Best AI Models for the RTX 5080: What 16GB Runs Well
The whole 14B class with context to spare, the 24B coders as a tight fit, and the 27B models that miss by a gigabyte. Every footprint computed, no invented speeds.
The best AI model for the RTX 5080 is Qwen 3 14B, and 16GB lets you run it a quant level higher than a 12GB card can: Q6_K is about 11.5GB (14 billion params × ~0.82GB per billion), which still leaves ~4GB for context. The other three picks worth the card are Devstral Small 2 24B (~15GB at Q4_K_M, the strongest coder that fits, with almost no room left), the MoE gpt-oss:20b (~12 to 16GB resident, fast because only ~3.6B params fire per token), and Flux.1 dev FP8 (~11GB, ~13GB with LoRAs). The honest ceiling: Qwen3.6 27B needs ~17GB at Q4 and misses this card by about a gigabyte, which is the entire argument for 24GB. We do not own a 5080, so every speed on this page is a bandwidth ceiling with the arithmetic shown, never a rate we recorded.
Models that run well on the RTX 5080
| Model | Size | Build | Memory used | Speed |
|---|---|---|---|---|
| WINNERQwen 3 14B The 16GB winner and the reason to run this card instead of a 12GB one: the same model at Q6_K instead of Q4, with ~4GB still free for KV cache. Q8_0 computes to ~15GB (14 × ~1.06) and leaves nothing for context, so stop at Q6_K. ollama pull qwen3:14b (pick the q6_K tag on ollama.com/library/qwen3 for the higher quant) | 14B (dense) | Q6_K (or Q4_K_M) | ~11.5GB (14 × ~0.82) / ~9GB at Q4_K_M | roofline 960 ÷ 11.5 ≈ 83 tok/s at Q6_K, ÷ 9 ≈ 107 at Q4_K_M. Ceilings, not measurements. |
| Devstral Small 2 24B The strongest agentic coder that fits 16GB (Mistral reports 68.0% SWE-bench Verified). It is a tight fit: ~15GB of weights leaves roughly a gigabyte for KV cache, so set num_ctx deliberately and give the card nothing else to hold. ollama pull devstral-small-2:24b | 24B (dense) | Q4_K_M | ~15GB (24 × ~0.60) | roofline 960 ÷ 15 ≈ 64 tok/s. Ceiling only. |
| gpt-oss:20b OpenAI’s open-weight model, sized for exactly this tier. Fast general-purpose chat and tool use; the trade is that it fills most of the card, so it runs alone. ollama pull gpt-oss:20b | 20.9B (MoE, ~3.6B active) | MXFP4 | ~11GB of weights (20.9 × ~0.53); ~12 to 16GB resident with context | MoE: only ~3.6B params are read per token, so the bandwidth ceiling is in the hundreds and stops being the limit. We have no measured figure for this card. |
| Qwen 2.5 Coder 14B The context-heavy coding pick. Devstral beats it on agentic scores, but ~9GB of weights leaves ~7GB of KV budget, which is the difference between single-file edits and feeding an agent whole modules. ollama pull qwen2.5-coder:14b | 14B | Q4_K_M | ~9GB (14 × ~0.60) | roofline 960 ÷ 9 ≈ 107 tok/s. Ceiling only. |
| DeepSeek-R1 14B Visible chain-of-thought for hard problems. Reasoning chains eat context fast, and this is the tier where a 14B finally has the KV headroom for them. ollama pull deepseek-r1:14b | 14B | Q4_K_M | ~9GB (14 × ~0.60) | roofline 960 ÷ 9 ≈ 107 tok/s. Ceiling only. |
| Flux.1 dev FP8 Near-FP16 image quality with room for two or three LoRAs, ControlNet or 1536px renders. The FP16 reference build (~24GB) stays a 24GB-card job. ComfyUI: flux1-dev-fp8.safetensors | 12B | FP8 | ~11GB (~13GB with a LoRA stack) | diffusion is compute-bound, so the roofline method above does not apply, and we have no 5080 render timing to quote |
| Qwen 2.5 32B (Q3_K_S) The squeeze, included so you can make the call yourself: a 32B does load at 3-bit with a short context, but a 14B at Q6_K usually answers better and leaves room to work. Test both on your own prompts before committing. ollama pull qwen2.5:32b-instruct-q3_K_S | 32B | Q3_K_S | ~14 to 15GB (32 × ~0.43) | roofline 960 ÷ 15 ≈ 64 tok/s. Ceiling only. |
What won't fit on the RTX 5080
- ✗Qwen3.6 27B (needs ~17GB) — 27 × ~0.60 = ~16GB of weights at Q4_K_M before a single token of context, ~17GB as Ollama ships it. The best open model in the 27B class misses this card by about a gigabyte. This is the near-miss that sells 24GB cards.
- ✗Qwen3-Coder 30B (needs ~19GB) — 30 × ~0.60 = ~18 to 19GB at Q4. An MoE, so it would be fast here, but the weights have to fit first and they do not.
- ✗Qwen 2.5 Coder 32B (needs ~20GB) — The strongest dense open coder, and a 24GB pull. On 16GB, Devstral Small 2 is the ceiling.
- ✗Llama 3.3 70B (needs ~42GB at Q4, ~26GB even at Q2_K) — 70 × ~0.37 = ~26GB at the most aggressive usable quant, still 10GB past this card. A 70B is a 32GB card at Q2 or a 48GB pair at Q4.
RTX 5080-specific notes
- →Where the numbers come from: we do not own an RTX 5080 and have published no first-party speed figure for it. Every tok/s on this page is a roofline, NVIDIA’s listed 960 GB/s of memory bandwidth divided by the gigabytes a dense model reads per token. No inference engine reaches that ceiling, because attention, sampling and kernel launches all cost time the roofline ignores. Read each figure as the speed a model cannot exceed on this card. Footprints are parameter count × bytes per weight, using llama.cpp’s published bits per weight for each k-quant (Q3_K_S ~0.43GB per billion, Q4_K_M ~0.60, Q6_K ~0.82, Q8_0 ~1.06). Weights only; the KV cache is a separate bill.
- →What 16GB buys over 12GB is a quant level and a context window, not a new model class. The 14B class fits both cards at Q4 (~9GB). On the 5080 the same 14B runs at Q6_K (~11.5GB) with ~4GB spare, or stays at Q4 with ~7GB spare. For Qwen 3 14B the KV arithmetic is tidy: 40 layers × 8 KV heads × 128 head dim × 2 (K and V) × 2 bytes = 160KB per token at FP16, so ~7GB of headroom is roughly 40K tokens of context and ~4GB is roughly 25K. A 12GB card running the same model at Q4 has ~3GB spare, under 20K tokens. That is the upgrade, in numbers.
- →The 24B coders are the tier’s tight fit. Devstral Small 2 at Q4_K_M is ~15GB, so it loads with about a gigabyte for KV cache, and an agent loop that needs a 32K context will spill into system RAM and slow to a crawl. If you run agents on whole repositories, Qwen 2.5 Coder 14B at ~9GB with ~7GB of context budget is the better daily driver even though it scores lower. After loading anything on this list, run `ollama ps` and confirm it says 100% GPU; any CPU share means it spilled.
- →Same 16GB as the RTX 5070 Ti, same model list. NVIDIA’s comparison table lists the 5080 at 10,752 CUDA cores and 360 W against the 5070 Ti’s 8,960 cores and 300 W, both with 16 GB GDDR7. Nothing that fits one card fails on the other. The 5080 is the faster card at the same capacity; whether that speed is worth the price gap between them is the only question, because the model list is identical.
- →The laptop variant is the exception in the 50-series lineup: NVIDIA’s laptop comparison lists the RTX 5080 Laptop GPU at 16 GB GDDR7 on a 256-bit bus, the same memory and bus width as the desktop card, with 7,680 CUDA cores and an 80 to 150 W graphics power range. So, unlike the 5090 Laptop (24GB versus 32GB), every row on this page fits a 5080 laptop. Expect slower generation, and expect two laptops with a “5080” inside to differ a lot, because the power limit is chosen by the laptop maker, not by NVIDIA.
- →The Blackwell software trap applies to this card exactly as it does to the 5090: every RTX 50-series GPU reports compute capability 12.0 (sm_120), and PyTorch’s CUDA 12.6 wheels contain no kernels for it. Any project that pins an older torch will fail with “no kernel image is available for execution on this device”. Ollama bundles its own CUDA runtime and is largely unaffected; ComfyUI and anything with compiled extensions (xformers, flash-attn, bitsandbytes) are where it bites. Our fix guide for that error is linked below.
- →Power, from NVIDIA’s spec page: 360 W total graphics power, an 850 W required system power supply, and either 3× PCIe 8-pin through the adapter in the box or a single 450 W or greater PCIe Gen 5 cable. That is a far easier install than the 5090’s 575 W and 1000 W supply. The connector lesson still stands: prefer a native cable to an adapter chain, and seat it fully. LLM decoding is a sustained load measured in minutes, not the bursty load a gaming PSU was sized around.
- →Price sanity: NVIDIA announced the RTX 5080 starting at $999, and our GPU-prices guide tracks how far above MSRP the 50-series has been selling. The question to ask at any price is whether you are memory-bound. If your daily models are 14B or smaller, gpt-oss:20b, or Flux FP8, this card runs all of them well. If you keep wanting Qwen3.6 27B or the 32B coders, no 16GB card at any price gets you there, and a used 24GB card runs that whole class at Q4.
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Frequently asked questions
What is the best AI model for an RTX 5080?
Qwen 3 14B, run at Q6_K (~11.5GB) rather than the Q4 build a 12GB card is limited to, with ~4GB left for context. For agentic coding, Devstral Small 2 24B (~15GB) is the strongest model that fits, but it fits with almost no room, so Qwen 2.5 Coder 14B (~9GB) is the better pick for long repo contexts. For a fast general assistant, gpt-oss:20b. For images, Flux.1 dev FP8 with LoRAs. All four footprints are arithmetic, and the speeds on this page are ceilings, not measurements.
How many tokens per second does an RTX 5080 get?
We have not measured one, so we will not quote a number as if we had. What we can give you is the ceiling, and you can compute it for any dense model yourself: token generation reads the whole weight set once per token, so the upper bound is memory bandwidth divided by footprint. NVIDIA lists the 5080 at 960 GB/s, so a 9GB model cannot exceed 960 ÷ 9 ≈ 107 tok/s and a 15GB model cannot exceed ≈ 64. Real engines land well below that. A page quoting an exact 5080 figure without naming the rig, engine, quant and context length is guessing.
Can an RTX 5080 run a 32B model?
Only at 3-bit. Qwen 2.5 32B at Q3_K_S computes to ~14 to 15GB (32 × ~0.43), which loads with a short context. At Q4_K_M the same model is ~20GB and does not fit. In practice a 14B at Q6_K (~11.5GB) usually answers better than a 32B squeezed to Q3, and it leaves room to work, so test both on your own prompts before making the 32B your default. Full-quality 32B is what 24GB cards are for.
RTX 5080 or RTX 5090 for local AI?
The difference is capacity, not speed class. The 5090’s 32GB holds Qwen3.6 27B at Q8, a dense 32B at Q5, and a 70B at Q2; the 5080’s 16GB holds the 14B class at Q6 and the 24B coders at Q4. If the models you want are on this page, the 5080 runs them well and the 5090 buys speed you may not need. If you keep looking at the 27B and 32B rows, the 5090 is the card, and so is a used 24GB card at a fraction of the price. Our RTX 5090 vs 5080 comparison walks through it.
RTX 5080 or RTX 4090 for local AI?
For local AI the 4090 usually wins, and not on speed. Its 24GB runs Qwen3.6 27B, Qwen 2.5 Coder 32B and DeepSeek-R1 32B at full Q4; the 5080’s 16GB runs none of them. On bandwidth the two are close (NVIDIA lists 960 GB/s for the 5080 and 1,008 GB/s for the 4090), so the newer card is not faster on the token generation this page is about. Buy the 5080 if your models are 14B-class and you want Blackwell for other reasons; buy the 24GB card if the 27B to 32B class is the point.
Is the RTX 5080 Laptop the same as the desktop RTX 5080?
Same memory, different speed. NVIDIA lists the RTX 5080 Laptop GPU at 16 GB GDDR7 on a 256-bit bus, matching the desktop card, so every model on this page fits it. The chip has 7,680 CUDA cores against the desktop’s 10,752 and runs at 80 to 150 W against 360 W, with the exact limit set by the laptop maker. Expect the same model list and noticeably slower generation, and expect thin laptops to throttle further under a sustained decode.
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