Qwen3.8 27B
Local OKOKB · 24
Best for: Coding, Agent, Vision, Long context·Pop: 95/100
Perf: est. 45 tok/s, typical error 5% · first token ~0.7s
Fits in 24 GB VRAM with room to spare. Best for coding, agent, vision, long context on RTX 3090.
Best Local AI Models for RTX 3090 (24GB)
The RTX 3090 is the community favorite for local AI. Bought used, its 24GB of VRAM runs 32B parameter models that most cards cannot touch. Expect est. 117 tok/s, typical error 5% on 8B models and est. 35 tok/s on dense 32B, flagship-class capability at a fraction of current-gen prices.
Quick answer
The best local LLM for the RTX 3090 is Qwen3.8 27B (est. 45 tok/s, typical error 5%) on its 24GB VRAM. It uses ~16.5GB of VRAM; the RTX 3090 handles dense models up to ~32B at Q4. A 32B model at Q4 runs at est. 35 tok/s, typical error 5%. (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 24GB for weights, context, and KV-cache. The per-size table below uses that same budget. Other strong fits: Gemma 4 26B-A4B (26B, ~16GB) and Gemma 4 12B (Q8) (12B, ~12.8GB). On this card Qwen3.8 27B runs at est. 45 tok/s, typical error 5%. What it will not run: a 35B MoE (3B active) model does not fit in VRAM alone, so it spills to system RAM over PCIe and slows sharply.
$ollama run qwen3.8:27b
- Top pick
- Qwen3.8 27B
- Decode speed
- est. 45 tok/s, typical error 5%
- VRAM needed
- ~16.5 GB
Speeds are ModelFit estimates from memory bandwidth and model size, not measured benchmarks. Typical error: the median gap between the ModelFit speed model and measured runs on hardware the model was not fitted on, for this backend and model size.
Cite this page: ModelFit, RTX 3090 24GB for Local LLMs: Runs 32B Q4, Best Used 24GB Card, https://modelfit.io/gpu/rtx-3090/, updated October 2026, CC BY 4.0.
Specs
VRAM
24 GB GDDR6X
Speed (8B Q4)
est. 117 tok/stypical error 5%
Bandwidth
936 GB/s
Architecture
Ampere
Max model size
Up to ~32B dense
Compatibility
10 excellent, 0 workable
Vendor
Table 1
RTX 3090 estimated tokens/sec by model size
Q4_K_M · ModelFit estimate| Model size | Est. speed | Fit on 24GB |
|---|---|---|
| 7B | est. 132 tok/stypical error 5% | Fits in VRAM |
| 14B | est. 74 tok/stypical error 15% | Fits in VRAM |
| 20B MoE (3.6B active) | est. 151 tok/stypical error 15% | Fits in VRAM |
| 32B | est. 35 tok/stypical error 5% | Fits in VRAM |
| 35B MoE (3B active) | est. 130 tok/srough estimate, offload not measured | CPU offload (slow) |
| 70B | est. 2 tok/srough estimate, offload not measured | CPU offload (slow) |
| 120B MoE (5.1B active) | est. 40 tok/srough estimate, offload not measured | CPU offload (slow) |
Fig. 1RTX 3090: estimated speed by model size
Decode speed, generic Q4_K_M model of each size at 4K context, 936 GB/s, typical error 5-15%
Fits in 24 GBCPU offload (slow)
7B132
14B74
20B MoE (3.6B active)151
32B35
35B MoE (3B active)130
70B2
120B MoE (5.1B active)40
tok/s est.ModelFit estimates, not measured benchmarks: decode speed of a generic Q4_K_M model of each size at 4K context on the RTX 3090's 936 GB/s bandwidth. 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 24GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident. Offload rows are rough estimates: no measured run covers offload, so they carry no typical error. What typical error means.
Fig. 2
Context costs VRAM too.
Qwen3.8 27B loads ~16.5 GB of weights; at 16k context the KV cache adds ~1.0 GB (still fits the ~22 GB usable VRAM), and at 64k it adds ~4.0 GB (still fits).
Weights 17 GBKV cache · 16k context 1.0 GBHeadroom 4.1 GBRuntime reserve 2.4 GB
The model leaves about 4.1 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.
Get told when a model beats Qwen3.8 27B on your RTX 3090
One email when a new open-weight model is a better fit for 24 GB of VRAM, plus the Thursday weekly.
You'll also get the weekly ModelFit email. By signing up you agree to our Privacy Policy. Unsubscribe from either email anytime, in one click.
Where to buy the RTX 3090
Storage & accessories for your model library
- Internal NVMe SSD · 2TBA Gen4 M.2 drive keeps your whole GGUF and quant collection on fast local storage, loading models straight off NVMe.Check price on Amazon
- USB4 NVMe Enclosure40Gbps external storage fast enough to run models from. Pair it with an M.2 drive for a portable model vault.Check price on Amazon
ModelFit may earn a commission on purchases through these links, at no extra cost to you.
RTX 3090 VRAM for AI: what actually fits?
24GB GDDR6X at 936 GB/s unlocks a tier of models that 16GB cards cannot reach. DeepSeek-R1 32B, Qwen 2.5 32B, and Command-R 35B all fit comfortably at Q4 quantization. You get about 23GB usable, so 32B Q4 models (~20GB) load fully in VRAM with 3GB left for context. The 3090 is the cheapest way to run 32B models without CPU offloading, making it the darling of the r/LocalLLaMA community.
Table 2
RTX 3090 vs similar GPUs
| Hardware | Memory | Speed | Bandwidth |
|---|---|---|---|
| RTX 4080 SUPER | 16 GB | est. 102 tok/stypical error 5% | 736 GB/s |
| RTX 5080 | 16 GB | est. 132 tok/stypical error 5% | 960 GB/s |
| RTX 3090 | 24 GB | est. 117 tok/stypical error 5% | 936 GB/s |
| RTX 4090 | 24 GB | est. 133 tok/stypical error 5% | 1008 GB/s |
Recommended models
10 modelsGemma 4 26B-A4B
Local OKOKB · 26
Best for: Chat, Coding, Multimodal·Pop: 86/100
Perf: est. 147 tok/s, typical error 15% · first token ~0.1s
Fits in 24 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 3090.
Gemma 4 12B (Q8)
Local OKExcellentA · 41
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: est. 54 tok/s, typical error 15% · first token ~2.0s
Fits in 24 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 3090.
Nemotron 3.5 Lightning 30B-A3B (IQ4_XS)
Local OKOKB · 13
Best for: Agentic, Coding, Long context·Pop: 78/100
Perf: est. 175 tok/s, typical error 15% · first token ~0.1s
Fits in 24 GB VRAM with room to spare. Best for agentic, coding, long context on RTX 3090.
Qwen3.5 35B-A3B Instruct
Local OKOKB · 7
Best for: Reasoning, Coding, Agent scenarios·Pop: 90/100
Perf: est. 212 tok/s, typical error 15% · first token ~0.1s
Fits in 24 GB VRAM with room to spare. Best for reasoning, coding, agent scenarios on RTX 3090.
LFM2 24B-A2B Instruct
Local OKExcellentA · 35
Best for: Local AI agents, privacy-first tool calling, MCP workflows·Pop: 80/100
Perf: est. 225 tok/s, typical error 15% · first token ~0.1s
Fits in 24 GB VRAM with room to spare. Best for local ai agents, privacy-first tool calling, mcp workflows on RTX 3090.
GLM-4.7-Flash 30B-A3B
Local OKOKB · 12
Best for: Coding, Agentic tasks, Reasoning·Pop: 78/100
Perf: est. 156 tok/s, typical error 15% · first token ~0.1s
Fits in 24 GB VRAM with room to spare. Best for coding, agentic tasks, reasoning on RTX 3090.
AREX-2 27B
Local OKOKB · 20
Best for: Agents, Deep research, Coding·Pop: 60/100
Perf: est. 44 tok/s, typical error 5% · first token ~0.7s
Fits in 24 GB VRAM with room to spare. Best for agents, deep research, coding on RTX 3090.
Gemma 4 31B
Local OKOKB · 7
Best for: Quality, Coding, Multimodal·Pop: 84/100
Perf: est. 37 tok/s, typical error 5% · first token ~0.8s
Fits in 24 GB VRAM with room to spare. Best for quality, coding, multimodal on RTX 3090.
Laguna XS 2.1
Local OKOKB · 6
Best for: Agentic coding, Long-horizon tasks·Pop: 72/100
Perf: est. 198 tok/s, typical error 15% · first token ~0.1s
Fits in 24 GB VRAM with room to spare. Best for agentic coding, long-horizon tasks on RTX 3090.
Video and image models on the RTX 3090
Runs · Stated: the linked source says a GPU with that much VRAM runs the model. Runs · Published: a dated run on the named card with that much VRAM. unknown: no source yet. Every VRAM tier: video · image. Ranked with reasons: video models for 24 GB of VRAM · image models for 24 GB of VRAM.
Video
| Model | Fits | Quality | App |
|---|---|---|---|
| HunyuanVideo-1.5Tencent Hunyuan Community License | Runs · Stated24 GB VRAM · ComfyUI native workflow · ComfyUI docs: HunyuanVideo 1.5 | #38 of 48 in text to video4,292 votessnapshot 2026-09-22 | ComfyUI |
| LTX-2.3LTX-2 Community License Agreement | Runs · Stated24 GB VRAM · two-stage production at 720p to 1080p · LTX blog: hardware for AI video models | No independent score yet | Lightricks/LTX-2 ltx-pipelines, ComfyUI |
| LTX-2.5LTX-2.x Community License Agreement | Runs · Stated24 GB VRAM · LTX Desktop app, 32 GB system RAM · LTX Desktop README (Lightricks) | No independent score yet | Lightricks/LTX-2 ltx-pipelines, ComfyUI |
| MiniMax H3MiniMax H3 Community License Agreement | Runs · Stated24 GB VRAM · Diffusers int8 with streamed blocks, about 75 GB system RAM · Diffusers docs: MiniMax-H3 memory | #8 of 48 in text to video11,417 votessnapshot 2026-09-22 | stable-diffusion.cpp, ComfyUI |
| Mochi 1 previewApache License 2.0 | Runs · Stated22 GB VRAM · Diffusers bf16 variant with CPU offload · Mochi 1 model cardComplete: Official Diffusers BF16 variant40.0 GB download | #48 of 48 in text to video5,893 votessnapshot 2026-09-22 | Diffusers, ComfyUI |
| Wan2.1Apache License 2.0 | Runs · Stated13 GB VRAM · T2V 14B in Diffusers, bf16 with group offloading to system RAM · Diffusers docs: Wan pipeline | No independent score yet | ComfyUI |
| Wan2.2 A14BApache License 2.0 | Runs · PublishedRTX 5060 Ti · 16 GB VRAM · ComfyUI fp16 I2V with 4-step LoRAs, 64 GB system RAM · ComfyUI issue #10662 | #42 of 48 in text to video10,458 votessnapshot 2026-09-22 | ComfyUI |
| Wan2.2 TI2V 5BApache License 2.0 | Runs · Stated24 GB VRAM · official generate.py at 1280x704 with offload flags · Wan2.2-TI2V-5B model card | No independent score yet | ComfyUI |
Quality: blind human votes from Arena (arena.ai), CC BY 4.0, snapshot 2026-09-22. Scores are for the Arena checkpoint, not your local quantized build.
Image
| Model | Fits | Quality | App |
|---|---|---|---|
| FLUX 3 Image KleinFLUX Kommunity License v1.0 | Runs · Stated8 GB VRAM · official ComfyUI int8 files · FLUX 3 Image Klein README (Black Forest Labs) | No independent score yet | Diffusers |
| FLUX.1 [dev]FLUX.1 [dev] Non-Commercial License v1.1.1 | Runs · Stated6 GB VRAM · stable-diffusion.cpp with GGUF weights · stable-diffusion.cpp FLUX docs | #78 of 82 in text to image49,221 votessnapshot 2026-10-07 | Diffusers, ComfyUI |
| FLUX.2 [dev]FLUX Non-Commercial License | Runs · Stated24 GB VRAM · 4-bit quantization in Diffusers · FLUX.2 dev Diffusers guide (Black Forest Labs) | #38 of 82 in text to image77,988 votessnapshot 2026-10-07 | Diffusers, ComfyUI, stable-diffusion.cpp |
| FLUX.2 [klein]Apache 2.0 (4B), FLUX Non-Commercial License (9B) | Runs · Stated13 GB VRAM · Klein 4B · FLUX.2 [klein] 4B model cardComplete: Klein 4B BF1616.0 GB download | No independent score yet | Diffusers, stable-diffusion.cpp |
| Krea 2 TurboKrea 2 Community License Agreement v.1 | Runs · PublishedRTX 4070 · 12 GB VRAM · ComfyUI fp8, dynamic VRAM paging, 8 steps · ComfyUI issue #15365 | No independent score yet | Diffusers, stable-diffusion.cpp |
| Qwen-Image-2.1Qwen Research License Agreement | Runs · PublishedRTX 4090 · 24 GB VRAM · Diffusers bf16 with model CPU offload, 1024x1024, 50 GB free system RAM required · MindrLabs Qwen-Image-2.1 README | #19 of 82 in text to image11,441 votessnapshot 2026-10-07 | Diffusers, stable-diffusion.cpp |
| SDXL Base 1.0CreativeML Open RAIL++-M License | Runs · Stated8 GB VRAM · SDXL 1.0, no app named · SDXL 1.0 announcement (Stability AI) | No independent score yet | ComfyUI, stable-diffusion.cpp |
| Z-Image-TurboApache 2.0 | Runs · Stated16 GB VRAM · stated for consumer GPUs · Z-Image-Turbo model card | #59 of 82 in text to image24,808 votessnapshot 2026-10-07 | Diffusers, stable-diffusion.cpp |
| MOSS-ImageApache 2.0 | unknownComplete: UD-Q4_K_XL GGUF13.2 GB download | No independent score yet | stable-diffusion.cpp |
| Stable Diffusion 3.5 LargeStability AI Community License | unknownComplete: Q4_0 GGUF16.4 GB download | #81 of 82 in text to image23,396 votessnapshot 2026-10-07 | ComfyUI |
Quality: blind human votes from Arena (arena.ai), CC BY 4.0, snapshot 2026-10-07. Scores are for the Arena checkpoint, not your local quantized build.
Can I run it on the RTX 3090?
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_Mfits
- Qwen3 14B Q4_K_Mfits
- GPT-OSS 20B MXFP4fits
- Qwen3.8 27B Q4_K_Mfits
- Llama 3.3 70B Instruct Q4_K_Mno
- Qwen3.5 35B-A3B Instruct Q4_K_Mfits
- Gemma 4 26B-A4B Q4_K_Mfits
- Qwen3.5 4B Instruct Q4_K_Mfits
- Gemma 4 E4B Q4_K_Mfits
- Gemma 3 4B Instruct Q4_K_Mfits
- Llama 3.1 8B Instruct Q4_K_Mfits
- Qwen3.5 9B Instruct Q4_K_Mfits
- Gemma 4 12B Q4_K_Mfits
- Mistral Nemo 12B Q4_K_Mfits
- Qwen3.5 9B Instruct (Q8) Q8_0fits
- Gemma 4 12B (Q8) Q8_0fits
- LFM2 24B-A2B Instruct Q4_K_Mfits
- Qwen3.5 27B Instruct Q4_K_Mfits
- Qwen3.6 27B Q4_K_Mfits
- Gemma 4 31B Q4_K_Mfits
- Qwen3.6 35B-A3B Q4_K_Mtight
- Qwen3.8 27B (Q8) Q8_0tight
Similar GPUs for local AI
Speeds are estimates for Qwen3 8B Q4_K_M, typical error 5%.
Compatible model families
- QwenAlibaba 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 3090 FAQ: common questions
How much VRAM does the RTX 3090 have for LLMs?
The RTX 3090 has 24GB GDDR6X VRAM with 936 GB/s bandwidth. About 23GB is usable for models. This is the cheapest GPU that can run 32B parameter models entirely in VRAM at Q4 quantization.
What size LLM can I run on an RTX 3090?
Up to 32B parameter models at Q4 quantization. Top picks: DeepSeek-R1 32B, Qwen 2.5 32B, and Command-R 35B. For 70B models, you would need Q2 quantization or dual GPUs.
Is a used RTX 3090 still worth it for local AI in 2026?
Yes. The RTX 3090 is the best value GPU for large model inference in 2026. Bought used, its 24GB VRAM handles 32B models that 16GB cards, even new flagship ones, cannot. The r/LocalLLaMA community consistently ranks it as the top recommendation.
RTX 3090 vs RTX 4090 for AI: which should I buy?
The RTX 4090 is 14% faster (est. 133 tok/s, typical error 5% vs est. 117 tok/s on 8B) with the same 24GB VRAM. But it costs several times more once you compare a new 4090 against a used 3090. The 3090 offers much better value for AI workloads.
Where can I buy a used RTX 3090 for AI?
Check eBay, r/hardwareswap, and local marketplaces. Look for cards that were not used for cryptocurrency mining. The Founders Edition and EVGA models have good cooling for sustained AI workloads.
How fast is a 27B-class model like Qwen3.8 27B on the RTX 3090?
ModelFit estimates a 32B model on the RTX 3090 at Q4_K_M runs at est. 35 tok/s, typical error 5%. The current 27B-class pick in the catalog is Qwen3.8 27B (ollama run qwen3.8:27b).
Related guides & benchmarks
More local AI guides
- How Much RAM (or VRAM) Do You Need for a Local LLM?The model-size-to-memory matrix: what each VRAM and RAM tier actually runs.
- Best LLM for MacBook (Apple Silicon)Unified-memory Macs run bigger models per dollar. Picks by RAM tier, M1 to M5.
- Local AI image generator on MacCompare image models by RAM and app.
- Local image generation on MacImage models and apps by Mac RAM.
Browse all NVIDIA GPUs for AI
Real-world calibration
Have this hardware? Share a measured run.
Time a model that fits your machine with Ollama, using the same prompts as every other run. The CLI shows your result and asks before it sends anything; your machine then joins the speed test leaderboard. It does not change the quality benchmark.
$ npx @modelfit/cli@latest bench
Want personalized recommendations?
Use our interactive wizard to compare models across Apple Silicon and NVIDIA GPUs.