RUNLOCALAIv38
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RUNLOCALAI · v38
Will it run? / NVIDIA GeForce RTX 4060 Ti 16GB / agents

What can NVIDIA GeForce RTX 4060 Ti 16GB run for agents?

Build: NVIDIA GeForce RTX 4060 Ti 16GB + — + 32 GB RAM (windows)

Memory: 16 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
74 models

Ranked by fit for agents use case + predicted speed. Click a row for VRAM breakdown.

#1Qwen 2.5 7B Instruct
7B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 10.1 GBHeadroom: 5.9 GB
ollama run qwen2.5:7b
25
tok/s
Estimated
Weights
7.44 GB
KV cache
0.47 GB
Activations
0.38 GB
Runtime
1.80 GB
Model details →
#2Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 10.4 GBHeadroom: 5.6 GB
ollama run mistral:7b
39
tok/s
Estimated
Weights
4.81 GB
KV cache
3.50 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#3Llama 3.1 Nemotron Nano 8B
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.9 GBHeadroom: 5.1 GB
39
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#4Llama 3.1 8B Instruct
8B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 11.8 GBHeadroom: 4.2 GB
ollama run llama3.1:8b
22
tok/s
Estimated
Weights
8.50 GB
KV cache
1.07 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
#5Qwen 2.5 Math 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 8.0 GBHeadroom: 8.0 GB
44
tok/s
Estimated
Weights
4.23 GB
KV cache
1.75 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#6Qwen 3 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB
44
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#7CodeQwen 1.5 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB
44
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#8Qwen 2-VL 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB
44
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#9Qwen 2.5-VL 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB
44
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#10Qwen3.5 9B Thai Law Base
8.95B
qwen
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 9.7 GBHeadroom: 6.3 GB
35
tok/s
Estimated
Weights
5.40 GB
KV cache
2.24 GB
Activations
0.27 GB
Runtime
1.80 GB
Model details →
#11Qwen 2.5 Coder 7B Instruct
7B
qwen
Commercial OK
Quant: Q6_KContext: 8,192VRAM: 11.4 GBHeadroom: 4.6 GB
ollama run qwen2.5-coder:7b
32
tok/s
Estimated
Weights
5.78 GB
KV cache
3.50 GB
Activations
0.30 GB
Runtime
1.80 GB
Model details →
#12Mistral Turkish v2 (brooqs)
7.2B
mistral
Quant: Q4_0Context: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB
ollama run brooqs/mistral-turkish-v2:latest
46
tok/s
Estimated
Weights
4.05 GB
KV cache
3.60 GB
Activations
0.21 GB
Runtime
1.80 GB
Model details →

Runs with tradeoffs
79 models

Tight VRAM, partial CPU offload, or context-limited.

Hermes 3 Llama 3.1 8B
8B
hermes
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.7 GBHeadroom: 1.3 GB
  • • Tight VRAM fit — only 1.3 GB headroom left for context growth
ollama run hermes3:8b
22
tok/s
Estimated
Weights
8.50 GB
KV cache
4.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
Qwen 2.5 14B Instruct
14B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GB
  • • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run qwen2.5:14b
22
tok/s
Estimated
Weights
8.45 GB
KV cache
1.75 GB
Activations
0.42 GB
Runtime
1.80 GB
Model details →
Dolphin 3.0 Mistral 24B
24B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 29.0 GBHeadroom: 6.2 GB
  • • Partial CPU offload: ~45% of layers run on CPU
ollama run dolphin-mistral:24b
13
tok/s
Estimated
Weights
14.49 GB
KV cache
12.00 GB
Activations
0.73 GB
Runtime
1.80 GB
Model details →
Qwen 3 30B-A3B
30B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 24.6 GBHeadroom: 10.6 GB
  • • Partial CPU offload: ~35% of layers run on CPU
ollama run qwen3:30b
10
tok/s
Estimated
Weights
18.11 GB
KV cache
3.75 GB
Activations
0.91 GB
Runtime
1.80 GB
Model details →
Qwen 2.5 Coder 32B Instruct
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 24.2 GBHeadroom: 11.0 GB
  • • Partial CPU offload: ~34% of layers run on CPU
ollama run qwen2.5-coder:32b
10
tok/s
Estimated
Weights
19.32 GB
KV cache
2.15 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
Qwen 3 32B
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 26.1 GBHeadroom: 9.1 GB
  • • Partial CPU offload: ~39% of layers run on CPU
ollama run qwen3:32b
10
tok/s
Estimated
Weights
19.32 GB
KV cache
4.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
Qwen 2.5 32B Instruct
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 26.1 GBHeadroom: 9.1 GB
  • • Partial CPU offload: ~39% of layers run on CPU
ollama run qwen2.5:32b
10
tok/s
Estimated
Weights
19.32 GB
KV cache
4.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
Nemotron 3 Nano (30B-A3B)
30B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 24.6 GBHeadroom: 10.6 GB
  • • Partial CPU offload: ~35% of layers run on CPU
ollama run nemotron3:nano
10
tok/s
Estimated
Weights
18.11 GB
KV cache
3.75 GB
Activations
0.91 GB
Runtime
1.80 GB
Model details →

What if you upgraded?

Hypothetical scenarios. We re-ran the compatibility engine for each.

+32 GB system RAM

~$80–150

Doubles your CPU-offload working set. Helps when models don't quite fit in VRAM.

Unlocks: 98 new comfortable, 89 new tradeoff

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
Shop this upgrade↗

Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)

~$350

22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~288 GB/s to ~616 GB/s.

Unlocks: 126 new comfortable

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 4060 Ti 16GB

~$449

Tensor parallelism splits the model across both cards, effectively doubling VRAM. Bandwidth doesn't double — runs ~1.5× the single-card speed in practice.

Unlocks: 160 new comfortable

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
Shop this upgrade↗

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

DeepSeek V4 Pro (1.6T MoE)
1600B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
Qwen 3.5 235B-A17B (MoE)
397B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
Qwen 3 235B-A22B
235B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
DeepSeek R1 (671B reasoning)
671B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
DeepSeek V4 Flash (284B MoE)
284B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—

How to read these numbers

Measured here
Measured here - RunLocalAI ran this exact combo on owner hardware with public evidence.

Source-backed
Source-backed / community - a reproduced public source supports the speed, but it is not labeled as owner-measured.

Extrapolated
Extrapolated - predicted from a measured benchmark on similar-bandwidth hardware.

Estimated
Estimated - formula based on VRAM bandwidth and model architecture; not a benchmark row.

RunLocalAI Will-It-Run Framework →

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