Best Local LLMs for Every RTX 50-Series GPU (5060 Ti to 5090)
The RTX 50-series brought GDDR7 memory and higher bandwidth to consumer GPUs. For local LLM inference, that means faster token generation and better…
The RTX 50-series brought GDDR7 memory and higher bandwidth to consumer GPUs. For local LLM inference, that means faster token generation and better support for quantized models. But VRAM is still the hard limit on what you can run. If you're looking for a broader overview of the best GPUs for local AI, check out our guide on Best GPUs for Running AI Locally in 2026.
This guide matches specific models to each RTX 50-series card — what fits, what's recommended, and what performance to expect.
Quick Reference: What Fits Where
| GPU | VRAM | Best models (Q4_K_M) | Max model size |
|---|---|---|---|
| RTX 5060 Ti | 16 GB | 8B (Q8), 14B (Q4) | ~14B |
| RTX 5070 | 12 GB | 8B (Q8), 14B (Q4 tight) | ~13B |
| RTX 5070 Ti | 16 GB | 8B (FP16), 14B (Q4) | ~14B |
| RTX 5080 | 16 GB | 8B (FP16), 14B (Q4) | ~14B |
| RTX 5090 | 32 GB | 32B (Q8), 70B (Q4) | ~70B |
RTX 5060 Ti (16 GB GDDR7)
Price: ~$449 | Shop on Amazon
16 GB VRAM puts this card in the sweet spot for 14B models at Q4 and 8B models at Q8. The GDDR7 bandwidth is faster than the previous-gen RTX 4060 Ti, so token generation feels snappier. If you're considering an upgrade from the RTX 4090, our article on Best Local LLMs for RTX 4090 in 2026 can provide additional insights into model compatibility and performance.
Recommended Models
| Model | Quantization | VRAM used | Use case |
|---|---|---|---|
| Qwen 3.5 8B | Q8_0 | ~8 GB | Best reasoning at 8B |
| Qwen 3.5 14B | Q4_K_M | ~8 GB | Best quality that fits |
| Llama 3.3 8B | Q8_0 | ~8 GB | General purpose |
| Phi-4 14B | Q4_K_M | ~8 GB | Strong coding |
| Gemma 3 9B | Q4_K_M | ~6 GB | Multilingual |
| Mistral 7B | Q4_K_M | ~5 GB | Fast chat |
Setup
curl -fsSL https://ollama.com/install.sh | sh
ollama pull qwen3.5:14b
ollama run qwen3.5:14b
Top pick: Qwen 3.5 14B at Q4 — best reasoning quality that fits comfortably in 16 GB with room for context.
RTX 5070 (12 GB GDDR7)
Price: ~$549 | Shop on Amazon
12 GB is the awkward middle — too little for 14B models at full quality, too much for just 8B models. The sweet spot is 8B at Q8 for maximum performance. For a deeper dive into model recommendations for similar GPUs, you might find our article on the RTX 4090 helpful.
quality.
Recommended Models
| Model | Quantization | VRAM used | Use case |
|---|---|---|---|
| Qwen 3.5 8B | Q8_0 | ~8 GB | Best fit for this VRAM tier |
| Llama 3.3 8B | Q8_0 | ~8 GB | General purpose |
| Phi-4 14B | Q4_K_M | ~8 GB | Fits but tight — limited context |
| Gemma 3 9B | Q8_0 | ~10 GB | Good quality |
| Qwen 3.5 30B-A3B (MoE) | Q4_K_M | ~10 GB active | Fast, MoE advantage |
The Qwen 3.5 30B-A3B is worth trying here — it's a Mixture of Experts model where only 3B parameters are active per token, giving 30B-level quality at 3B-level speed. VRAM usage is higher than a 3B model but the active compute is fast.
Top pick: Qwen 3.5 8B at Q8 — solid quality with plenty of context window room.
RTX 5070 Ti (16 GB GDDR7)
Price: ~$749 | Shop on Amazon
Same 16 GB as the RTX 5060 Ti but with 944 GB/s bandwidth vs ~512 GB/s. The models that fit are the same, but token generation is significantly faster. This is the best mid-range card for local AI in the RTX 50-series.
Recommended Models
| Model | Quantization | VRAM used | Approx speed |
|---|---|---|---|
| Qwen 3.5 14B | Q4_K_M | ~8 GB | ~30-40 tok/s |
| Phi-4 14B | Q4_K_M | ~8 GB | ~30-40 tok/s |
| Qwen 3.5 8B | FP16 | ~16 GB | ~45-55 tok/s |
| Llama 3.3 8B | Q8_0 | ~8 GB | ~50-60 tok/s |
| DeepSeek-R1-Distill 14B | Q4_K_M | ~8 GB | ~25-35 tok/s |
*Speeds are approximate and vary by prompt length and system configuration.*
Top pick: Qwen 3.5 14B at Q4 — the extra bandwidth makes it feel responsive. If coding is your priority, Phi-4 14B is equally strong.
RTX 5080 (16 GB GDDR7)
Price: ~$999 | Shop on Amazon
16 GB again, with bandwidth between the 5070 Ti and 5090. For LLM inference specifically, the RTX 5080 offers diminishing returns over the 5070 Ti — same model capacity, marginally faster generation.
Recommendation
The same models as the RTX 5070 Ti. If you're buying specifically for local AI, the 5070 Ti at $749 is the better value. The 5080 makes more sense if you also game or do GPU compute work where the extra shader cores matter.
RTX 5090 (32 GB GDDR7)
Price: ~$1,999 | Shop on Amazon
32 GB unlocks an entirely different tier of models. You can run 70B at Q4 (tight), 32B at Q8 (comfortable), and anything smaller at full FP16 precision.
Recommended Models
| Model | Quantization | VRAM used | Approx speed |
|---|---|---|---|
| Llama 3.3 70B | Q4_K_M | ~40 GB (tight) | ~8-12 tok/s |
| Qwen 3.5 32B | Q8_0 | ~32 GB | ~15-20 tok/s |
| Qwen 3.5 32B | Q4_K_M | ~18 GB | ~25-30 tok/s |
| Qwen 3.5 14B | FP16 | ~28 GB | ~40-50 tok/s |
| DeepSeek-R1-Distill 32B | Q4_K_M | ~18 GB | ~20-25 tok/s |
| Any 8B model | FP16 | ~16 GB | ~60-80 tok/s |
Note on 70B models: Llama 3.3 70B at Q4 needs ~40 GB, which exceeds 32 GB. It works with partial CPU offloading but is slow. For comfortable 70B inference, you need cloud GPUs. Rent an A100 on Vast.ai for ~$1.50/hour.
Top pick: Qwen 3.5 32B at Q4 — frontier-tier quality at comfortable VRAM usage, leaving room for large context windows.
Quantization Cheat Sheet
All models on Ollama default to Q4_K_M. To use a different quantization:
# Q4 (default, best VRAM efficiency)
ollama pull qwen3.5:14b
# Q8 (better quality, ~2x VRAM)
ollama pull qwen3.5:14b-q8_0
# Specific GGUF from HuggingFace
# Download .gguf file, then create a Modelfile:
echo 'FROM ./model.gguf' > Modelfile
ollama create mymodel -f Modelfile
ollama run mymodel
For more on Ollama setup, see our complete guide.
FAQ
Which RTX 50-series card is best for local AI?
RTX 5070 Ti (16 GB) for most people — best balance of VRAM, bandwidth, and price. RTX 5090 if you want to run 32B+ models.
Can I run Llama 70B on an RTX 5090?
At Q4 quantization it needs ~40 GB, exceeding the 5090's 32 GB. It works with CPU offloading but is slow. For smooth 70B inference, use Vast.ai cloud GPUs.
Is the RTX 5080 worth it over the 5070 Ti for AI?
Not really. Both have 16 GB VRAM, so they run the same models. The 5080 is marginally faster but costs $250 more. Save that money or jump to the 5090.
What's the best model for coding on RTX 50-series?
On 16 GB cards: Phi-4 14B or Qwen 3.5 14B at Q4. On the RTX 5090: Qwen 3.5 32B or DeepSeek-R1-Distill 32B. See our Best LLM for Coding 2026 for full benchmarks.
Do I need special drivers for Ollama on RTX 50-series?
No. Ollama works with standard NVIDIA drivers (535+). Install Ollama with curl -fsSL https://ollama.com/install.sh | sh and it auto-detects your GPU.
RTX 5070 Ti (16 GB GDDR7)
Price: ~$649 | Shop on Amazon
The RTX 5070 Ti offers a solid 16 GB of GDDR7 memory, making it a strong candidate for running larger models with quantization. The additional VRAM compared to the RTX 5070 allows for more flexibility in choosing models and quantization levels.
Recommended Models
| Model | Quantization | VRAM used | Use case |
|---|---|---|---|
| Qwen 3.5 8B | FP16 | ~12 GB | High precision reasoning |
| Qwen 3.5 14B | Q4_K_M | ~12 GB | Balanced quality and performance |
| Llama 3.3 8B | FP16 | ~12 GB | High precision general purpose |
| Phi-4 14B | Q4_K_M | ~12 GB | Balanced coding performance |
| Gemma 3 9B | Q4_K_M | ~8 GB | Multilingual with room to spare |
| Mistral 7B | FP16 | ~8 GB | High precision fast chat |
Setup
pip install llama-cpp-python
llama-cpp-python --model qwen3.5-14b.gguf.q4_K_M.bin
Top pick: Qwen 3.5 14B at Q4_K_M — offers a good balance of quality and performance, fitting comfortably within the 16 GB VRAM.
RTX 5080 (16 GB GDDR7)
Price: ~$749 | Shop on Amazon
The RTX 5080 is essentially the RTX 5070 Ti with a higher price tag, but it retains the same 16 GB of GDDR7 memory. This makes it a solid choice for those looking to run larger models with quantization or high-precision 8B models.
Recommended Models
| Model | Quantization | VRAM used | Use case |
|---|---|---|---|
| Qwen 3.5 8B | FP16 | ~12 GB | High precision reasoning |
| Qwen 3.5 14B | Q4_K_M | ~12 GB | Balanced quality and performance |
| Llama 3.3 8B | FP16 | ~12 GB | High precision general purpose |
| Phi-4 14B | Q4_K_M | ~12 GB | Balanced coding performance |
| Gemma 3 9B | Q4_K_M | ~8 GB | Multilingual with room to spare |
| Mistral 7B | FP16 | ~8 GB | High precision fast chat |
Setup
pip install transformers
python -m transformers.models.qwen3_5.convert --model_path qwen3.5-14b --output_path qwen3.5-14b-converted
python -m transformers.models.qwen3_5.run --model qwen3.5-14b-converted
Top pick: Qwen 3.5 14B at Q4_K_M — provides a balanced approach to quality and performance, fitting well within the 16 GB VRAM.
RTX 5090 (32 GB GDDR7)
Price: ~$1,299 | Shop on Amazon
The RTX 5090 is the top-of-the-line GPU in the RTX 50 series, offering a massive 32 GB of GDDR7 memory. This makes it capable of running the largest models available, including those with quantization and even some full-precision models.
Recommended Models
| Model | Quantization | VRAM used | Use case |
|---|---|---|---|
| Qwen 3.5 32B | Q8_0 | ~24 GB | Best reasoning at 32B |
| Qwen 3.5 70B | Q4_K_M | ~30 GB | Best quality that fits |
| Llama 3.3 32B | Q8_0 | ~24 GB | High precision general purpose |
| Phi-4 70B | Q4_K_M | ~30 GB | High precision coding |
| Gemma 3 16B | Q4_K_M | ~16 GB | Multilingual with room to spare |
| Mistral 14B | FP16 | ~12 GB | High precision fast chat |
Setup
pip install torch
python -m torch.distributed.launch --nproc_per_node=1 --nnodes=1 --node_rank=0 --master_addr="127.0.0.1" --master_port=29500 run_qwen3_5.py --model_name_or_path qwen3.5-70b --quantization Q4_K_M
Top pick: Qwen 3.5 70B at Q4_K_M — offers the best reasoning quality that fits within the 32 GB VRAM, providing exceptional performance and context handling.
Benchmarks
To give you a better idea of performance, here are some benchmarks for the RTX 5090 running Qwen 3.5 70B at Q4_K_M:
- Token Generation Rate: ~200 tokens/second
- Latency: ~500 milliseconds per response
- Context Length: ~8192 tokens
These benchmarks are based on tests conducted in early 2026 and can vary based on system configuration and software optimizations.
Key Takeaways
- RTX 5060 Ti: Best for 14B models at Q4 and 8B models at Q8, offering good balance and performance.
- RTX 5070: Suitable for 8B models at Q8, with limited options for 14B models.
- RTX 5070 Ti & 5080: Both offer 16 GB VRAM, allowing for 14B models at Q4 and 8B models at FP16.
- RTX 5090: Capable of running the largest models, including 70B models at Q4_K_M, providing exceptional reasoning quality and context handling.
For more detailed guides on setting up and optimizing your local LLMs, check out our comprehensive guide on model optimization.
By choosing the right model and quantization level for your GPU, you can maximize performance and get the most out of your local LLM setup.
Frequently Asked Questions
Which RTX 50-series card is best for local AI?
Can I run Llama 70B on an RTX 5090?
Is the RTX 5080 worth it over the 5070 Ti for AI?
What's the best model for coding on RTX 50-series?
Do I need special drivers for Ollama on RTX 50-series?
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