: [FAIL] {repr(clean[:60])}")
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results[model] = model_results
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unload(model)
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# Summary table
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print(f"\n{'='*60}")
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print("THROUGHPUT SUMMARY (tok/s)")
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print('='*60)
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print(f"{'Task':<16} {'qwen3:8b':>10} {'qwen3:14b':>10} {'qwen3.6':>10}")
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print("-"*50)
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for test_name, _, _ in TESTS:
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row = f"{test_name:<16}"
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for model in MODELS:
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tps = results[model][test_name][0]
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row += f" {tps:>10.1f}"
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print(row)
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print()
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print("Reference (full-GPU machine from OLLAMA.md):")
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print(" qwen3:14b ~66 tok/s | qwen3.6 ~32 tok/s")
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print()
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# VRAM fit analysis
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print("VRAM FIT (12 GB available):")
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for model, size_gb in [("qwen3:8b", 5.2), ("qwen3:14b", 9.3), ("qwen3.6", 23)]:
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fit = "FITS" if size_gb < 10 else "SPLIT"
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print(f" {model:<16} {size_gb:>5.1f} GB [{fit}]")
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27
tmp_hw_check.ps1
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27
tmp_hw_check.ps1
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$cpu = Get-WmiObject Win32_Processor | Select-Object -First 1
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$ram = Get-WmiObject Win32_ComputerSystem
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$gpu = Get-WmiObject Win32_VideoController | Where-Object { $_.AdapterRAM -gt 0 }
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$os = Get-WmiObject Win32_OperatingSystem
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Write-Output "=== CPU ==="
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Write-Output " $($cpu.Name)"
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Write-Output " Cores: $($cpu.NumberOfCores) physical / $($cpu.NumberOfLogicalProcessors) logical"
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Write-Output " Max MHz: $($cpu.MaxClockSpeed)"
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Write-Output "`n=== RAM ==="
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$ramGB = [math]::Round($ram.TotalPhysicalMemory / 1GB, 1)
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Write-Output " Total: $ramGB GB"
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Write-Output "`n=== GPU(s) ==="
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foreach ($g in $gpu) {
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$vramMB = [math]::Round($g.AdapterRAM / 1MB, 0)
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Write-Output " $($g.Name)"
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Write-Output " VRAM: $vramMB MB"
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Write-Output " Driver: $($g.DriverVersion)"
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}
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Write-Output "`n=== Storage (system drive) ==="
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$disk = Get-WmiObject Win32_LogicalDisk -Filter "DeviceID='C:'"
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$freeGB = [math]::Round($disk.FreeSpace / 1GB, 1)
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$totalGB = [math]::Round($disk.Size / 1GB, 1)
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Write-Output " C: $freeGB GB free / $totalGB GB total"
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124
tmp_ollama_bench.py
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124
tmp_ollama_bench.py
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"""
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Throughput benchmark for DESKTOP-0O8A1RL.
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Tests: current settings, then pulls GPU layer count while running.
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"""
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import urllib.request, json, time, subprocess, threading, sys
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PROMPT = (
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"List 10 common Windows troubleshooting steps an IT technician uses daily. "
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"One sentence each, numbered."
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)
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def get_ps():
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"""Check which models are loaded and how many GPU layers."""
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try:
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r = urllib.request.urlopen("http://localhost:11434/api/ps", timeout=5)
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return json.loads(r.read())
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except:
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return {}
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def run_gen(model, num_predict=300):
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payload = {
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"model": model,
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"prompt": PROMPT,
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"stream": False,
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"options": {"num_predict": num_predict}
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}
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start = time.time()
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req = urllib.request.Request(
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"http://localhost:11434/api/generate",
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data=json.dumps(payload).encode(),
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headers={"Content-Type": "application/json"},
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)
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r = json.loads(urllib.request.urlopen(req, timeout=300).read())
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elapsed = time.time() - start
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eval_tok = r.get("eval_count", 0)
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eval_ns = r.get("eval_duration", 1)
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prompt_tok = r.get("prompt_eval_count", 0)
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prompt_ns = r.get("prompt_eval_duration", 1)
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gen_tps = eval_tok / (eval_ns / 1e9) if eval_ns else 0
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prompt_tps = prompt_tok / (prompt_ns / 1e9) if prompt_ns else 0
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return {
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"elapsed": elapsed,
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"eval_tok": eval_tok,
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"gen_tps": gen_tps,
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"prompt_tok": prompt_tok,
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"prompt_tps": prompt_tps,
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"response_snippet": r["response"].strip()[:200],
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}
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def print_result(label, res, ps_info=None):
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print(f"\n {label}")
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print(f" Generation: {res['gen_tps']:.1f} tok/s ({res['eval_tok']} tokens in {res['elapsed']:.1f}s)")
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print(f" Prompt eval: {res['prompt_tps']:.1f} tok/s ({res['prompt_tok']} tokens)")
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if ps_info:
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for m in ps_info.get("models", []):
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layers = m.get("size_vram", 0) / 1024 / 1024
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total = m.get("size", 0) / 1024 / 1024
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pct = (m.get("size_vram", 0) / m.get("size", 1)) * 100 if m.get("size") else 0
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print(f" GPU VRAM used: {layers:.0f} MB / {total:.0f} MB total ({pct:.0f}% in VRAM)")
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# ── Check initial Ollama env ────────────────────────────────────────────────
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print("=" * 60)
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print("DESKTOP-0O8A1RL — Ollama Throughput Benchmark")
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print("GPU: RTX 5070 Ti Laptop (4 GB VRAM)")
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print("=" * 60)
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# ── Test 1: qwen3:14b current settings ─────────────────────────────────────
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print("\n[1] Warming up qwen3:14b...")
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# warmup
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run_gen("qwen3:14b", num_predict=20)
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time.sleep(2)
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ps = get_ps()
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print(f" ps after warmup: {ps}")
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print("\n[2] qwen3:14b — current settings (300 tokens)")
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res14 = run_gen("qwen3:14b", num_predict=300)
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ps14 = get_ps()
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print_result("qwen3:14b", res14, ps14)
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# Unload
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print("\n[3] Unloading model...")
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try:
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payload = {"model": "qwen3:14b", "keep_alive": 0}
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urllib.request.urlopen(urllib.request.Request(
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"http://localhost:11434/api/generate",
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data=json.dumps(payload).encode(),
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headers={"Content-Type": "application/json"}
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), timeout=10)
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except:
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pass
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time.sleep(3)
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# ── Test 2: qwen3.6 current settings ───────────────────────────────────────
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print("\n[4] qwen3.6:latest — current settings (300 tokens)")
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print(" (This may take a while to load from disk...)")
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run_gen("qwen3.6:latest", num_predict=20) # warmup load
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time.sleep(2)
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ps36w = get_ps()
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print(f" ps after warmup: {ps36w}")
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res36 = run_gen("qwen3.6:latest", num_predict=300)
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ps36 = get_ps()
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print_result("qwen3.6:latest", res36, ps36)
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# ── Summary ─────────────────────────────────────────────────────────────────
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print("\n" + "=" * 60)
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print("SUMMARY")
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print("=" * 60)
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print(f" qwen3:14b gen: {res14['gen_tps']:.1f} tok/s prompt: {res14['prompt_tps']:.1f} tok/s")
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print(f" qwen3.6 gen: {res36['gen_tps']:.1f} tok/s prompt: {res36['prompt_tps']:.1f} tok/s")
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print()
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print(" Reference (other machine benchmark from OLLAMA.md):")
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print(" qwen3:14b ~66 tok/s")
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print(" qwen3.6 ~32 tok/s")
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print()
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# VRAM analysis
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for label, model_size_gb in [("qwen3:14b", 8.8), ("qwen3.6", 22)]:
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vram_gb = 4.0
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pct_gpu = min(vram_gb / model_size_gb * 100, 100)
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print(f" {label} ({model_size_gb} GB): {pct_gpu:.0f}% fits in 4 GB VRAM → rest on CPU/RAM")
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print()
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print(" Diagnosis: Both models exceed 4 GB VRAM → split CPU/GPU or pure CPU")
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print(" Expected impact: 2-4x slower than a machine with sufficient VRAM")
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Reference in New Issue
Block a user