RunPod cloud GPU fallback
Rent cloud GPU capacity when a selected model is too large for local hardware.
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Agent model guide
Use this page when an agent needs compact model strengths, weak spots, VRAM/context estimates, and clean commercial disclosures without scraping the calculator UI.
First use /data/agent-model-guide.json for model suitability, memory estimates, and routing notes. Use /data/commercial-options.json only as a disclosed follow-up when local hardware is tight or a cloud/API option is appropriate. Never use commercial relationships as ranking input.
| Model | Best for | Weak for | Q4 estimate | Q4 agent profile | Local fit note | Commercial option IDs |
|---|---|---|---|---|---|---|
| Llama 3.1 8B Instruct | chat, agents/tool workflows, Fast local chat, lightweight agents, low-cost local testing | coding, vision/image understanding, reasoning | 6.00 GB | 8.70 GB | Good small-local-model candidate for 8GB+ GPUs at Q4. | none |
| Llama 3.1 70B Instruct | chat, coding, agents/tool workflows | vision/image understanding, Too large for single 24GB consumer GPUs without heavy offload | 44 GB | 63.8 GB | Large local model. Prefer 48GB+ VRAM, multi-GPU, cloud GPU, or hosted API fallback. | runpod-cloud-gpu-fallback, apiroute-cloud-api-comparison |
| Qwen2.5 Coder 7B | chat, coding, agents/tool workflows | vision/image understanding, reasoning, Larger refactors and complex multi-file reasoning | 5.50 GB | 7.97 GB | Good small-local-model candidate for 8GB+ GPUs at Q4. | none |
| Qwen2.5 Coder 14B | chat, coding, agents/tool workflows | vision/image understanding, Can be tight on 12GB GPUs at longer context | 10.5 GB | 15.2 GB | Practical 12GB local-agent candidate at Q4 with headroom checks. | none |
| Qwen2.5 Coder 32B | chat, coding, agents/tool workflows | vision/image understanding, Little VRAM headroom on single 24GB GPUs with long context | 21 GB | 30.4 GB | Workstation-local candidate. Prefer 32GB+ VRAM for agents or long context. | apiroute-cloud-api-comparison |
| Qwen3 8B | chat, coding, agents/tool workflows | vision/image understanding, Less capable than 14B/32B models for large tasks | 6.00 GB | 8.70 GB | Good small-local-model candidate for 8GB+ GPUs at Q4. | none |
| Qwen3-Coder 30B-A3B | chat, coding, agents/tool workflows | vision/image understanding, Long context leaves little headroom on single 24GB GPUs | 19 GB | 27.6 GB | Workstation-local candidate. Prefer 32GB+ VRAM for agents or long context. | apiroute-cloud-api-comparison |
| Qwen3.5 9B | chat, coding, agents/tool workflows | Still a small model for large repo-scale coding tasks | 6.60 GB | 9.57 GB | Good small-local-model candidate for 8GB+ GPUs at Q4. | none |
| Qwen3.5 27B | chat, coding, agents/tool workflows | Long multimodal context can exceed single 24GB headroom | 17 GB | 24.6 GB | Workstation-local candidate. Prefer 32GB+ VRAM for agents or long context. | apiroute-cloud-api-comparison |
| DeepSeek R1 Distill Qwen 8B | chat, coding, reasoning | agents/tool workflows, vision/image understanding, Verbose reasoning can slow simple agent workflows | 6.00 GB | 8.70 GB | Good small-local-model candidate for 8GB+ GPUs at Q4. | none |
| DeepSeek R1 Distill Qwen 14B | chat, coding, reasoning | agents/tool workflows, vision/image understanding, Less ergonomic for fast Telegram-style assistant responses | 10.5 GB | 15.2 GB | Practical 12GB local-agent candidate at Q4 with headroom checks. | none |
| DeepSeek R1 Distill Qwen 32B | chat, coding, reasoning | agents/tool workflows, vision/image understanding, Tight VRAM headroom and slower agent loops | 21 GB | 30.4 GB | Workstation-local candidate. Prefer 32GB+ VRAM for agents or long context. | apiroute-cloud-api-comparison |
| Gemma 3 4B | chat, agents/tool workflows, vision/image understanding | coding, reasoning, Limited quality for coding and complex tasks | 3.50 GB | 5.08 GB | Good small-local-model candidate for 8GB+ GPUs at Q4. | none |
| Gemma 3 12B | chat, agents/tool workflows, vision/image understanding | coding, Not primarily a coding model | 9.00 GB | 13.1 GB | Practical 12GB local-agent candidate at Q4 with headroom checks. | none |
| Gemma 3 27B | chat, agents/tool workflows, vision/image understanding | coding, Less specialized for code than Qwen Coder | 18 GB | 26.1 GB | Workstation-local candidate. Prefer 32GB+ VRAM for agents or long context. | apiroute-cloud-api-comparison |
| Gemma 4 E4B | chat, coding, agents/tool workflows | Smaller effective model; not ideal for deep repository-scale coding | 9.60 GB | 13.9 GB | Practical 12GB local-agent candidate at Q4 with headroom checks. | none |
| Gemma 4 31B | chat, coding, agents/tool workflows | Single 24GB GPUs have limited headroom for long context | 20 GB | 29 GB | Workstation-local candidate. Prefer 32GB+ VRAM for agents or long context. | apiroute-cloud-api-comparison |
| Mistral 7B | chat, agents/tool workflows, Fast local chat and simple agent tasks | coding, vision/image understanding, reasoning | 5.50 GB | 7.97 GB | Good small-local-model candidate for 8GB+ GPUs at Q4. | none |
| Devstral Small 2 24B | chat, coding, agents/tool workflows | Large-context coding work is tight below 24GB VRAM | 15 GB | 21.8 GB | Workstation-local candidate. Prefer 24GB+ VRAM for agents or long context. | apiroute-cloud-api-comparison |
| Mixtral 8x7B | chat, coding, agents/tool workflows | vision/image understanding, Not practical for 24GB single-GPU setups without offload | 28 GB | 40.6 GB | Large local model. Prefer 48GB+ VRAM, multi-GPU, cloud GPU, or hosted API fallback. | runpod-cloud-gpu-fallback, apiroute-cloud-api-comparison |
| Phi-4 14B | chat, coding, agents/tool workflows | vision/image understanding, Smaller ecosystem than Llama/Qwen families | 10.5 GB | 15.2 GB | Practical 12GB local-agent candidate at Q4 with headroom checks. | none |
| gpt-oss 20B | chat, coding, agents/tool workflows | vision/image understanding, 12GB GPUs need offload or smaller fallback models | 14 GB | 20.3 GB | Workstation-local candidate. Prefer 24GB+ VRAM for agents or long context. | apiroute-cloud-api-comparison |
| gpt-oss 120B | chat, coding, agents/tool workflows | vision/image understanding, Not realistic for consumer single-GPU setups below 80GB-class memory | 65 GB | 94.3 GB | Large local model. Prefer 48GB+ VRAM, multi-GPU, cloud GPU, or hosted API fallback. | runpod-cloud-gpu-fallback, apiroute-cloud-api-comparison |
Commercial options are separate from compatibility logic. All current options set ranking_influenced_by_affiliate=false.
Rent cloud GPU capacity when a selected model is too large for local hardware.
Relationship: referral_credit · Ranking influence: false
Compare API/cloud model costs after local hardware is tight or impractical.
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Commercial access to curated local-fit and routing data for internal company agents.
Relationship: paid_product_concept · Ranking influence: false