RUNLOCALAIv38
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RUNLOCALAI · v38
Will it run? / NVIDIA GeForce RTX 5070 Ti / reasoning

What can NVIDIA GeForce RTX 5070 Ti run for reasoning?

Build: NVIDIA GeForce RTX 5070 Ti + — + 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 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: 5.1 GB
121
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: 5.1 GB
ollama run RefinedNeuro/RN_TR_R1:latest
121
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: 5.1 GB
121
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#4EXAONE Deep 7.8B
7.8B
other
Quant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 5.3 GB
124
tok/s
Estimated
Weights
4.71 GB
KV cache
3.90 GB
Activations
0.24 GB
Runtime
1.80 GB
Model details →
#5RefinedNeuro RN TR R2
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.9 GBHeadroom: 5.1 GB
ollama run RefinedNeuro/RN_TR_R2:latest
121
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#6InternLM 2.5 7B Chat
7B
internlm
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB
138
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#7Qwen 2.5 Math 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 8.0 GBHeadroom: 8.0 GB
138
tok/s
Estimated
Weights
4.23 GB
KV cache
1.75 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#8Qwen 3 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB
138
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#9Trendyol LLM 7B Chat v0.1
7B
llama
Quant: Q4_K_MContext: 4,096VRAM: 8.0 GBHeadroom: 8.0 GB
138
tok/s
Estimated
Weights
4.23 GB
KV cache
1.75 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#10E5 Mistral 7B Instruct
7.11B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 6.1 GB
136
tok/s
Estimated
Weights
4.29 GB
KV cache
3.56 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#11Mistral 7B Instruct v0.2
7B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB
138
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#12Salamandra 7B Instruct
7B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB
138
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
79 models

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

NVIDIA Nemotron Nano 9B v2 Japanese
9B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.0 GBHeadroom: 4.0 GB
  • • Tight VRAM fit — only 4.0 GB headroom left for context growth
107
tok/s
Estimated
Weights
5.43 GB
KV cache
4.50 GB
Activations
0.28 GB
Runtime
1.80 GB
Model details →
DeepSeek R1 Distill Qwen 7B
7B
deepseek
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 13.1 GBHeadroom: 2.9 GB
  • • Tight VRAM fit — only 2.9 GB headroom left for context growth
ollama run deepseek-r1:7b
78
tok/s
Estimated
Weights
7.44 GB
KV cache
3.50 GB
Activations
0.38 GB
Runtime
1.80 GB
Model details →
Phi-4 Reasoning 14B
14B
phi
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 phi4-reasoning:14b
69
tok/s
Estimated
Weights
8.45 GB
KV cache
1.75 GB
Activations
0.42 GB
Runtime
1.80 GB
Model details →
DeepSeek R1 Distill Qwen 14B
14B
deepseek
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 deepseek-r1:14b
69
tok/s
Estimated
Weights
8.45 GB
KV cache
1.75 GB
Activations
0.42 GB
Runtime
1.80 GB
Model details →
DeepSeek V3 Lite (16B MoE)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 13.9 GBHeadroom: 2.1 GB
  • • Tight VRAM fit — only 2.1 GB headroom left for context growth
402
tok/s
Estimated
Weights
9.66 GB
KV cache
2.00 GB
Activations
0.49 GB
Runtime
1.80 GB
Model details →
DeepSeek R1 Distill Mistral 24B
24B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 29.0 GBHeadroom: 6.2 GB
  • • Partial CPU offload: ~45% of layers run on CPU
40
tok/s
Estimated
Weights
14.49 GB
KV cache
12.00 GB
Activations
0.73 GB
Runtime
1.80 GB
Model details →
Omni 31B Turkish Reasoning
31B
other
Quant: Q4_K_MContext: 2,048VRAM: 25.3 GBHeadroom: 9.9 GB
  • • Partial CPU offload: ~37% of layers run on CPU
31
tok/s
Estimated
Weights
18.72 GB
KV cache
3.88 GB
Activations
0.94 GB
Runtime
1.80 GB
Model details →
DeepSeek R1 Distill Qwen 32B
32B
deepseek
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 deepseek-r1:32b
30
tok/s
Estimated
Weights
19.32 GB
KV cache
4.00 GB
Activations
0.97 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 ~896 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 5070 Ti

~$849

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.

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