01
Qwen3.6 35B-A3B
Qwen / 35B / Q4_K_M / ~22 GB
Best for: Reasoning, Coding, Agents·Pop: 88/100
Perf: ~118 tok/s · first token ~0.9s
Local OKOKB · 24
Fits in 32 GB VRAM with room to spare. Best for reasoning, coding, agents on RTX 5090.
Best Local AI Models for RTX 5090 (32GB)
The RTX 5090 pairs 32GB of GDDR7 with 1,792 GB/s of bandwidth, the most of any consumer card. It runs 8B models at an estimated 145 tok/s and loads 70B parameter models at Q3 quantization. No other consumer GPU holds a 70B model locally.
Quick answer
The best local LLM for the RTX 5090 is Qwen3.6 35B-A3B at ~118 tok/s on its 32GB VRAM. It uses ~22GB of VRAM; the RTX 5090 handles up to 32B parameter models at Q4. A 27B-class model (Qwen3.8 27B) at Q4 runs at ~45 tok/s. (ModelFit, 2026)
Sizing rule: a Q4 model needs about 0.6 GB of VRAM per billion parameters, and ModelFit budgets 90% of this card's 32GB for weights, context, and KV-cache. The per-size table below uses that same budget. Other strong fits: Qwen3.5 35B-A3B Instruct (35B, ~20GB) and Gemma 4 26B-A4B (26B, ~16GB). Qwen3.6 35B-A3B runs at an estimated 118 tok/s on this card. What it will not run: a 120B MoE (5.1B active) model does not fit in VRAM alone, so it spills to system RAM over PCIe and slows sharply.
$ollama run qwen3.6:35b-a3b
TOP PICK
Qwen3.6 35B-A3B
EST. SPEED
~118 tok/s
VRAM NEEDED
~22 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks.
Cite this page: ModelFit, RTX 5090 32GB Local LLM: Runs 70B, Top Pick ~118 tok/s, https://modelfit.io/gpu/rtx-5090/, updated October 2026, CC BY 4.0.
Last updated: October 14, 2026 · Editor: ModelFit Team
RTX 5090 Estimated Tokens/sec by Model Size
Q4_K_M · ModelFit estimate| Model Size | Est. Speed | Fit on 32GB |
|---|---|---|
| 7B | ~162 tok/s | Fits in VRAM |
| 14B | ~90 tok/s | Fits in VRAM |
| 20B MoE (3.6B active) | ~138 tok/s | Fits in VRAM |
| 32B | ~45 tok/s | Fits in VRAM |
| 35B MoE (3B active) | ~117 tok/s | Fits in VRAM |
| 70B | ~23 tok/s | Fits in VRAM |
| 120B MoE (5.1B active) | ~19 tok/s | CPU offload (slow) |
Estimated Speed by Model Size
Fits in memoryCPU offload
ModelFit estimates, not measured benchmarks: anchored to an 8B-class Q4_K_M model at 16K context on the RTX 5090's 1792 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 32GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident.
Context costs VRAM too. Qwen3.6 35B-A3B loads ~22 GB of weights; at 16k context the KV cache adds ~0.3 GB (still fits the ~29 GB usable VRAM), and at 64k it adds ~1.3 GB (still fits).
Weights 22 GBKV cache · 16k context 0.3 GBHeadroom 6.5 GBRuntime reserve 3.2 GB
The model leaves about 6.5 GB of the usable VRAM budget free after weights and 16k context.
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.
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RTX 5090 VRAM for AI: What Actually Fits?
32GB GDDR7 at 1,792 GB/s is the highest bandwidth of any consumer card. This is enough to load 32B models at Q6 or even Q8 quantization with room to spare, delivering higher quality than Q4 on smaller cards. The 5090 also handles multi-model setups: run a 14B chat model alongside a 7B coding model simultaneously. For the largest 70B models at Q3_K_M (~30GB), it fits with about 1GB headroom. No other consumer card runs a 70B model at this quality.
Recommended Models
registry-verified10 models02
Qwen3.5 35B-A3B Instruct
Qwen / 35B / Q4_K_M / ~20 GB
Best for: Reasoning, Coding, Agent scenarios·Pop: 90/100
Perf: ~118 tok/s · first token ~0.9s
Local OKExcellentA · 31
Fits in 32 GB VRAM with room to spare. Best for reasoning, coding, agent scenarios on RTX 5090.
03
Gemma 4 26B-A4B
Gemma / 26B / Q4_K_M / ~16 GB
Best for: Chat, Coding, Multimodal·Pop: 86/100
Perf: ~118 tok/s · first token ~0.3s
Local OKExcellentA · 44
Fits in 32 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 5090.
04
Nemotron 3.5 Lightning 30B-A3B
Nemotron / 30B / Q4_K_M / ~23.7 GB
Best for: Agentic, Coding, Long context·Pop: 78/100
Perf: ~125 tok/s · first token ~0.9s
Local OKOKB · 18
Fits in 32 GB VRAM with room to spare. Best for agentic, coding, long context on RTX 5090.
05
Qwen3.8 27B
Qwen / 27B / Q4_K_M / ~16.5 GB
Best for: Coding, Agent, Vision, Long context·Pop: 95/100
Perf: ~52 tok/s · first token ~0.4s
Local OKExcellentA · 43
Fits in 32 GB VRAM with room to spare. Best for coding, agent, vision, long context on RTX 5090.
06
Qwen3.5 27B Instruct
Qwen / 27B / Q4_K_M / ~16 GB
Best for: Chat, Coding, Complex reasoning·Pop: 82/100
Perf: ~52 tok/s · first token ~0.4s
Local OKExcellentA · 44
Fits in 32 GB VRAM with room to spare. Best for chat, coding, complex reasoning on RTX 5090.
07
Qwen3.6 27B
Qwen / 27B / Q4_K_M / ~18 GB
Best for: Coding, Quality, Long context·Pop: 92/100
Perf: ~52 tok/s · first token ~0.4s
Local OKExcellentA · 38
Fits in 32 GB VRAM with room to spare. Best for coding, quality, long context on RTX 5090.
08
GLM-4.7-Flash 30B-A3B
Zhipu / 30B / Q4_K_M / ~19 GB
Best for: Coding, Agentic tasks, Reasoning·Pop: 78/100
Perf: ~125 tok/s · first token ~0.9s
Local OKExcellentA · 34
Fits in 32 GB VRAM with room to spare. Best for coding, agentic tasks, reasoning on RTX 5090.
09
Laguna XS 2.1
Laguna / 33B / Q4_K_M / ~20.3 GB
Best for: Agentic coding, Long-horizon tasks·Pop: 72/100
Perf: ~121 tok/s · first token ~0.9s
Local OKOKB · 30
Fits in 32 GB VRAM with room to spare. Best for agentic coding, long-horizon tasks on RTX 5090.
10
Ornith 1.0 35B
Ornith / 35B / Q4_K_M / ~21.2 GB
Best for: Agentic coding·Pop: 72/100
Perf: ~41 tok/s · first token ~1.0s
Local OKOKB · 26
Fits in 32 GB VRAM with room to spare. Best for agentic coding on RTX 5090.
Can I Run It on the RTX 5090?
Per-model verdict pages for this exact card: weights, KV cache by context length, and estimated tok/s, each derived from the same engine the recommendations above use.
Qwen3 8B Q4_K_MfitsQwen3 14B Q4_K_MfitsGPT-OSS 20B MXFP4fitsQwen3.8 27B Q4_K_MfitsLlama 3.3 70B Instruct Q4_K_MtightQwen3.5 35B-A3B Instruct Q4_K_MfitsGemma 4 26B-A4B Q4_K_MfitsQwen3.5 4B Instruct Q4_K_MfitsGemma 4 E4B Q4_K_MfitsGemma 3 4B Instruct Q4_K_MfitsLlama 3.1 8B Instruct Q4_K_MfitsQwen3.5 9B Instruct Q4_K_MfitsGemma 4 12B Q4_K_MfitsMistral Nemo 12B Q4_K_MfitsQwen3.5 9B Instruct (Q8) Q8_0fitsGemma 4 12B (Q8) Q8_0fitsLFM2 24B-A2B Instruct Q4_K_MfitsQwen3.5 27B Instruct Q4_K_MfitsQwen3.6 27B Q4_K_MfitsGemma 4 31B Q4_K_MfitsQwen3.6 35B-A3B Q4_K_MfitsQwen3.8 27B (Q8) Q8_0fits
Similar GPUs for Local AI
Compatible Model Families
Qwen
Alibaba Cloud: Qwen3.8 27B local flagship (Apache 2.0, vision)
LlamaMeta: Most popular open-weight model family
DeepSeekDeepSeek AI: V4.1 Flash: multimodal 552B MoE, API only
MistralMistral AI: Excellent performance-per-parameter ratio
GemmaGoogle DeepMind: Gemma 4 E4B: 4.5B effective, runs on 8GB Macs
PhiMicrosoft: Best quality-per-gigabyte at small sizes
RTX 5090 FAQ: Common Questions
How much VRAM does the RTX 5090 have for LLMs?
The RTX 5090 has 32GB GDDR7 VRAM with 1,792 GB/s bandwidth, the most of any consumer GPU. About 31GB is usable for models. This fits 70B models at Q3 quantization and 32B models at Q8 for maximum quality.
What size LLM can I run on an RTX 5090?
Up to 70B parameter models at Q3 quantization, or 32B models at Q8 for best quality. The 5090 is the only consumer GPU that can run Llama 3.1 70B locally. For 7B-8B models it delivers an estimated 145 tok/s (14B lands around an estimated 90).
Is the RTX 5090 good for local AI in 2026?
The RTX 5090 has the most VRAM (32GB) and the highest bandwidth (1,792 GB/s) of any consumer GPU. At an estimated 145 tok/s on 8B models, responses feel instant. Its 32GB VRAM unlocks 70B models that no other consumer card can handle. The 2026 memory shortage has pushed it far above its $1,999 launch MSRP, so it is only justified if you genuinely need large models.
RTX 5090 vs RTX 4090 for running AI models?
The RTX 5090 is 39% faster on 8B models (an estimated 145 vs 104 tok/s) with 33% more VRAM (32 vs 24GB). It runs 70B models that the 4090 cannot. The 4090 is discontinued and both now trade well above MSRP, but the 5090 is the clear choice for new purchases in 2026.
Can the RTX 5090 replace cloud AI services?
For many use cases, yes. A 32B model at Q6 on the 5090 rivals GPT-4 quality for coding and reasoning tasks, running at an estimated 30-35 tok/s with low latency and complete privacy. The main gap is context length: cloud models support 128K+ tokens while local 70B models are limited to 8K-16K.
How fast is a 27B-class model like Qwen3.8 27B on the RTX 5090?
ModelFit estimates a 32B model on the RTX 5090 runs at roughly 45 tok/s at Q4_K_M. The current 27B-class pick in the catalog is Qwen3.8 27B (ollama run qwen3.8:27b).
Real-world calibration
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