RUNLOCALAIv38
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RUNLOCALAI · v38
Will it run? / NVIDIA GeForce RTX 3090 / reasoning

What can NVIDIA GeForce RTX 3090 run for reasoning?

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

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

Runs comfortably
110 models

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

#1Llama 3.1 Nemotron Nano 8B
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.9 GBHeadroom: 13.1 GB
126
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#2RefinedNeuro RN TR R1
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.9 GBHeadroom: 13.1 GB
ollama run RefinedNeuro/RN_TR_R1:latest
126
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#3DeepSeek R1 Distill Llama 8B
8B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.9 GBHeadroom: 13.1 GB
126
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#4NVIDIA Nemotron Nano 9B v2 Japanese
9B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.0 GBHeadroom: 12.0 GB
112
tok/s
Estimated
Weights
5.43 GB
KV cache
4.50 GB
Activations
0.28 GB
Runtime
1.80 GB
Model details →
#5DeepSeek R1 Distill Qwen 7B
7B
deepseek
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 13.1 GBHeadroom: 10.9 GB
ollama run deepseek-r1:7b
82
tok/s
Estimated
Weights
7.44 GB
KV cache
3.50 GB
Activations
0.38 GB
Runtime
1.80 GB
Model details →
#6Phi-4 Reasoning 14B
14B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.7 GBHeadroom: 6.3 GB
ollama run phi4-reasoning:14b
72
tok/s
Estimated
Weights
8.45 GB
KV cache
7.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
#7DeepSeek R1 Distill Qwen 14B
14B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.7 GBHeadroom: 6.3 GB
ollama run deepseek-r1:14b
72
tok/s
Estimated
Weights
8.45 GB
KV cache
7.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
#8DeepSeek V3 Lite (16B MoE)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 20.0 GBHeadroom: 4.0 GB
420
tok/s
Estimated
Weights
9.66 GB
KV cache
8.00 GB
Activations
0.49 GB
Runtime
1.80 GB
Model details →
#9Phi-4 14B
14B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.7 GBHeadroom: 6.3 GB
ollama run phi4:14b
72
tok/s
Estimated
Weights
8.45 GB
KV cache
7.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
#10EXAONE Deep 7.8B
7.8B
other
Quant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 13.3 GB
129
tok/s
Estimated
Weights
4.71 GB
KV cache
3.90 GB
Activations
0.24 GB
Runtime
1.80 GB
Model details →
#11RefinedNeuro RN TR R2
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.9 GBHeadroom: 13.1 GB
ollama run RefinedNeuro/RN_TR_R2:latest
126
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#12InternLM 2.5 7B Chat
7B
internlm
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB
144
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →

Runs with tradeoffs
52 models

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

DeepSeek R1 Distill Mistral 24B
24B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 20.0 GBHeadroom: 4.0 GB
  • • Tight VRAM fit — only 4.0 GB headroom left for context growth
42
tok/s
Estimated
Weights
14.49 GB
KV cache
3.00 GB
Activations
0.73 GB
Runtime
1.80 GB
Model details →
Omni 31B Turkish Reasoning
31B
other
Quant: Q4_K_MContext: 8,192VRAM: 37.0 GBHeadroom: 6.2 GB
  • • Partial CPU offload: ~35% of layers run on CPU
33
tok/s
Estimated
Weights
18.72 GB
KV cache
15.50 GB
Activations
0.94 GB
Runtime
1.80 GB
Model details →
DeepSeek R1 Distill Qwen 32B
32B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 38.1 GBHeadroom: 5.1 GB
  • • Partial CPU offload: ~37% of layers run on CPU
ollama run deepseek-r1:32b
31
tok/s
Estimated
Weights
19.32 GB
KV cache
16.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
QwQ 32B Preview
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 38.1 GBHeadroom: 5.1 GB
  • • Partial CPU offload: ~37% of layers run on CPU
ollama run qwq:32b
31
tok/s
Estimated
Weights
19.32 GB
KV cache
16.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
EXAONE 4.0.1 32B
32B
exaone
Quant: Q4_K_MContext: 8,192VRAM: 38.1 GBHeadroom: 5.1 GB
  • • Partial CPU offload: ~37% of layers run on CPU
31
tok/s
Estimated
Weights
19.32 GB
KV cache
16.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
Qwen3 Swallow 32B RL v0.2
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 38.1 GBHeadroom: 5.1 GB
  • • Partial CPU offload: ~37% of layers run on CPU
31
tok/s
Estimated
Weights
19.32 GB
KV cache
16.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
DeepSeek R1 Distill Qwen 3 32B
32B
deepseek
Commercial OK
Quant: AWQ-INT4Context: 2,048VRAM: 39.4 GBHeadroom: 3.8 GB
  • • Partial CPU offload: ~39% of layers run on CPU
19
tok/s
Estimated
Weights
32.00 GB
KV cache
4.00 GB
Activations
1.60 GB
Runtime
1.80 GB
Model details →
Sarvam M
24B
mistral
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 23.0 GBHeadroom: 1.0 GB
  • • Tight VRAM fit — only 1.0 GB headroom left for context growth
42
tok/s
Estimated
Weights
14.49 GB
KV cache
6.00 GB
Activations
0.73 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, 65 new tradeoff

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

Upgrade to NVIDIA RTX PRO 4500 Blackwell

see current pricing

32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~936 GB/s to ~896 GB/s.

Unlocks: 124 new comfortable

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

Add a second NVIDIA GeForce RTX 3090

~$899

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: 148 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 (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 R1 (671B reasoning)
671B
deepseek
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.

—

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.

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