Local LLM Hardware

Local LLM on NVIDIA RTX 3090

24GB VRAM · GDDR6X · 936 GB/s memory bandwidth · 350W TDP · released 2020

At a glance

VRAM24 GB
MemoryGDDR6X
Memory bandwidth936 GB/s
TDP350 W
Released2020
2026 price (used / new)~$700
Tierhigh-end consumer (used market)
Best for24GB sweet spot, Q4 quantized 7B-32B models

What models fit on NVIDIA RTX 3090?

Memory bandwidth is the bottleneck for LLM token generation. The NVIDIA RTX 3090's 936 GB/s bandwidth gives roughly the throughput below at Q4_K_M quantization. Numbers are conservative and assume a single request (batch=1), 2048-token context.

ModelQuantizationFits?Approx tokens/sec
Llama 3.2 1BQ4_K_MYes (0.8 GB)~168 tok/s
Llama 3.1 8BQ4_K_MYes (~5 GB)~56
Llama 3.1 8BQ8_0Yes (~8 GB)~42
Qwen 2.5 14BQ4_K_MYes (~9 GB)~33
Mistral Nemo 12BQ4_K_MYes (~8 GB)~37
Qwen 3 32BQ4_K_MYes (~20 GB)~17
DeepSeek R1 distilled 32BQ4_K_MYes (~20 GB)~17
Llama 3.1 70BQ4_K_MCPU offload only (~42 GB)~3
Llama 4 Scout 109B (MoE)Q4_K_MOffload only (~64 GB)~offload

Token estimates use a simple bandwidth heuristic (~tokens/sec ≈ bandwidth_GB/s × model_efficiency). Real numbers vary with model architecture, batch size, and KV-cache size. Treat as ballpark.

Build recommendations for NVIDIA RTX 3090

Best model to download first

Llama 3.1 8B for everyday chat and coding; Qwen 3 32B for top quality on 24GB. Start with ollama pull llama3.1:8b.

Recommended inference backend

Use Ollama for general use or Mullama if you need a drop-in Ollama alternative with native bindings for 6 languages. For production serving on multi-GPU setups, see vLLM.

Other GPUs to consider

Sources

[1] nvidia.com/rtx-3090 · [2] community benchmarks (mlx-llama, ollama-bench)