Answer nine questions. We rank the GPUs in our catalog by fit for local AI on your stack — top picks, alternates, and what to avoid. Hand-written rationale per card, honest caveats, and a one-click handoff into the custom build engine.
We don’t fake tok/s numbers. Every recommendation cites a model class and a workload-realistic range. Cards over your budget appear last with explicit framing. Recommendations are rule-based scoring, not measured benchmarks.
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Top pick for your setup. With your $300 budget on Linux for coding agents, the NVIDIA GeForce RTX 5090 Mobile ranks here because 24 GB hits the workable band for coding agents — fits at sensible quants without becoming the bottleneck.
Each dimension is a 0-100 score. The card's position in the ranking is the weighted sum — but we surface tiers, not raw numbers. Bars are sorted by weight (most-influential first).
Tier mapping: top ≥ 75 composite · alternate 60-74 · acceptable 40-59 · avoid < 40 or over-budget / incompatible.
Strong alternate. With your $300 budget on Linux for coding agents, the NVIDIA GeForce RTX 4090 Mobile sits in this tier on a balance of capability, OS compat, power, and budget fit.
Each dimension is a 0-100 score. The card's position in the ranking is the weighted sum — but we surface tiers, not raw numbers. Bars are sorted by weight (most-influential first).
Tier mapping: top ≥ 75 composite · alternate 60-74 · acceptable 40-59 · avoid < 40 or over-budget / incompatible.
Strong alternate. With your $300 budget on Linux for coding agents, the NVIDIA GeForce RTX 3080 16GB (Mobile) sits in this tier on a balance of capability, OS compat, power, and budget fit.
Each dimension is a 0-100 score. The card's position in the ranking is the weighted sum — but we surface tiers, not raw numbers. Bars are sorted by weight (most-influential first).
Tier mapping: top ≥ 75 composite · alternate 60-74 · acceptable 40-59 · avoid < 40 or over-budget / incompatible.
Strong alternate. With your $300 budget on Linux for coding agents, the Intel Arc A770 16GB sits in this tier on a balance of capability, OS compat, power, and budget fit.
Each dimension is a 0-100 score. The card's position in the ranking is the weighted sum — but we surface tiers, not raw numbers. Bars are sorted by weight (most-influential first).
Tier mapping: top ≥ 75 composite · alternate 60-74 · acceptable 40-59 · avoid < 40 or over-budget / incompatible.
Out of budget for this query. With your $300 budget on Linux for coding agents, the NVIDIA RTX 2080 Ti 22GB (China-mod) ranks here because street price around $350 sits above your $300 budget — listed for the upgrade-path conversation, not as a recommendation.
Each dimension is a 0-100 score. The card's position in the ranking is the weighted sum — but we surface tiers, not raw numbers. Bars are sorted by weight (most-influential first).
Tier mapping: top ≥ 75 composite · alternate 60-74 · acceptable 40-59 · avoid < 40 or over-budget / incompatible.
Multi-store, multi-region prices for every card here. US/EU/UK/CA/AU — see what these cards actually cost in your region before you buy.
One step further: this card + runtime + 1-3 models + cost rollup + ready-to-paste install script. Eight inputs → full rig.
Once you’ve picked a card, model the full build (CPU, RAM, runtime) for which models fit comfortably.
The long-form essay version: VRAM tiers, MoE math, NVLink truth, used-market price discipline.
Curated multi-GPU and Apple-cluster setups with effective-VRAM math you can trust.
How the trust layer behind these recommendations actually works — every dimension, every formula, the honest limits.
Where the intelligence graph has signal vs which model × hardware × quant cohorts are still underpowered.
Help tip a cohort across the 5-row threshold for outlier detection — the most operator-impactful contribution.