RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
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SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
Will it run? / NVIDIA GeForce RTX 3090 Ti / chat

What can NVIDIA GeForce RTX 3090 Ti run for chat?

Build: NVIDIA GeForce RTX 3090 Ti + — + 32 GB RAM (windows)

Memory: 24 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
83 models

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

#1Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.8 GBHeadroom: 9.2 GB
ollama run llama3.2:3b
194
tok/s
E
Weights
3.19 GB
KV cache
1.50 GB
Activations
8.35 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#2Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 18.5 GBHeadroom: 5.5 GB
ollama run mistral:7b
128
tok/s
E
Weights
4.81 GB
KV cache
3.50 GB
Activations
8.43 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#3Llama 3.3 8B Instruct
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 19.1 GBHeadroom: 4.9 GB
128
tok/s
E
Weights
4.83 GB
KV cache
4.00 GB
Activations
8.43 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#4Falcon 3 7B Instruct
7B
falcon
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 6.1 GB
146
tok/s
E
Weights
4.23 GB
KV cache
3.50 GB
Activations
8.40 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#5InternLM 2.5 7B Chat
7B
internlm
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 6.1 GB
146
tok/s
E
Weights
4.23 GB
KV cache
3.50 GB
Activations
8.40 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#6Tulu 3 8B
8B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 19.1 GBHeadroom: 4.9 GB
128
tok/s
E
Weights
4.83 GB
KV cache
4.00 GB
Activations
8.43 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#7Granite 3.0 2B Instruct
2B
granite
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 7.7 GBHeadroom: 16.3 GB
511
tok/s
E
Weights
1.21 GB
KV cache
0.50 GB
Activations
4.16 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#8Granite 3.0 8B Instruct
8B
granite
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 13.0 GBHeadroom: 11.0 GB
128
tok/s
E
Weights
4.83 GB
KV cache
2.00 GB
Activations
4.34 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#9Qwen 3 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 6.1 GB
146
tok/s
E
Weights
4.23 GB
KV cache
3.50 GB
Activations
8.40 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#10Dolphin 3.0 Llama 3.2 3B
3B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 10.6 GB
341
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#11Hermes 3 Llama 3.2 3B
3B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 10.6 GB
341
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#12Ministral 8B Instruct
8B
mistral
Quant: Q4_K_MContext: 8,192VRAM: 19.1 GBHeadroom: 4.9 GB
128
tok/s
E
Weights
4.83 GB
KV cache
4.00 GB
Activations
8.43 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →

Runs with tradeoffs
49 models

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

Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 20.2 GBHeadroom: 3.8 GB
  • • Tight VRAM fit — only 3.8 GB headroom left for context growth
ollama run gemma2:9b
114
tok/s
E
Weights
5.43 GB
KV cache
4.50 GB
Activations
8.46 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Mistral Nemo 12B Instruct
12B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 23.6 GBHeadroom: 0.4 GB
  • • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run mistral-nemo:12b
85
tok/s
E
Weights
7.25 GB
KV cache
6.00 GB
Activations
8.55 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Gemma 3 12B
12B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 23.6 GBHeadroom: 0.4 GB
  • • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run gemma3:12b
85
tok/s
E
Weights
7.25 GB
KV cache
6.00 GB
Activations
8.55 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 22.9 GBHeadroom: 1.1 GB
  • • Tight VRAM fit — only 1.1 GB headroom left for context growth
ollama run qwen3:8b
73
tok/s
E
Weights
8.50 GB
KV cache
4.00 GB
Activations
8.62 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Mistral Small 3 24B
24B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 22.1 GBHeadroom: 1.9 GB
  • • Tight VRAM fit — only 1.9 GB headroom left for context growth
ollama run mistral-small:24b
43
tok/s
E
Weights
14.49 GB
KV cache
3.00 GB
Activations
2.77 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Mistral Medium 3 24B (dense)
24B
mistral
Quant: Q4_K_MContext: 2,048VRAM: 22.1 GBHeadroom: 1.9 GB
  • • Tight VRAM fit — only 1.9 GB headroom left for context growth
43
tok/s
E
Weights
14.49 GB
KV cache
3.00 GB
Activations
2.77 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Mistral Small 3.2 24B
24B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 22.1 GBHeadroom: 1.9 GB
  • • Tight VRAM fit — only 1.9 GB headroom left for context growth
43
tok/s
E
Weights
14.49 GB
KV cache
3.00 GB
Activations
2.77 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Gemma 4 26B MoE
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 23.6 GBHeadroom: 0.4 GB
  • • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run gemma4:26b-moe
39
tok/s
E
Weights
15.70 GB
KV cache
3.25 GB
Activations
2.83 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →

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: 1 new comfortable, 61 new tradeoff

  • • SmolLM 2 360M Instruct
  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • Llama 3.3 70B Instruct
Shop this upgrade↗

Upgrade to NVIDIA GeForce RTX 5090

~$2499

32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~? GB/s to ~1792 GB/s.

Unlocks: 29 new comfortable

  • • DeepSeek R1 Distill Qwen 7B
  • • Qwen 3 8B
  • • Hermes 3 Llama 3.1 8B
  • • Gemma 2 9B Instruct
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 3090 Ti

~$1199

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: 41 new comfortable

  • • DeepSeek R1 Distill Qwen 7B
  • • Qwen 3 8B
  • • Hermes 3 Llama 3.1 8B
  • • Gemma 2 9B Instruct
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 (24 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 (24 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 (24 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 (24 GB) + 60% of system RAM (19 GB) combined.

—
Llama 4 Scout
109B
llama
Commercial OK

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.

—

How to read these numbers

M
Measured — we ran this exact combo on owner hardware.

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

E
Estimated — pure formula based on VRAM bandwidth and model architecture.

Full methodology →

Want a specific benchmark we don't have? Email support@runlocalai.co and we'll prioritize it.