Which RTX to use for running LLMs in 2026: comparison by model

NVIDIA dominates local LLM inference in 2026, mainly thanks to its mature CUDA ecosystem and the huge amount of VRAM available even on mobile GPUs. But picking the wrong RTX can leave you unable to run the LLM you wanted. This guide tells you which specific RTX fits each model size, what tokens/s to expect, and when Apple Silicon is a valid alternative.

Why NVIDIA dominates local inference

Three reasons:

• CUDA: the most mature GPU acceleration framework. Tools like llama.cpp, vLLM, exllamav2, tabbyAPI are optimized for it.
• Available VRAM: consumer RTX go up to 24 GB (4090), 32 GB (5090 desktop) or more on Pro line. AMD and Intel typically max out at 16 GB.
• Throughput: Tensor Cores architecture specifically accelerates AI ops. An RTX 4070 is 4-5x faster than its CPU equivalent.

Minimum VRAM by LLM size

Practical rule for Q4_K_M quantized models:

| Model | Params | VRAM Q4 | VRAM Q5 | VRAM Q6 | |---|---:|---:|---:|---:| | Llama 3.2 1B | 1B | 1 GB | 1 GB | 1.5 GB | | Llama 3.2 3B | 3B | 2 GB | 2.5 GB | 3 GB | | Llama 3.1 8B | 8B | 5 GB | 6 GB | 7 GB | | Mistral 7B | 7B | 4.5 GB | 5.5 GB | 6.5 GB | | Llama 3.1 13B | 13B | 8 GB | 9.5 GB | 11 GB | | Qwen 2.5 14B | 14B | 8.5 GB | 10 GB | 12 GB | | DeepSeek R1 14B | 14B | 8.5 GB | 10 GB | 12 GB | | Qwen 2.5 32B | 32B | 19 GB | 22 GB | 26 GB | | DeepSeek R1 32B | 32B | 19 GB | 22 GB | 26 GB | | Llama 3.3 70B | 70B | 40 GB | 47 GB | 56 GB |

Add 1-3 GB extra for context (depends on length).

RTX 4060 mobile (8 GB) — entry-level inference

• VRAM: 8 GB GDDR6
• TGP: 75-140 W depending on laptop
• Typical laptop price: $1,000-1,400 USD

Models it runs comfortably: Llama 3.2 3B, Llama 8B Q4, Mistral 7B Q5, Qwen 7B.

Typical tokens/s: 25-40 t/s on Llama 8B Q4.

Doesn't run: anything above 8B without offload to RAM (brutal speed drop).

RTX 4070 / 4070 Ti mobile (8 GB) — mainstream

• VRAM: 8 GB GDDR6
• TGP: 80-140 W

Same VRAM as the 4060 but more compute. Improves throughput by 30-40% on the same models. Still limited to models up to 8B without offload.

If you'll do local AI, the 4060 is better value than the 4070 at equal VRAM, unless you also game a lot.

RTX 4080 / 4080 Super mobile (12 GB) — serious mainstream

• VRAM: 12 GB GDDR6
• TGP: 100-175 W

This is where it gets interesting. Covers Llama 13B in Q5/Q6 comfortably, Qwen 14B Q5, vision models, Stable Diffusion XL with LoRAs.

Typical tokens/s: 20-30 t/s on Llama 13B Q4, 12-18 t/s on Llama 13B Q5.

RTX 5070 / 5070 Ti mobile (12 GB) — 2025 refresh

• VRAM: 12 GB GDDR7
• TGP: 90-150 W

Same VRAM as 4080, better efficiency and GDDR7 support. For local AI basically equivalent to RTX 4080 but with better battery under load.

RTX 5080 mobile (16 GB) — high-end

• VRAM: 16 GB GDDR7
• TGP: 100-175 W

Now you're serious. Covers 13B in Q6/Q8, Llama 30B Q4 with minimal offload, light LoRA fine-tuning, SDXL with multiple controls.

Tokens/s: 30-50 t/s on Llama 13B Q4, 8-12 t/s on Llama 30B Q4.

RTX 5090 mobile (24 GB) — top tier

• VRAM: 24 GB GDDR7
• TGP: 120-175 W

The maximum on laptop. Runs Llama 30B in Q5/Q6 comfortably, 70B models with moderate RAM offload.

Typical laptop price: $3,500-5,500 USD.

RTX A6000 / RTX Pro 6000 — workstation

Pro lines with 48 GB of VRAM. Only on desktop workstations or Dell Precision / HP ZBook laptops at $6,000+ USD. Doesn't make sense for home use.

Expected throughput by model

Tokens per second under typical conditions (100% GPU, medium context):

| GPU | Llama 8B Q4 | Llama 13B Q4 | Llama 30B Q4 | |---|---:|---:|---:| | RTX 4060 | 30 t/s | with offload | unviable | | RTX 4070 | 40 t/s | 18 t/s offload | unviable | | RTX 4080 | 50 t/s | 25 t/s | with offload | | RTX 5070 | 55 t/s | 30 t/s | with offload | | RTX 5080 | 70 t/s | 40 t/s | 10 t/s | | RTX 5090 | 90 t/s | 55 t/s | 20 t/s |

For reference: comfortable human reading is 5-8 tokens/s. Above that, text appears faster than you can read.

Alternatives to NVIDIA

Apple Silicon (Mac M5 Pro/Max)

Key advantage: unified memory. What on NVIDIA would be 64 GB of VRAM (impossible on laptop), on M5 Max is simply "64 GB total RAM". Lets you run Llama 70B Q4 on a laptop.

Penalty: raw tokens/sec is 20-40% lower than equivalent NVIDIA. But throughput stays usable.

Snapdragon X2 Elite (NPU)

80 TOPS NPU. Covers small models (3B-7B) with good battery. For casual and mobile, perfect. For serious, falls short.

AMD Radeon RX 7800S / 7900M

Compatible with ROCm but software ecosystem immature compared to CUDA. If you already have one and want to experiment, works. To buy new, NVIDIA wins on ease.

Final recommendation

• Try local AI without spending much: RTX 4060 mobile on a ~$1,300 USD laptop. Covers 80% of use cases.
• Serious daily use: RTX 4080 / 5070 mobile + 32 GB RAM. ~$2,000-2,500 USD. Setup that covers 13B models with quality.
• Large models on laptop: MacBook Pro M5 Max with 64-96 GB. ~$3,500-5,000 USD.
• Pro / fine-tuning: desktop with RTX 5090 + 64 GB RAM, not laptop.

FAQ

Is RTX 4090 mobile (16 GB) worth it when RTX 5080 (16 GB) exists? Same VRAM, the 5080 brings GDDR7 and better efficiency. Prefer 5080 unless 4090 is significantly cheaper.

Can I combine two RTX in a laptop for more VRAM? No. Laptops don't support multi-GPU. Only desktops with NVLink.

Will RTX 5060 mobile (when released) be better than 4060? Probably yes in efficiency and slightly in throughput. But still 8 GB VRAM, same model ceiling.

Do I need CUDA or does DirectML/Vulkan work too? CUDA is 2-5x faster for LLMs. DirectML exists but is far behind. For serious work, NVIDIA + CUDA.

Which specific laptop suits you?

Tell the AI advisor what models you want to run (Llama 70B, DeepSeek R1, Qwen 32B...) and your budget. You'll know the laptop with the specific RTX you need and the expected throughput.