01
Qwen3.5 9B Instruct (Q8)
Qwen / 9B / Q8_0 / ~10.7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~53 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 5080.
Best Local AI Models for RTX 5080 (16GB)
The RTX 5080 is the fastest 16GB card for local AI. At 94 tokens per second for 8B models, it outperforms even the RTX 3090 in raw speed while costing less. The best choice for users who want top speed with 14B models.
Quick answer
The best local LLM for the RTX 5080 is Qwen3.5 9B Instruct (Q8) at ~53 tok/s on its 16GB VRAM. It uses ~10.7GB of VRAM; the RTX 5080 handles up to 14B parameter models at Q4. A 14B model runs at ~58 tok/s.
$ollama run qwen3.5:9b-q8_0
TOP PICK
Qwen3.5 9B Instruct (Q8)
EST. SPEED
~53 tok/s
VRAM NEEDED
~10.7 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
RTX 5080 Estimated Tokens/sec by Model Size
Q4_K_M · ModelFit estimate| Model Size | Est. Speed | Fit on 16GB |
|---|---|---|
| 7B | ~105 tok/s | Fits in VRAM |
| 14B | ~58 tok/s | Fits in VRAM |
| 20B MoE (3.6B active) | ~89 tok/s | Fits in VRAM |
| 32B | ~6 tok/s | CPU offload (slow) |
| 35B MoE (3B active) | ~57 tok/s | CPU offload (slow) |
| 70B | ~1 tok/s | CPU offload (slow) |
| 120B MoE (5.1B active) | ~13 tok/s | CPU offload (slow) |
ModelFit estimates, not measured benchmarks: anchored to an 8B-class Q4_K_M model at 16K context on the RTX 5080's 960 GB/s bandwidth, then scaled by model size. MoE rows scale by active parameters (decode reads only the active experts), so a 35B MoE runs far faster than a dense 32B. "CPU offload" sizes exceed the 16GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident.
Context costs VRAM too. Qwen3.5 9B Instruct (Q8) loads ~10.7 GB of weights; at 16k context the KV cache adds ~0.5 GB (still fits the ~14 GB usable VRAM), and at 64k it adds ~2.0 GB (still fits).
KV-cache figures assume an fp16 cache, the llama.cpp/Ollama default. Standard GQA models use a size-class estimate (8 KV heads x 128 head dim class); hybrid linear-attention models (Qwen3.5/3.6, Qwen3-Next) use the exact per-token cost from their published config, since only their sparse full-attention layers cache KV. A q8_0 KV cache roughly halves either figure. Estimates, not measurements.
Where to Buy the RTX 5080
≈ $999 streetStorage & accessories for your model library
Internal NVMe SSD · 2TB~$170
A Gen4 M.2 drive keeps your whole GGUF and quant collection on fast local storage, loading models straight off NVMe.
Check price on AmazonUSB4 NVMe Enclosure~$80
40Gbps external storage fast enough to run models from. Pair it with an M.2 drive for a portable model vault.
Check price on AmazonModelFit may earn a commission on purchases through these links, at no extra cost to you. Prices shown are approximate street references.
RTX 5080 VRAM for AI: What Actually Fits?
16GB GDDR7 at 960 GB/s makes the RTX 5080 the bandwidth champion of 16GB cards. It loads 14B models at Q4 with ~5GB headroom and runs them at an estimated 58 tok/s. On 8B models it pushes ~94 tok/s; for context, the RTX 3090 achieves ~87 tok/s with 24GB VRAM. The 5080 is faster despite 8GB less memory. If your models fit in 16GB, this card maximizes speed. For models that need 20GB+, you will need to step up to the RTX 4090 or 5090.
RTX 5080 vs Similar GPUs
| Hardware | Memory | Speed | Bandwidth | Price |
|---|---|---|---|---|
| RTX 5070 Ti | 16 GB | 87 tok/s | 896 GB/s | $749 |
| RTX 5080 | 16 GB | 94 tok/s | 960 GB/s | $999 |
| RTX 4080 SUPER | 16 GB | 79 tok/s | 736 GB/s | $1,597 |
| RTX 5090 | 32 GB | 145 tok/s | 1792 GB/s | $2,499 |
Recommended Models
registry-verified10 models02
Gemma 4 12B
Gemma / 12B / Q4_K_M / ~8 GB
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: ~67 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 5080.
03
Qwen3 14B
Qwen / 14B / Q4_K_M / ~11 GB
Best for: Coding, Quality·Pop: 84/100
Perf: ~58 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for coding, quality on RTX 5080.
04
Gemma 3 12B Instruct
Gemma / 12B / Q4_K_M / ~9.5 GB
Best for: Chat, Quality·Pop: 76/100
Perf: ~67 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, quality on RTX 5080.
05
Mistral Nemo 12B
Mistral / 12B / Q4_K_M / ~9.5 GB
Best for: Chat, Translation·Pop: 78/100
Perf: ~67 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, translation on RTX 5080.
06
Llama 3.1 8B Instruct (Q5)
Llama / 8B / Q5_K_M / ~8 GB
Best for: Chat, Coding·Pop: 68/100
Perf: ~81 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for chat, coding on RTX 5080.
07
GPT-OSS 20B
GPT-OSS / 21B / MXFP4 / ~13.8 GB
Best for: Chat, Coding, Reasoning·Pop: 85/100
Perf: ~79 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for chat, coding, reasoning on RTX 5080.
08
LFM2 24B-A2B Instruct
LFM2 / 24B / Q4_K_M / ~14 GB
Best for: Local AI agents, privacy-first tool calling, MCP workflows·Pop: 80/100
Perf: ~106 tok/s · first token ~0.3s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for local ai agents, privacy-first tool calling, mcp workflows on RTX 5080.
09
Qwen3.5 9B Instruct
Qwen / 9B / Q4_K_M / ~7 GB
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: ~85 tok/s · first token ~0.4s
Local OKExcellent
Fits in 16 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 5080.
10
Qwen2.5 Coder 14B
Qwen / 14B / Q4_K_M / ~11 GB
Best for: Coding·Pop: 68/100
Perf: ~58 tok/s · first token ~0.4s
Local OKOK
Fits in 16 GB VRAM with room to spare. Best for coding on RTX 5080.
Models Too Big for 16GB? Rent a Cloud GPU
by the hourThe RTX 5080 tops out around up to 14b parameter models. For anything bigger, an hourly rented GPU runs the same open weights with the same Ollama workflow, billed by the hour, no hardware purchase needed.
RunPodHourly GPU pods (RTX 4090 to H100) with one-click Ollama/vLLM templates.Rent
Vast.aiMarketplace of rented GPUs, usually the cheapest per-hour prices.Rent
ModelFit may earn a commission on sign-ups made through these links, at no extra cost to you.
Similar GPUs for Local AI
Compatible Model Families
Qwen
Alibaba Cloud: Widest size range (0.5B to 235B)
LlamaMeta: Most popular open-weight model family
DeepSeekDeepSeek AI: Best-in-class reasoning with R1 models
MistralMistral AI: Excellent performance-per-parameter ratio
GemmaGoogle DeepMind: Excellent quality at small sizes (1B-9B)
PhiMicrosoft: Best quality-per-gigabyte at small sizes
RTX 5080 FAQ: Common Questions
How much VRAM does the RTX 5080 have for LLMs?
The RTX 5080 has 16GB GDDR7 VRAM with 960 GB/s bandwidth, the highest of any 16GB consumer card. About 15.5GB is usable for models. Perfect for 14B models at Q4 with room for generous context windows.
What size LLM can I run on an RTX 5080?
Up to 14B parameter models at Q4 quantization. The 5080 runs them at an estimated 58 tok/s (8B models hit ~94), faster than any other 16GB card. For 32B models, you need 24GB+ VRAM: consider the RTX 4090 or 5090 instead.
Is the RTX 5080 good for local AI in 2026?
The RTX 5080 is the best 16GB card for AI speed in 2026. At ~94 tok/s on 8B models, it beats the RTX 3090 while costing less. The only downside is that 16GB limits you to 14B models; the 5090 (32GB) unlocks 70B models.
RTX 5080 vs RTX 5090 for running AI models?
The RTX 5090 (32GB) has double the VRAM and 54% more 8B speed (an estimated 145 vs 94 tok/s), but costs 2.5x more ($2,499 vs $999). Get the 5080 if 14B models are enough. Get the 5090 only if you need 32B-70B models.
RTX 5080 vs RTX 5070 Ti: which is better for AI?
Both have 16GB VRAM. The 5080 is 8% faster (an estimated 94 vs 87 tok/s on 8B) but costs $250 more. For most AI workloads, the 5070 Ti is the better value. The 5080 is for users who want maximum speed from 16GB.
How fast is a 27B-class model on the RTX 5080?
The RTX 5080's 16GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 20B MoE (3.6B active), at an estimated 89 tok/s.
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