pyr0ball
c93466c037
Three tools: snipe_search (GPU-scored trust-ranked), snipe_enrich (deep BTF scraping), snipe_save (persist search to Snipe UI). GPU inference scoring uses VRAM + arch tier weighted composite. LLM-condensed output trims verbose listing dicts to trust/price/GPU/url. Configure via ~/.claude.json with SNIPE_API_URL env var pointing at local or cloud API.
143 lines
5.7 KiB
Python
143 lines
5.7 KiB
Python
"""GPU architecture and VRAM scoring for laptop mainboard inference-value ranking.
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Parses GPU model names from eBay listing titles and scores them on two axes:
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- vram_score: linear 0–100, anchored at 24 GB = 100
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- arch_score: linear 0–100, architecture tier 1–5 (5 = newest)
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inference_score = (vram_score × vram_weight + arch_score × arch_weight)
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/ (vram_weight + arch_weight)
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Patterns are matched longest-first to prevent "RTX 3070" matching before "RTX 3070 Ti".
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"""
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from __future__ import annotations
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import re
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from dataclasses import dataclass
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@dataclass(frozen=True)
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class GpuSpec:
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model: str # canonical name, e.g. "RTX 3070 Ti"
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vram_gb: int
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arch_tier: int # 1–5; 5 = newest generation
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arch_name: str # human-readable, e.g. "Ampere"
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vendor: str # "nvidia" | "amd" | "intel"
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@dataclass
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class GpuMatch:
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spec: GpuSpec
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vram_score: float
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arch_score: float
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inference_score: float
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# ── GPU database ──────────────────────────────────────────────────────────────
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# Laptop VRAM often differs from desktop; using common laptop variants.
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# Listed longest-name-first within each family to guide sort order.
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_GPU_DB: list[GpuSpec] = [
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# NVIDIA Ada Lovelace — tier 5
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GpuSpec("RTX 4090", 16, 5, "Ada Lovelace", "nvidia"),
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GpuSpec("RTX 4080", 12, 5, "Ada Lovelace", "nvidia"),
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GpuSpec("RTX 4070 Ti", 12, 5, "Ada Lovelace", "nvidia"),
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GpuSpec("RTX 4070", 8, 5, "Ada Lovelace", "nvidia"),
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GpuSpec("RTX 4060 Ti", 8, 5, "Ada Lovelace", "nvidia"),
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GpuSpec("RTX 4060", 8, 5, "Ada Lovelace", "nvidia"),
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GpuSpec("RTX 4050", 6, 5, "Ada Lovelace", "nvidia"),
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# NVIDIA Ampere — tier 4
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GpuSpec("RTX 3090", 24, 4, "Ampere", "nvidia"), # rare laptop variant
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GpuSpec("RTX 3080 Ti", 16, 4, "Ampere", "nvidia"),
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GpuSpec("RTX 3080", 8, 4, "Ampere", "nvidia"), # most laptop 3080s = 8 GB
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GpuSpec("RTX 3070 Ti", 8, 4, "Ampere", "nvidia"),
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GpuSpec("RTX 3070", 8, 4, "Ampere", "nvidia"),
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GpuSpec("RTX 3060", 6, 4, "Ampere", "nvidia"),
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GpuSpec("RTX 3050 Ti", 4, 4, "Ampere", "nvidia"),
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GpuSpec("RTX 3050", 4, 4, "Ampere", "nvidia"),
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# NVIDIA Turing — tier 3
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GpuSpec("RTX 2080", 8, 3, "Turing", "nvidia"),
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GpuSpec("RTX 2070", 8, 3, "Turing", "nvidia"),
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GpuSpec("RTX 2060", 6, 3, "Turing", "nvidia"),
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GpuSpec("GTX 1660 Ti", 6, 3, "Turing", "nvidia"),
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GpuSpec("GTX 1660", 6, 3, "Turing", "nvidia"),
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GpuSpec("GTX 1650 Ti", 4, 3, "Turing", "nvidia"),
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GpuSpec("GTX 1650", 4, 3, "Turing", "nvidia"),
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# NVIDIA Pascal — tier 2
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GpuSpec("GTX 1080", 8, 2, "Pascal", "nvidia"),
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GpuSpec("GTX 1070", 8, 2, "Pascal", "nvidia"),
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GpuSpec("GTX 1060", 6, 2, "Pascal", "nvidia"),
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GpuSpec("GTX 1050 Ti", 4, 2, "Pascal", "nvidia"),
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GpuSpec("GTX 1050", 4, 2, "Pascal", "nvidia"),
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# AMD RDNA3 — tier 5
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GpuSpec("RX 7900M", 16, 5, "RDNA3", "amd"),
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GpuSpec("RX 7700S", 8, 5, "RDNA3", "amd"),
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GpuSpec("RX 7600M XT", 8, 5, "RDNA3", "amd"),
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GpuSpec("RX 7600S", 8, 5, "RDNA3", "amd"),
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GpuSpec("RX 7600M", 8, 5, "RDNA3", "amd"),
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# AMD RDNA2 — tier 4
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GpuSpec("RX 6850M XT", 12, 4, "RDNA2", "amd"),
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GpuSpec("RX 6800S", 12, 4, "RDNA2", "amd"),
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GpuSpec("RX 6800M", 12, 4, "RDNA2", "amd"),
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GpuSpec("RX 6700S", 10, 4, "RDNA2", "amd"),
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GpuSpec("RX 6700M", 10, 4, "RDNA2", "amd"),
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GpuSpec("RX 6650M", 8, 4, "RDNA2", "amd"),
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GpuSpec("RX 6600S", 8, 4, "RDNA2", "amd"),
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GpuSpec("RX 6600M", 8, 4, "RDNA2", "amd"),
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GpuSpec("RX 6500M", 4, 4, "RDNA2", "amd"),
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# AMD RDNA1 — tier 3
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GpuSpec("RX 5700M", 8, 3, "RDNA1", "amd"),
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GpuSpec("RX 5600M", 6, 3, "RDNA1", "amd"),
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GpuSpec("RX 5500M", 4, 3, "RDNA1", "amd"),
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# Intel Arc Alchemist — tier 4 (improving ROCm/IPEX-LLM support)
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GpuSpec("Arc A770M", 16, 4, "Alchemist", "intel"),
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GpuSpec("Arc A550M", 8, 4, "Alchemist", "intel"),
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GpuSpec("Arc A370M", 4, 4, "Alchemist", "intel"),
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GpuSpec("Arc A350M", 4, 4, "Alchemist", "intel"),
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]
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def _build_patterns() -> list[tuple[re.Pattern[str], GpuSpec]]:
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"""Compile regex patterns, sorted longest-model-name first to prevent prefix shadowing."""
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result = []
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for spec in sorted(_GPU_DB, key=lambda s: -len(s.model)):
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# Allow optional space or hyphen between tokens (e.g. "RTX3070" or "RTX-3070")
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escaped = re.escape(spec.model).replace(r"\ ", r"[\s\-]?")
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result.append((re.compile(escaped, re.IGNORECASE), spec))
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return result
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_PATTERNS: list[tuple[re.Pattern[str], GpuSpec]] = _build_patterns()
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def parse_gpu(title: str) -> GpuSpec | None:
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"""Return the first GPU model found in a listing title, or None."""
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for pattern, spec in _PATTERNS:
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if pattern.search(title):
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return spec
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return None
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def score_gpu(spec: GpuSpec, vram_weight: float, arch_weight: float) -> GpuMatch:
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"""Compute normalized inference value scores for a GPU spec.
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vram_score: linear scale, 24 GB anchors at 100. Capped at 100.
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arch_score: linear scale, tier 1 = 0, tier 5 = 100.
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inference_score: weighted average of both, normalized to the total weight.
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"""
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vram_score = min(100.0, (spec.vram_gb / 24.0) * 100.0)
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arch_score = ((spec.arch_tier - 1) / 4.0) * 100.0
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total_weight = vram_weight + arch_weight
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if total_weight <= 0:
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inference_score = 0.0
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else:
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inference_score = (
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vram_score * vram_weight + arch_score * arch_weight
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) / total_weight
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return GpuMatch(
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spec=spec,
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vram_score=round(vram_score, 1),
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arch_score=round(arch_score, 1),
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inference_score=round(inference_score, 1),
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)
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