Qwen3.5 9B Instruct
Local OKExcellentA · 35
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: est. 53 tok/s, typical error 5% · first token ~0.6s
Fits in 12 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 3060.
Best Local AI Models for RTX 3060 (12GB)
The RTX 3060 is the cheapest way into 12GB of VRAM for local AI, handling 7B-8B parameter models at est. 50 tok/s, typical error 5%. It is no longer the bargain it was: the 2026 memory shortage pushed it far above its launch MSRP, close to cards with more VRAM, so check the current price against a newer 16GB card before buying.
Quick answer
The best local LLM for the RTX 3060 is Qwen3.5 9B Instruct (est. 53 tok/s, typical error 5%) on its 12GB VRAM. It uses ~7GB of VRAM; the RTX 3060 handles up to 9B parameter models at Q4. A 14B model at Q4 runs at est. 30 tok/s, typical error 15%. (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 12GB for weights, context, and KV-cache. The per-size table below uses that same budget. Other strong fits: Ornith 1.0 9B (9B, ~5.6GB) and Gemma 4 12B (12B, ~8GB). On this card Qwen3.5 9B Instruct runs at est. 53 tok/s, typical error 5%. What it will not run: a 20B MoE (3.6B active) model does not fit in VRAM alone, so it spills to system RAM over PCIe and slows sharply.
$ollama run qwen3.5:9b
- Top pick
- Qwen3.5 9B Instruct
- Decode speed
- est. 53 tok/s, typical error 5%
- VRAM needed
- ~7 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 3060 12GB Local LLM (2026): Best 7B-9B Picks, est. 50 tok/s, https://modelfit.io/gpu/rtx-3060/, updated October 2026, CC BY 4.0.
Specs
VRAM
12 GB GDDR6
Speed (8B Q4)
est. 50 tok/stypical error 5%
Bandwidth
360 GB/s
Architecture
Ampere
Max model size
Up to 9B parameter models
Compatibility
10 excellent, 0 workable
Vendor
Table 1
RTX 3060 estimated tokens/sec by model size
Q4_K_M · ModelFit estimate| Model size | Est. speed | Fit on 12GB |
|---|---|---|
| 7B | est. 57 tok/stypical error 5% | Fits in VRAM |
| 14B | est. 30 tok/stypical error 15% | Fits in VRAM |
| 20B MoE (3.6B active) | est. 50 tok/stypical error 15% | CPU offload (slow) |
| 32B | est. 1 tok/stypical error 5% | CPU offload (slow) |
| 35B MoE (3B active) | est. 28 tok/stypical error 15% | CPU offload (slow) |
| 70B | est. 1 tok/stypical error 10% | CPU offload (slow) |
| 120B MoE (5.1B active) | est. 17 tok/stypical error 15% | CPU offload (slow) |
Fig. 1RTX 3060: estimated speed by model size
Decode speed, generic Q4_K_M model of each size at 4K context, 360 GB/s, typical error 5-15%
Fits in 12 GBCPU offload (slow)
7B57
14B30
20B MoE (3.6B active)50
32B1
35B MoE (3B active)28
70B1
120B MoE (5.1B active)17
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 3060's 360 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 12GB VRAM; dense models slow to a crawl there, MoE models degrade less because hot experts stay GPU-resident. What typical error means.
Fig. 2
Context costs VRAM too.
Qwen3.5 9B Instruct loads ~7 GB of weights; at 16k context the KV cache adds ~0.5 GB (still fits the ~11 GB usable VRAM), and at 64k it adds ~2.0 GB (still fits).
Weights 7.0 GBKV cache · 16k context 0.5 GBHeadroom 3.3 GBRuntime reserve 1.2 GB
The model leaves about 3.3 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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Where to buy the RTX 3060
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RTX 3060 VRAM for AI: what actually fits?
With 12GB GDDR6, the RTX 3060 loads any 7B-9B model in Q4 quantization with room left for a 4K-8K context window. Models like Qwen 2.5 7B and Llama 3.2 8B use about 5-6GB, leaving headroom for KV cache. Larger 14B models require Q3 quantization or partial CPU offloading, which cuts speed by 70-80%. Stick to 7B-9B Q4 for the best experience on this card.
Table 2
RTX 3060 vs similar GPUs
| Hardware | Memory | Speed | Bandwidth |
|---|---|---|---|
| RTX 3070 | 8 GB | est. 61 tok/stypical error 5% | 448 GB/s |
| RTX 3060 | 12 GB | est. 50 tok/stypical error 5% | 360 GB/s |
| RTX 4070 | 12 GB | est. 73 tok/stypical error 5% | 504 GB/s |
| RTX 4060 Ti | 16 GB | est. 43 tok/stypical error 5% | 288 GB/s |
| RTX 5060 Ti | 16 GB | est. 68 tok/stypical error 5% | 448 GB/s |
Recommended models
registry-verified10 modelsOrnith 1.0 9B
Local OKExcellentA · 48
Best for: Agentic coding on small machines·Pop: 76/100
Perf: est. 53 tok/s, typical error 5% · first token ~0.6s
Fits in 12 GB VRAM with room to spare. Best for agentic coding on small machines on RTX 3060.
Gemma 4 12B
Local OKOKB · 26
Best for: Chat, Coding, Multimodal·Pop: 80/100
Perf: est. 35 tok/s, typical error 15% · first token ~0.9s
Fits in 12 GB VRAM with room to spare. Best for chat, coding, multimodal on RTX 3060.
LFM2.5 8B-A1B
Local OKExcellentA · 49
Best for: On-device agents, tool calling, multilingual chat·Pop: 72/100
Perf: est. 139 tok/s, typical error 15% · first token ~0.2s
Fits in 12 GB VRAM with room to spare. Best for on-device agents, tool calling, multilingual chat on RTX 3060.
Gemma 4 E4B (Q8)
Local OKExcellentA · 31
Best for: On-device, Mobile, Chat·Pop: 82/100
Perf: est. 53 tok/s, typical error 5% · first token ~3.5s
Fits in 12 GB VRAM with room to spare. Best for on-device, mobile, chat on RTX 3060.
MiMo-V2.6-Distill-Qwen-9B
Local OKExcellentA · 46
Best for: Agentic coding, Small machines·Pop: 60/100
Perf: est. 51 tok/s, typical error 5% · first token ~0.6s
Fits in 12 GB VRAM with room to spare. Best for agentic coding, small machines on RTX 3060.
Ministral 3 8B
Local OKExcellentA · 44
Best for: Chat, Coding, Vision·Pop: 74/100
Perf: est. 44 tok/s, typical error 5% · first token ~0.7s
Fits in 12 GB VRAM with room to spare. Best for chat, coding, vision on RTX 3060.
Qwen3.5 4B Instruct
Local OKExcellentA · 68
Best for: Coding, Agents, Multimodal·Pop: 88/100
Perf: est. 91 tok/s, typical error 5% · first token ~0.3s
Fits in 12 GB VRAM with room to spare. Best for coding, agents, multimodal on RTX 3060.
Ministral 3 14B
Local OKOKB · 16
Best for: Chat, Reasoning, Vision·Pop: 74/100
Perf: est. 30 tok/s, typical error 15% · first token ~1.1s
Fits in 12 GB VRAM with room to spare. Best for chat, reasoning, vision on RTX 3060.
Qwen3.5 9B Instruct (Q8)
Local OKOKB · 1
Best for: Quality, Coding, Reasoning·Pop: 86/100
Perf: est. 33 tok/s, typical error 5% · first token ~3.9s
Fits in 12 GB VRAM with room to spare. Best for quality, coding, reasoning on RTX 3060.
Can I run it on the RTX 3060?
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_Mtight
- GPT-OSS 20B MXFP4tight
- Qwen3.8 27B Q4_K_Mno
- Llama 3.3 70B Instruct Q4_K_Mno
- Qwen3.5 35B-A3B Instruct Q4_K_Mno
- Gemma 4 26B-A4B Q4_K_Mtight
- 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_0tight
- LFM2 24B-A2B Instruct Q4_K_Mtight
- Qwen3.5 27B Instruct Q4_K_Mtight
- Qwen3.6 27B Q4_K_Mno
- Gemma 4 31B Q4_K_Mno
- Qwen3.6 35B-A3B Q4_K_Mno
- Qwen3.8 27B (Q8) Q8_0no
Similar GPUs for local AI
- RTX 4060 Ti16GB · est. 43 tok/s
- RTX 30708GB · est. 61 tok/s
- RTX 5060 Ti16GB · est. 68 tok/s
- RTX 407012GB · est. 73 tok/s
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 3060 FAQ: common questions
How much VRAM does the RTX 3060 have for LLMs?
The RTX 3060 has 12GB GDDR6 VRAM. After OS and driver overhead (~0.5GB), about 11.5GB is available for model loading. This comfortably fits 7B-9B parameter models at Q4 quantization with room left for the KV cache.
What size LLM can I run on an RTX 3060?
You can run up to 9B parameter models at Q4 quantization. Popular choices include Qwen 2.5 7B (~5.2GB), Llama 3.2 8B (~5.6GB), and Mistral 7B (~4.4GB). For 14B models, you would need Q3 quantization which reduces output quality.
Is the RTX 3060 good for local AI in 2026?
Yes. The RTX 3060 remains the cheapest route to 12GB of VRAM in 2026, especially used. On 8B models it delivers est. 50 tok/s, typical error 5%, fast enough for interactive chat. Its 12GB VRAM handles most popular 7B models at full quality.
RTX 3060 vs RTX 4060 Ti for running LLMs: which is better?
The RTX 4060 Ti 16GB has 4GB more VRAM, allowing 14B models. However, its bandwidth is lower (288 vs 360 GB/s), so it is actually slower for 7B-8B models. If you only run 7B models, the RTX 3060 is better value. For 14B models, the 4060 Ti wins.
How do I set up Ollama on an RTX 3060?
Install Ollama from ollama.com: it auto-detects your RTX 3060 via CUDA. Then run "ollama run qwen2.5:7b" to start chatting. No extra configuration is needed. Make sure your NVIDIA drivers are up to date (545+ recommended).
How fast is a 27B-class model on the RTX 3060?
The RTX 3060's 12GB of VRAM cannot fit a 32B model comfortably. The largest size class it fits is 14B, at est. 30 tok/s, typical error 15%. The largest individual model in the catalog that fits is Ministral 3 14B (14B).
Related guides & benchmarks
- How to Install Ollama: Complete Setup GuideStep-by-step Ollama installation for beginners on any platform.
- Local LLMs vs GPT-4 and Claude: How They CompareSee how local 7B-8B models on your GPU compare to cloud APIs.
- Qwen 3.5 Small Models: 4B Beats 20BSmall models that run great on 12GB GPUs like the RTX 3060.
Sizing local AI? Start with RAM & VRAM
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$ npx -y @wecko-ai/modelfit bench --submit --cleanup
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