DeepSeek-R1 on RTX 3090: What Actually Works（英文原文）
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推荐先阅读英文页： https://localvram.com/en/blog/deepseek-r1-on-rtx-3090-what-works/
RTX 3090 remains one of the best value cards for local LLM work in 2026, but success depends on quantization and context discipline.
Baseline guidance
- Prioritize Q4 for larger model variants
- Cap context for sustained runs
- Monitor thermal drop-off over one-hour windows
Typical failure modes
- OOM on aggressive context settings
- Throughput drops under heat and long sessions
- Instability when combining large context and high output token counts
Recommended workflow
- Start with a conservative context budget.
- Validate latency and throughput on your real prompt set.
- Run sustained load and compare start vs end tokens/s.
- Publish verification logs for reproducibility.
Decision checkpoint
If you need predictable long-context performance, combine local 3090 daily workloads with cloud fallback for peak sessions.