Best Local LLM for Every RTX 50 Series GPU (2026 Model-GPU Matrix)

You already own (or are about to buy) an RTX 50-series GPU. The question isn't "which card is best for AI?" — every GPU roundup on the internet already answers that. The real question is the reverse: "I have an RTX 5070 Ti. What should I actually run on it?"

This guide is the model-to-GPU matrix no one else is writing. For each VRAM tier in the RTX 50 stack, we match the best local LLM at Q4 quantization, runner-ups for coding and vision, cited tok/s benchmarks, and the specific MoE corrections that most competitor guides get wrong. For the broader buying-side view, see our GPU buying guide; this post is what to do after you've bought one.

The bottom line: for local AI in 2026, the RTX 5080 is the best single-GPU pairing with Gemma 4 27B or Qwen 3 27B at Q4 quantization; the RTX 5090 is the only consumer card that runs Llama 4 Scout's full 109B-parameter MoE locally at Q4 without offloading; and any RTX 50 card below 16GB of VRAM is effectively capped at 14B-parameter models once multimodal overhead is counted.

The RTX 50 Series for Local AI — At a Glance

The Blackwell generation matters for local inference for three concrete reasons. Fifth-generation tensor cores doubled throughput over Ada Lovelace for INT4/INT8 workloads. Native FP4 is supported for the first time on consumer GPUs — the emerging quantization format that preserves more quality than INT4 at the same memory footprint. And GDDR7 bumped bandwidth by 33–55% across the stack, which is the single biggest bottleneck for interactive LLM tok/s.

Here's the full stack at a glance, with the one model you should be running on each card in April 2026:

A pricing caveat: the ongoing DRAM shortage has pushed 2026 GPU prices $15–$30 above MSRP for every additional VRAM tier — see our DRAM shortage GPU analysis. NVIDIA has also confirmed no new gaming GPUs in 2026 — the RTX 50 Super refresh pushed to Q3 at the earliest — so the current stack is what you're working with for at least the next six months. For a broader market view, see what to buy for local AI in 2026.

How We Matched Models to Cards

Most "best local LLM for GPU X" posts hand-wave the VRAM math. We don't. Every recommendation in this guide follows four explicit rules:

1. Fit the model in ~80% of VRAM at Q4 or Q5. The remaining 20% is for the KV cache, activations, and OS overhead. A 13GB model on a 16GB GPU has almost no context budget; a 10GB model on the same card runs comfortably at 16K+ context.
2. Q4_K_M is the default quantization. It's the best quality/size trade-off for every modern model family — Unsloth, the llama.cpp team, and Julien Simon (AI infrastructure analyst, ex-AWS) all converge on Q4_K_M as the production standard in 2026. Go up to Q5 or Q8 only if you have headroom.
3. Multimodal models carry a ~1.4GB vision-encoder tax. This is the input almost no competitor guide surfaces. Qwen3-VL, Gemma 4 multimodal, Apriel 1.5 and similar models add a fixed ~1.4GB overhead for the vision tower regardless of prompt content. On an 8GB card, that's 17.5% of total memory gone before you load the language model. Vision is effectively off the table below 12GB.
4. MoE effective VRAM is closer to active-parameter count for compute, but full-parameter count for memory. Llama 4 Scout (109B total, 8B active) and Qwen3-30B-A3B (30B total, 3B active) generate tokens like small dense models but must be fully loaded into VRAM. This changes which cards can run them entirely. Our VRAM math uses the total parameter count for memory, active for speed.

For the deeper version of this math — activation memory, Flash Attention, batch-size scaling — see our VRAM requirements guide. For the runtime side, our Ollama setup walkthrough and llama.cpp notes cover the software layer.

RTX 5090 (32GB GDDR7) — Flagship for 70B-Class Local AI

The RTX 5090 ($1,999 – $2,199) is in a class of its own. With 32GB of GDDR7 at 1,792 GB/s and 21,760 CUDA cores, it's the only consumer GPU that runs the flagship open-weight MoE models locally without offloading or multi-GPU setups.

Best pick: Llama 4 Scout (109B total, 8B active, Q4). This is the model the RTX 5090 was built for in 2026. At Q4 the full 109B parameter MoE fits in ~24GB of VRAM — leaving 8GB of headroom for 32K+ context. Because only 8B parameters are active per token, you get dense-7B inference speed (18–22 tok/s on the 5090 per LM Studio Community benchmarks) with the reasoning quality of a much larger model. No other single consumer GPU runs this model at Q4 without offloading. See our full Llama 4 hardware guide for the complete VRAM breakdown.

Runner-ups:

• Qwen 3 72B at Q4 (~45GB — tight, requires aggressive Q3_K_M or KV cache offload). On a clean Q4, the dense 72B exceeds 32GB; most users will run it at Q3_K_M (~28GB) or use Llama 4 Maverick 70B which is tuned for 32GB cards.
• DeepSeek R1 70B at Q4 for reasoning-heavy workloads like math, code review, and agent planning. The 5090 hits 15–18 tok/s on R1 70B Q4 per community benchmarks — see our DeepSeek R1 local setup guide.
• Image and video generation: Flux.1-dev runs at native FP16 in 24GB — the 5090's 32GB leaves room for 2048×2048 generations and LoRA adapters. HunyuanVideo Q8 also fits comfortably for local video generation. See our video generation GPU guide.

Benchmark table — RTX 5090 top 3 model picks:

Used-market alternative: the RTX 3090 ($699 – $999) with 24GB is still the best value 24GB-class card, but Julien Simon flagged in his April 2026 Medium roundup that "used 3090 pricing has inflated to within $300 of a new RTX 5080 — the value calculus has shifted toward new Blackwell cards for anyone not specifically targeting 70B dense models." For the cross-gen head-to-head, see RTX 5090 vs RTX 4090 and our full 5090 vs 4090 breakdown.

RTX 5080 (16GB GDDR7) — Sweet Spot for 13B–30B Models

The RTX 5080 ($999 – $1,099) is the card to beat for single-GPU local AI in the under-$1,200 bracket. 16GB of GDDR7 at 960 GB/s and 10,752 CUDA cores mean it's not just enough for 27B dense models — it's fast at them.

Best pick: Qwen 3 27B at Q4. At 15GB VRAM used, it fits the 80% rule with enough room for 8K–16K context. LM Studio Community benchmarks consistently place the RTX 5080 at 48–54 tok/s on Qwen 3 27B Q4 — well above the interactive threshold. Qwen 3 27B is also our default recommendation for multilingual work and code-adjacent tasks.

Runner-ups:

• Gemma 4 27B at Q4: tied with Qwen 3 27B on quality, slightly stronger on English reasoning. Vision overhead pushes it to ~16.5GB total — usable but leaves no context headroom on 16GB. Drop to Gemma 4 14B Q5 if you need multimodal.
• Mistral Small 4 7B at FP16: for maximum quality on a smaller model, the 7B at full FP16 (14GB) gives you the best text quality the 5080 can produce in any parameter count. See our Mistral model page.
• Coding: CodeLlama 34B at Q4 (~19GB — requires aggressive Q3) or Qwen 2.5 Coder 32B at Q4 (~18GB — same constraint). The cleaner choice on the 5080 is Qwen 2.5 Coder 14B at Q8, which fits in 15GB and outperforms many 34B Q3 configs. For the full setup, see our AI coding rig guide.
• Emerging pick: GPT-OSS 20B has been trending on r/LocalLLaMA as the best text-only sub-20B model for 16GB cards (13.7GB at Q4, ~42 tok/s on the 5080 per community threads).

Multimodal caveat: at 16GB, vision models are tight. Expect to trade off context length for images. If vision is your primary use case, the RTX 5090's 32GB is the comfortable tier. For the cross-gen comparison, see RTX 5080 vs RTX 4080 SUPER, and our RTX 5080 vs RTX 4090 analysis for the Ada Lovelace question.

RTX 5070 Ti (16GB GDDR7) — Best Price/Performance Blackwell

The RTX 5070 Ti ($749 – $799) is the sleeper pick of the RTX 50 stack for local AI. Same 16GB of GDDR7 as the RTX 5080, same 5th-gen tensor cores, same FP4 support, same Blackwell feature set — for $250 less.

Best pick: Gemma 4 27B at Q4. With 300W TDP and 896 GB/s bandwidth, the 5070 Ti hits a typical 62 tok/s on Gemma 4 27B Q4 (LM Studio Community data) — genuinely competitive with cards $500 more expensive. The pairing works because Gemma 4 is the most VRAM-efficient 27B-class model — at Q4 it lands at ~14.8GB, giving just enough context headroom on 16GB.

Runner-ups: Qwen 3 27B Q4 (~15GB, tighter), Mistral Small 4 7B FP16 (14GB, highest single-model quality). For multimodal on a 5070 Ti, step down to Gemma 4 14B Q5 (~9GB) and you'll have the full vision stack with comfortable context.

For the deep-dive on this card specifically, see our dedicated RTX 5070 Ti for local AI deep-dive and the sibling comparison in RTX 5060 Ti vs RTX 5070 Ti. For broader comparisons across Blackwell and Ada, our best GPU for AI roundup ranks all of them.

RTX 5060 Ti 16GB — Entry Blackwell for Real Local AI

The RTX 5060 Ti 16GB ($429 – $479) rewrote the "entry tier for local AI" definition. Before April 2026, the entry tier was 8GB of VRAM and limited to 7B models. The 5060 Ti 16GB put 16GB of GDDR7 and Blackwell 5th-gen tensor cores under $500.

Best pick: Gemma 4 14B at Q5. At 11GB, Gemma 4 14B Q5 fits comfortably with 5GB of headroom — enough for 32K context sessions. Expect 40–50 tok/s in typical use, which is well above interactive. This is the single best model for someone running local AI on a budget in 2026.

Runner-ups:

• Qwen 3 14B at Q4 (~8.5GB) — slightly weaker than Gemma 4 14B but stronger on multilingual.
• Phi-4 14B at Q5 (~10GB) — Microsoft's reasoning specialist, the best small-model pick for math/code.
• Multimodal: Llava 1.6 13B Q4 (~8GB) or Qwen3-VL 8B Q4 (~6.5GB plus 1.4GB vision tower) both fit with context headroom. This is the cheapest RTX 50 card that does multimodal properly.
• Stable Diffusion XL: native FP16 fits in 12GB — the 5060 Ti 16GB hits ~6.2 it/s per TechPowerUp.

The RTX 4060 Ti 16GB ($399 – $449) is the Ada Lovelace alternative. It has the same VRAM ceiling but 55% less bandwidth (288 GB/s vs 448 GB/s) and no FP4 support. For the head-to-head, see RTX 5060 Ti vs RTX 4060 Ti. For the ultra-budget alternative from Intel, see RTX 4060 Ti vs Intel Arc B580 — the Intel Arc B580 ($249 – $289) is the only sub-$300 card worth running local AI on. For the full $500-and-under picture, see our AI on a budget hub.

RTX 5060 (8GB) — Budget Tier, Smaller-Model Workloads

The RTX 5060 ($299 – $349) is where local AI gets honest about its limits. 8GB of GDDR7 and 448 GB/s bandwidth is workable for smaller-model chat, but the ceiling drops hard.

Best pick: Gemma 4 7B at Q4 (~4.5GB, ~50 tok/s) or Llama 4 Scout 3B-distill Q4 (~2.5GB) for faster inference on simpler tasks. Both leave comfortable context room on 8GB.

Runner-ups: Phi-4-mini (2.7B, ~2GB at Q4) for reasoning. Qwen 3 7B Q4 (~4.5GB) as a Gemma 4 alternative. For model-level detail, see the Qwen 3 hardware guide.

Warning: multimodal models eat the VRAM budget fast. A vision-enabled 7B model plus the 1.4GB vision encoder lands at ~6GB — leaving 2GB for KV cache and OS. Treat the RTX 5060 as a text-only tier. For the dedicated RTX 5060 analysis, see our RTX 5060 local AI review.

RTX 5050 / 5050 Laptop (8GB) — Notes on the Floor

The RTX 5050 ($249 – $329) and its laptop-SKU sibling are the floor of "usable for local AI." 8GB of GDDR7 at 330 GB/s bandwidth means you can run a 4B–7B model for chat, but expect slower tok/s than the 5060 at the same model size.

Best pick: Gemma 4 4B at Q4 (~2.5GB, ~40 tok/s) or Phi-4-mini Q4 (~2GB) for reasoning tasks. Both models were specifically designed for the 4GB–8GB tier and deliver real quality at that size.

Honest upgrade advice: if local AI is your primary reason for buying a GPU, step up to the RTX 5060 Ti 16GB. The $100–$150 premium doubles your VRAM and bandwidth, unlocks the entire 14B dense tier, and makes multimodal viable. The RTX 5050 makes sense only if gaming is the primary workload and AI is secondary.

"I Own X — What Should I Run?" Quick-Pick Matrix

This is the one-table version of the whole guide. One row per RTX 50 card; one recommendation per use case:

One escape hatch worth flagging: if your workload regularly needs >32GB of effective VRAM for local AI — whether that's 70B dense at Q8, 100B+ MoE at Q8, or multiple models loaded simultaneously — the Mac Studio M4 Max 128GB ($1,999 – $4,499) is a better value than any consumer RTX 50 card. See RTX 5090 vs Mac Studio M4 Max for the head-to-head.

When to Upgrade Beyond Consumer RTX 50

There are four specific triggers that mean consumer RTX 50 isn't the right tier anymore:

1. You need single 70B dense at Q8 (not Q4). No consumer RTX 50 card fits a 70B dense model at Q8 — that's 42GB+ of memory. The right tier is the RTX Pro 5000 72GB workstation card. See RTX Pro 5000 72GB vs RTX 5090 for the decision framework.
2. You need 100B+ MoE at Q8 for production. Qwen 3 122B-A10B, Llama 4 Maverick 70B at Q8 — these need 70GB+ of VRAM. A Mac Studio M4 Max 128GB or dual RTX 5090 (with multi-GPU setup) is the cost-effective path.
3. You're replacing a production API with local inference. At that scale, A100 80GB ($12,000 – $15,000) or H100 PCIe ($25,000 – $33,000) is the right tier. Consumer cards don't offer the ECC, reliability, and multi-instance GPU features enterprise serving needs.
4. Your workload is MoE-dominant with >64GB active memory. The emerging alternative here is Framework Desktop with Strix Halo ($1,599 – $1,999, 128GB unified memory). Our Strix Halo analysis has the details. For Apple alternatives at a lower price point, see DGX Spark vs Mac Studio M4 Max.

A note on Mistral Large 3 (released April 9, 2026): it's technically open-weight (675B MoE, 41B active), but it needs a node of H200s or A100s to run. It is not runnable on any consumer RTX 50 card at any quantization. If you see it in a "local LLM" roundup, the roundup is wrong.

Bottom Line — What to Run Right Now

If we had to reduce this entire guide to five sentences:

• If you have an RTX 5090: run Llama 4 Scout 109B-A8B at Q4. It's the model this card was built for.
• If you have an RTX 5080 or 5070 Ti: run Qwen 3 27B or Gemma 4 27B at Q4. Skip Q5 — you'll lose context headroom.
• If you have an RTX 5060 Ti 16GB: run Gemma 4 14B at Q5. Comfortable context, interactive speeds, strong multimodal if you want it.
• If you have an RTX 5060 (8GB): run Gemma 4 7B at Q4. Treat multimodal as out of reach.
• If you have an RTX 5050 or any 6GB–8GB card: run Gemma 4 4B or Phi-4-mini, and budget for an upgrade to 16GB+ if AI is the primary use case.

For the buying-side version of this analysis (which card to buy for which workload, not which model to run on a card you already own), see our best GPU for AI guide and the local LLM hub. For software setup once you've picked your model, our running LLMs locally walkthrough and Ollama setup guide cover the full stack.

One final tip: model weights load from storage at the start of every session. A PCIe 4.0 NVMe like the Samsung 990 Pro 4TB ($289 – $339) loads a 15GB Q4 model in ~2 seconds vs 10+ seconds on a SATA SSD. On a GPU this fast, the storage bottleneck becomes surprisingly visible — budget for the NVMe if you swap models regularly.